0

993

BIPHASIC DROPLET MICROFLUIDICS IN RELATION TO

PHARMACEUTICAL INDUSTRIAL BIOCHEMICAL

SCREENING

A Thesis submitted to The University of Manchester for the degree of

Doctor of Philosophy

Faculty of Engineering and Physical Sciences

2016

Brett Andrew Litten BSc. (Hons.)

~

1

Table of Contents

ABSTRACT 7

ABBREVIATIONS 10

DECLARATION AND COPYRIGHT STATEMENT 13

ACKNOWLEDGEMENTS 14

1 INTRODUCTION

1.1 Overview 15

1.2 Drug Discovery and Screening 15

1.2.1 Early Phase Screening 15

1.2.2 Drug Metabolism and Pharmacokinetics 18

1.2.3 Phase I/II Metabolism 19

1.2.4 Recombinant Cytochrome P450 Enzymes 20

1.2.5 Lipinski's 'Rule of Five' 23

1.2.6 Microtitre Plate Enzyme Inhibition Assays 23

1.2.7 Droplet Microfluidic Chip Assays 27

1.2.8 Microfluidics, Industrial Screening and the ‘Killer App’ 29

1.2.9 Challenges for Microfluidics in Industry 30

1.2.10 The Cost of Screening 33

1.2.11 Integration with Existing Technology 35

1.2.12 Droplet Assays 35

1.2.13 Facilitation of Biochemical Screening in Microfluidics 37

1.2.14 Cell Screening – Encapsulation 37

1.2.15 '3D' Cell Assay & Hydrogels 38

1.3 Microfluidic Devices 40

1.3.1 Fabrication 40

1.3.2 Sealing 42

1.3.3 Device Surface Functionalisation `Hydrophobicity' 44

1.3.4 Functionalisation of Polymer Chips 45

2

1.3.5 Fluid Connection 45

1.3.6 Fluid Propulsion - Hydraulic Pressure 46

1.3.7 Fluid Propulsion - Electro-osmotic flow (EOF) 48

1.3.8 Fluid Propulsion - Centrifugal Force 50

1.4 Detection and Analytical Techniques 50

1.4.1 Jablonski Diagrams 51

1.4.2 Absorbance 52

1.4.3 Vibrational Relaxation and Internal Conversion 53

1.4.4 Fluorescence 54

1.4.5 Intersystem Crossing (IS) 55

1.4.6 Absorbance Detection and Microfluidics 56

1.4.7 Fluorescence Detection and Microfluidics 57

1.4.8 Luminescence Detection and Microfluidics 57

1.4.9 Mass Spectrometry and Microfluidics 59

1.4.10 Other Detection Methods and Microfluidics 59

1.4.11 Advantages and Disadvantages of Analytical Techniques 60

1.5 Droplet Production & Manipulation 61

1.5.1 Surface Tension 61

1.5.2 Surfactants in Droplet Microfluidic Systems 64

1.5.3 Reynolds and Capillary Numbers 66

1.5.4 Droplet Detachment: Squeezing, dripping and jetting 66

1.5.5 T-junction and Flow Focussing Droplet Generation 67

1.5.6 Oils for carrier phase 68

1.5.7 Droplet sorting - Hydrodynamic Sorting 70

1.5.8 Droplet sorting - Dielectrophoretic Sorting 70

1.5.9 Droplet sorting - Magnetic Sorting 71

1.5.10 Droplet sorting - Optical Sorting (laser tweezers) 71

1.5.11 Droplet sorting - Electrowetting-On-Dielectric 72

3

1.5.12 Geometry-Mediated Passive Droplet Fusion 73

1.5.13 Electrofusion 73

1.5.14 Droplet Fission 74

1.6 Partitioning and Droplet Surface Interactions 75

1.6.1 Partitioning in Drug Discovery 75

1.6.2 Partitioning in Microfluidics 76

1.6.3 Proteins at Liquid Interfaces 77

2 PROJECT AIMS

2.1 Objectives 78

3 EXPERIMENTAL

3.1 Chemical and Reagents 80

3.1.1 Preparation of Phosphate Buffer Solution 80

3.1.2 Preparation of NADPH Solution 83

3.1.3 Preparation of Cytochrome P450 Enzyme Solution 83

3.1.4 Preparation of CEC Substrate Solution 83

3.2 Apparatus 84

3.2.1 Chip Designs 84

3.2.2 Chip Fabrication 84

3.2.3 Chip Sealing 87

3.2.4 Fluid Delivery 89

3.2.5 Analytic Apparatus for Fluorescent Detection 90

3.2.6 Fluorescence Intensity Measurements 92

3.2.7 Direct Observation 92

3.2.8 High Voltage Power Supply 92

3.3 Methods 93

3.3.1 Procedure for Oil Testing, Droplet Formation and Linearity 93

3.3.2 Synthesis and Characterisation of Fluorosurfactant 95

4

3.3.3 AZF Dissolution 100

3.3.4 AZF Critical Micelle Concentration (CMC) Determination

by Dynamic Light Scattering (DLS) 101

3.3.5 Procedure for Microtitre plate Cytochrome P450 Inhibition Assay and

Dependence on Reaction Temperature 101

3.3.6 Design of Electrical Heater for Incubation of Droplet Chips 103

3.3.7 Procedure for Chip-Based 1A2-CEC Inhibition Experiments 105

3.3.8 Procedure for Partitioning Experiments in Glass Vial 110

3.3.9 Investigating the Impact of Surfactant on Partitioning 112

3.3.10 Procedure for Partitioning Experiments Using AstraZeneca Collection

Library Compounds 113

3.3.11 Testing AZ Compound Solubility 115

3.3.12 Partitioning Test - Round 1 115

3.3.13 Partitioning Test - Round 2 116

3.3.14 Procedure for Predictive Partitioning Modelling 117

3.3.15 Partitioning Test - Round 3 118

3.3.16 Procedure for in situ Chip Partitioning Experiments 118

3.3.17 Procedure for Shake-Flask Experiment 120

3.3.18 Procedure for Labelling Proteins/Enzymes 121

3.3.19 Procedure for Determination of Droplet Labelling Ratio 122

3.3.20 Procedure for Blocking Enzyme Adsorption at the Interface 123

4 RESULTS & DISCUSSION: DROPLET FORMATION

4.0.1 Droplet Formation and Linearity 126

4.0.2 Dual Aqueous Input Electro-Fusion T-Junction Chip 135

4.0.3 Surfactant CMC Determination by Dynamic Light Scattering 137

4.1 Summary & Conclusions 138

4.1.1 Droplet Formation and Linearity 138

4.1.2 Dual Aqueous Input Electro-Fusion T-Junction Chip 139

4.1.3 Fluorosurfactant CMC Determination 139

5

5. RESULTS & DISCUSSION: COMPOUND PARTITIONING

5.0.1 Partitioning from Aqueous to Oil Phase (Glass Vial tests) 141

5.0.2 AstraZeneca (AZ) Library Compounds: Aqueous Solubility 146

5.0.3 AstraZeneca Library Compounds: Partitioning - Round 1 & 2 146

5.0.4 Predictive Modelling of Partitioning 156

5.0.5 AstraZeneca Library Compounds: Partitioning - Round 3 156

5.0.6 Partitioning from Droplets to Carrier Oil in Droplet Chip 159

5.1 Summary & Conclusions 160

5.1.1 Partitioning – Glass Vial Tests 160

5.1.2 Partitioning – Droplet Chip Tests 161

5.1.3 Partitioning – AZ Library Compounds & Predictive Modelling 161

6. RESULTS & DISCUSSION: CYTOCHROME P450 REACTION

6.0.1 Microtitre Plate: Reaction Dependence on Temperature 163

6.0.2 Microtitre Plate: Linearity at 34 °C and Standard CHC Curve 166

6.1 Summary & Conclusions 169

6.1.1 Reaction Dependence on Temperature & Linearity of Reaction 169

6.1.2 CHC Standard Curve 169

7. RESULTS & DISCUSSION: SPIRAL INCUBATION CHIP

7.0.1 Droplet Chip: CHC Standard Curve 171

7.0.2 Droplet Chip: Cytochrome P450 Enzyme Inhibition 175

7.1 Summary & Conclusions 181

7.1.1 P450 Enzyme Reaction in Spiral Incubation Chip 181

8. RESULTS & DISCUSSION: PARTITIONING FOLLOW-UP

8.0.1 Shake Flask 182

8.1 Summary & Conclusions 185

8.1.1 Shake Flask 185

6

9. RESULTS & DISCUSSION: SERPENTINE INCUBATION CHIP

9.0.1 Cytochrome P450 Control Activity (no inhibitor) 186

9.1 Summary & Conclusions 188

9.1.1 Serpentine Incubation Chip 188

10. RESULTS & DISCUSSION: BLOCKING PROTEINS

10.0.1 Determination of Fluorescent Tag 189

10.0.2 Labelled Proteins at the Droplet-Oil Interface 190

10.0.3 Droplet Formation in the Presence of Blocking Proteins 193

10.0.4 Impact of Blocking Proteins on Cytochrome Enzyme Reaction 194

10.0.5 Impact of Blocking Proteins on Cytochrome 1A2-CEC pIC50 197

10.1 Summary & Conclusions 200

10.1.1 Fluorescent Tagging & Labelled Proteins at the Droplet Interface 200

10.1.2 Droplet Formation in the Presence of Blocking Proteins 200

10.1.3 Impact of Blocking Proteins on the Cytochrome P450 Reaction 201

11. SUMMARY, CONCLUSIONS AND FURTHER WORK

11.1 Summary 202

11.2 Conclusions 205

11.2.1 Partitioning of Compound from the Droplet 205

11.2.2 Enzyme Inhibition & Proteins at the Droplet Interface 207

11.2.3 Droplet Technology as an Industrial Screening Tool 207

11.3 Further Work 208

11.3.1 Predictive Model Development 208

11.3.2 Reagents into Microfluidic Devices 209

11.3.3 Analytical Detection Methods 213

11.3.4 Cell-based Droplet Microfluidics 214

12. REFERENCES 216

7

Abstract

Many droplet microfluidic assays have been described in the literature over the last decade of

research, however, there has been little reported industrial use of droplet microfluidics in

drug discovery compound screening, and in particular that of P450 enzyme inhibition assays

for profiling drug-drug interactions. This is partly for Intellectual Property reasons, since

Pharmaceutical companies do not wish to give away trade secrets in a competitive market,

but also because the technology is not yet 'proven' and remains in the proof-of-concept stage.

In droplet microfluidics, where at least two liquid phases are encountered, it is important that

leakage of material between phases is addressed. This effect has been extensively reported in

the literature using fluorescent dyes, however there is very little evidence of research using

large compound sets of diverse chemistry. This is probably because few researchers have

access to the large pharmaceutical libraries necessary for this work.

This project assessed the feasibility of translating a widely used microtitre plate-based P450

enzyme inhibition assay to droplet format; determined the extent of partitioning from droplets

using a large pharmaceutical library set and attempted to model this behaviour, and thirdly,

considered the pharmacological impact the droplet format may have on the assay.

The P450 cytochrome 1A2 enzyme type (isoform) was chosen for translation to the micro-

droplet format. Assays of this type are often conducted using fluorogenic substrates, making

them favourable for relatively easy fluorescent detection in droplet format using simple

optical detection assemblies.

Oil selection was investigated to determine which oil systems would be better suited in

respect of droplet formation. The use of surfactants in the oil phase and its impact on droplet

formation was studied and the synthesis, preparation and characterisation of a custom

perfluoropolyether (PFPE) surfactant (‘AZF’) conducted.

8

Droplet chips were designed and fabricated to produce droplets of 200–300 µm diameter

using novel channel designs and sealing techniques. The droplets were analysed by

fluorescence spectroscopy using bespoke detector apparatus. Partitioning from aqueous to oil

phase was studied for a small range of compounds and oils (with and without surfactant for

fluorous oils). Partitioning was lowest using fluorous oils alone, and increased substantially

when surfactant was included. Results from the large pharmaceutical test set suggested the

percentage of compounds that may partition readily to the oil phase is low even when using

surfactant. However, attempts to correlate this to known physicochemical properties and to

develop a predictive model for fluorous solubility proved largely unsuccessful. Partitioning in

the droplet chip using a droplet collection pooling method was difficult to quantify as a

consequence of the profound impact turbulence had on partitioning.

Miniaturisation of the P450 cytochrome inhibition assay to the droplet format initially gave

poorly reproducible low signals. Possible causes included detector insensitivity, partitioning

of reagent and/or fluorescent metabolite over longer incubation times, and binding of the 1A2

P450 cytochrome enzyme-protein at the droplet interface.

Protein interaction at the droplet-oil boundary was studied by fluorescence labelling a protein

contained in 200µm droplets and observing the extent of fluorescence localisation at the

interface by epifluorescent and confocal fluorescence microscopy. The data from this work

indicates a pronounced localisation of protein at the droplet interface, possibly leading to

enzyme deactivation and the loss of signal seen for the assay in the droplet chip.

A number of protein titrations were co-added to the droplets as 'blocking proteins' which

were found to improve the reaction output, however were also noted to affect the

pharmacology of the assay, noted by an order of magnitude shift in the reported IC50 for the

test inhibitor used (fluvoxamine).

9

The effects of compound leakage from droplets, and the possible detrimental impact on

biological reagents by interaction at the droplet-oil interface, is a challenge that may limit

widespread adoption of droplet MF systems in drug screening operations. Appropriate

control measures and/or a means to reduce these effects are essential to enable accurate

quantification with industrial drug discovery environments.

The findings in this work highlight the challenges that have to be addressed for droplet

microfluidic technology to be successfully incorporated into key areas of assay screening

within drug discovery. In terms of further research, there is a significant requirement for the

research community to delve further into these challenges and work closely with the industry

sector to understand the beneficial role microfluidics can have and how to develop effective

robust strategies the industry can easily adopt to progress this area of science.

10

Abbreviations

AZ AstraZeneca

AZF AstraZeneca custom-made fluorosurfactant

ADC Analogue-to-Digital Convertor

ADME Adsorption, Distribution, Metabolism & Excretion

BSA Bovine Serum Albumin

°C Degrees Celsius

Ca Capillary number

CAD Computer Assisted Design

CCD Charged-Coupled Device

CEC 3-cyano-7-ethoxycoumarin

CHC 3-cyano-7-hydroxycoumarin

CNC Computer Numerical Control

Cpd(s) Compound(s)

CV Co-efficient of Variation

d or φ Diameter

DC Direct Current

DCM Dichloromethane

DDA Dodecylamine

DLS Dynamic Light Scattering

DMPK Drug Metabolism and Pharmacokinetics

DMSO Dimethylsulfoxide

cDNA complimentary Deoxyribonucleic Acid

EOF Electro-Osmotic Flow

EWOD Electro-Wetting On Dielectric

et al. abbreviation: et alia (Latin: ‘and others’)

FEP Fluoroethylene Propylene

FI Fluorescent Intensity

11

FITC Fluorescein Isothiocyanate

f.p.s Frames Per Second

FP Fluorescent Polarisation

FRET Fluorescent Resonance Energy Transfer

FSD Full Scale Deflection

h Height or depth

HCB High Content Biology

HF/HNO3 Hydrogen Fluoride / Nitric Acid

HF/NH4F Hydrogen Fluoride / Ammonium Fluoride

HPLC High Performance Liquid Chromatography

HTS High Throughput Screening

HV High Voltage

HVPS High Voltage Power Supply

IC50 Concentration to yield 50% inhibition of effect

i.d. Internal Diameter

in vitro latin: ‘In Glass’

in vivo latin: ‘Within the living’

in silico analogy: ‘By Computer’

IR Infrared

l Length

logD Logarithm of the Distribution Co-efficient (partitioning)

logP Logarithm of the Partition Co-efficient

MF Micro Fluidic Device(s)

MS Mass Spectrometry

NADPH Nicotinamide adenine dinucleotide phosphate

P450(s) Referring to ‘Cytochrome P450 enzyme(s)’

P Fluidic pressure

PAPS 3’-phosphoadenosine-5’-phosphosulfate

PC Polycarbonate

12

(q)PCR (quantitative) Polymerase Chain Reaction

PDMS Polydimethylsiloxane

PEG Poly ethylene glycol

PID Proportional Integral Derivative

PFPE Perfluoropolyether

PFO Perfluorooctanol

PMMA Polymethylmethacrylate

PMT Photo-Multiplier Tube

PoC Proof of Concept

PTFE Polytetrafluoroethylene

Proliferation (cell) Cell growth via cell division.

Q Volumetric or total flow rate

QT interval desc.: electrical depolarization and repolarisation of the

heart ventricles

Re Reynolds number

ROC Receiver Operating Characteristic

RSD Relative Standard Deviation

s Second (time)

SAR Structure-Activity Relationship

SBS Society for Biomolecular Sciences

THF Tetrahydrofuran

TRF Time Resolved Fluorescence

(µ)TAS (micro) Total Chemical Analysis System

USB Universal Serial Bus

UV Ultraviolet

VIS Visible

w Width

We Weber number

13

Declaration and Copyright Statement

No part of the work referred to in this doctoral thesis has been submitted (either previously,

or concurrently), in support of any application for other degree or qualification of this or any

other university, institute or centre of learning.

The ownership of any intellectual property rights described in this thesis is vested in the

University of Manchester (herein referred as ‘the University’) and AstraZeneca

Pharmaceuticals plc. (herein referred as ‘the Company’), subject to any prior agreement to

the contrary, and may not be made available for use by third parties without the written

permission of the University and the Company as appropriate, which will prescribe the terms

and conditions of any such agreement.

Further information on the conditions under which disclosures and exploitation may take

place is available from the University via the head of the School of Chemical Engineering

and Analytical Science, and from the Company via the Director of High Throughput

Screening, Discovery Sciences.

Copyright in text of this thesis rests with the Author. Copies (by any process) either in full,

or in extracts, may be made only in accordance with instructions given by the Author and

lodged in the John Ryland’s University Library of Manchester. Details may be obtained from

the Librarian. This page must form part of any such copies made.

Further copies (by any process) made in accordance with such instructions may not be made

without the written permission of the Author.

14

Acknowledgements

AstraZeneca

I would like to thank the AstraZeneca (AZ) colleagues who over the last several years have

helped enable the opportunity to embark upon this research and have provided the support

and sponsorship for my research endeavours:

Ian Wilson, Kin Tam, David Robinson, Christine Rigby, Brian Law, Madeleine Brady,

Carolyn Blackett, Derek Barratt, Mark Wigglesworth and Steve Rees.

In addition, I would like to extend thanks to Trevor Johnson, Ian Sinclair and Thierry Kogej

for their assistance with fluorosurfactant synthesis, HPLC-MS analysis and Bayesian model

construction, respectively.

University of Manchester

I would also like to thank my academic supervisors, Prof. Peter Fielden (PRF) and Prof.

Nicholas Goddard (NJG) for their supervision and encouragement – it has been a pleasure to

be part of their research group. I would particularly like to thank Dr. Stephan Mohr for his

invaluable help and tuition in micro-fabrication and for sharing his technical knowledge on

CAD design and CNC machining.

I would also like to thank the rest of the PRF/NJG group. Special thanks to Dr. Bernard

Treves Brown for helping me with all things relating to computers and to Dr. Amelia Markey

and Dr. Craig Alexander for their help and guidance during my time at the University of

Manchester.

Finally, I thank my wife and family for putting up with the pain of my research, constant

ramblings about microfluidics and compound screening.

Brett Litten, July 2016.

15

1 INTRODUCTION

1.1 Overview

This project concerned the application of droplet microfluidic technology as a proposed

viable option for the miniaturisation of screening activities in industrial drug discovery. The

body of this work attempted to address challenges requiring consideration to help enable

microfluidics to evolve from proof-of-concept processes (PoC) to platforms that are able to

deliver reproducible and accurate data in the discovery and development of new drugs.

Microfluidics as a screening technology is highly desirable in that it can achieve significant

reagent and cost reductions for an industry whose expenses constantly increase. This

introduction considers the state of the art of microfluidics and the present technology and

assay formats currently used in routine industrial screening.

The project specifically considered the biphasic environment of droplet microfluidics and the

impact this has for screening from several aspects; i) partitioning of material between liquid

phases; ii) droplet miniaturisation of an enzyme assay used regularly in drug screening; iii)

the impact of protein binding at the liquid–liquid droplet interface, and iv) how to mitigate

adverse interaction artefacts at the liquid-liquid droplet interface.

1.2 Drug Discovery and Screening

1.2.1 Early Phase Screening

Advances in chemical engineering and combinatorial chemistry have provided a means for

many novel compounds to be made with comparative ease on a short time scale. The vast

diversity of these compounds has resulted in a significant increase in the use of high-

16

throughput screening techniques within the pharmaceutical industry to characterise these

compounds1.

Miniaturisation of in vitro assays used within the pharmaceutical industry can lead to a

significant efficiency improvement, reduction of operational costs and a decrease in

consumption of reagent and/or test compound2. A reduction in the personnel resource

required to generate a given assay throughput is similarly an attractive feature of streamlined

assays and provides opportunity for employee skills to be used in the most effective and

efficient manner. Drug hunting screening activities in the pharmaceutical industry exploit

increasingly smaller assay footprints, with many biochemical and cell early-screening

processes using 384- and 1536- well microtitre plate formats1. In early phase drug discovery,

one strategy to find novel leads is through compound collection library screening. High

Throughput Screening (HTS) campaigns are executed whereby large numbers (typically

50000 to 2-3 million) of chemically diverse materials are screened against the validated target

of interest in an effort to find ‘active hits’ that may have the appropriate pharmacology for the

target in question3.

Despite significant gains, such as reduced reagent consumption and elevated throughput by

using 1536-well plate formats, HTS (Figure 1) remains a time-consuming and relatively

cumbersome process. However, given the expanse of chemical diversity, it remains one of the

main approaches to screen tens of thousands to millions of compounds against different

biological targets4-8. Despite the operational benefits conventional microtitre plate-based

miniaturisation may offer, reductions in well volume can give rise to other problems such as

evaporation leading to plate artefacts and an increase in non-specific binding due to larger

surface area-to-volume ratios9,10. Furthermore, high density microtitre plate assays often rely

upon relatively bulky and expensive equipment for high capacity automated screening11,12.

The desire to apply technologies that may enable new science and reduce the cost of finding

17

novel drug compounds is highly desirable. In this respect, microfluidic technology to aid

screening is an attractive option to scale down assays, reduce the consumption of expensive

reagents and thus help to reduce the extremely high cost of getting a drug to market13. In

time, microfluidics might also become a turn-key solution to enable discovery of new

approaches not yet considered possible.

Figure 1. Example HTS process map. Microtitre plate assays at HTS scale often run into costs >£10k. A

significant proportion of this cost results from the large number of plates and reagents involved. Initial

‘primary’ screening is conducted at a single concentration with additional confirmation and artefact screening

to reduce the false positive hit rate. Only confirmed active hits make it through to concentration response

assay. The whole process can take up to 6 months.

The recent re-focus on the value of phenotypic screening14, where the target may be unknown

or of a multiple nature, has led to a steady rise in the demand for early phase screening in cell

Assay Optimisation & Validation

Primary screen (100k + compounds)

Confirmation of Positive Hits

Orthogonal Assay

Concentration Response

(confirmed hits)

Data Reporting and Chemistry Follow-up

Near neighbour determination

Up to 6 months

18

lines or whole organism entities such as c. elegans15, which may present additional

challenges to performing quantifiable assays in miniaturised microtitre plates formats, thus

making microfluidic technology more attractive

1.2.2 Drug Metabolism and Pharmacokinetics

In pharmaceutical drug discovery, confirmed hits against the target biology will enter a

second phase of testing to further characterise the lead compounds (Figure 2)16,17. Typically,

assays in the early phase investigate the interaction of test compounds against the biology

target, whereas later stages will include panels of assays used to characterise physicochemical

and metabolic properties such as solubility, logD (distribution of compound by partitioning

between phases), blood plasma binding, drug transporters (intra- and inter- cellular),

metabolic profiling and investigation of in vivo phamacokinetics and pharmacodynamics18-22.

Figure 2. Some key stages of drug discovery & development screening activities for a drug project.

The cytochrome P450 inhibition assay is an important tool in early-phase screening for

identifying compounds that can cause undesirable drug-drug interactions in the clinic in

respect of drug metabolism by the liver. Historically, there is the example of co-

administration of the anti-inflammatory drug terfenidine, with the anti-fungal drug,

ketoconazole23. In this situation, ketoconazole was reported to have inhibited the action of

cytochrome 3A4 enzyme, switching off the metabolic pathway, and hence physiological

Project timeline

HTS & SAR

DMPK

Molecular Tox

19

clearance (elimination) of terfenidine. Over time, the concentration of terfenidine, which

affects the potassium ion channel responsible for correct heart regulation (QT interval),

increased to toxic levels, resulting in heart arrhythmia and sudden cardiac death23,24.

Cytochrome inhibition assays allow screening of test compounds often against the five major

human liver cytochrome P450 enzymes (1A2, 2C9, 2C19, 2D6, 3A4) responsible for a large

percentage of xenobiotic metabolism25-27. The assay enables scientists to calculate the

concentration of drug that will cause 50% inhibition (IC50) of a specific P450 enzyme-

substrate pair reaction by testing over a range of drug concentrations. The P450 enzyme-

specific substrates are metabolised to products that have been quantified by various analytical

techniques, such as fluorimetry26,28 or mass spectrometry29. Compounds found to be

affecting enzyme activity can thus be identified at an early stage and modified to remove or

reduce undesired interactions.

1.2.3 Phase I/II Metabolism

In the context of this project involving the miniaturisation of a P450 cytochrome assay, Phase

I metabolism is the most relevant. This type of metabolism occurs when compounds are first

passed through the liver to be processed for elimination30. Most often, this initial form of

metabolism involves making the material more water-soluble and hence more readily

excreted via the urine. There are a number of chemical reactions that can be accomplished to

achieve this aim. Table 1 summarises the most common routes for comparison.

20

Table 1. The main routes of Phase I and Phase II metabolism17.

Route Reaction Description

Phase I Oxidation At carbon, nitrogen or sulfur atoms. Removal of alkyl groups at oxygen atoms

to yield alcohols, or from nitrogen atoms to yield amines is also effective.

Phase I Hydrolysis Amides, peptides and esters can be cleaved by hydrolytic enzymes to form

alcohols and carboxylic acids.

Phase I Reduction Although not common in mammalian metabolism, nitro groups can be reduced

to amines. Ketones and aldehydes can be reduced to alcohols.

Phase II Glucoronisation Uridine diphosphoglucuronyl transferase catalyses the transformation of –OH

and –COOH groups to larger adducts increasing water solubility and markedly

decreased pharmacological activity. This process can also apply to NH2 in the

case of aromatic groups.

Phase II Sulfation In this process, mainly for low concentration phenols, the ‘active’ donor

required to catalyse the reaction is 3’-phosphoadenosine-5’-phosphosulfate

(PAPS).

Phase II Amino acid

conjugation

Many carboxylic acids will conjugate with amino acids such as glycine,

cysteine and taurine, increasing solubility for excretion. Animals excreting urea

(ureotelic) conjugate with glycine and those excreting uric acid (uricotelic) use

mainly ornithine.

Phase II Acetylation This reaction involves the addition of a CH3CO group to nitrogen atoms,

forming an amide. Acetyl-CoA catalyses this process. Although the polarity of

the molecule is largely unaffected, and hence solubility remaining similar, the

nitrogen atom is deactivated and thus the product has lower pharmacological

activity.

Phase II Mercapturic

acid conjugates

Reaction with the tripeptide, glutathione, followed by a multi-step conversion to

an N-acetyl cysteine (mercapturate), is a protective system in the body to

remove potentially harmful halogens from drugs.

1.2.4 Recombinant Cytochrome P450 Enzymes

To determine the potential for a candidate drug to inhibit a particular metabolic pathway,

screens are often designed to investigate a single target. For Cytochrome P450 inhibition,

recombinant systems are employed where the target enzyme is expressed in a protein

construct, often derived from an unrelated species, such as insect cells. This enables highly

specific studies to be performed to obtain IC50 data relating to a single enzyme.

Examples of such recombinant P450 enzymes are Baculosomes®, from Life Technologies

(microsomal sub-fraction of eukaryotic cell endoplastic reticulum, Figure 3). These are

prepared from insect cells infected with recombinant baculovirus containing a cDNA insert

for a specific human P450 enzyme. Other similar commercial preparations are available,

21

including Supersomes® from BD Gentest and Bactosomes® from Cypex. Assays

investigating enzyme inhibition can either use small 'drug-like' substrate probes25 or

fluorogenic substrate probes26,27.

Figure 3. Eukaryotic cell diagram, highlighting main components (slice). Figure courtesy of AstraZeneca. Cell

type is mammalian (not specified).

Assays using small 'drug-like' probes are generally regarded as being more pharmacologically

relevant as the products are also more ‘drug-like’ i.e. they more closely resemble the small-

molecule physicochemical properties of the candidate drugs being tested (see section on

Lipinski’s rules). Mass spectrometry can be used to detect and quantify the metabolic

products, however, for early screening processes where vast numbers of compounds are

tested against different P450 enzymes, it may be necessary to utilise spectroscopic techniques

to enable a higher throughput to meet business demand.

In one form of a typical fluorescence P450 inhibition experiment2,31, the enzyme is incubated

with an enzyme-specific coumarin substrate and test compound at known concentration. The

Nucleus

Mitochondria

Endoplasmic

reticulum

Cytosol

10 µm

22

reaction can be initiated by the addition of the reduced form of nicotinamide adenine

dinucleotide phosphate (NADPH) as depicted by the reaction schematic in Figure 4.

Figure 4. Reaction schematic for phase I metabolism of the fluorogenic substrate 3-cyano-7-ethoxycoumarin.

The reduced flavoprotein, NADPH, facilitates electron transfer in the cleavage of the ethoxy group producing

the fluorescent metabolite 3-cyano-7-hydroxycoumarin. Figure adapted from an image courtesy of the

AstraZeneca Structure & Biophysics group.

After the required incubation duration, reaction cessation may be achieved by the addition of

a suitable quenching reagent, such as chilled acetonitrile, which denatures the enzyme

protein, preventing further reaction17. The resultant fluorescent substrate metabolite is then

quantified by fluorescence spectroscopy and the metabolic inhibition calculated by

regression. The experiment is normally set up so there is an excess of enzyme, yielding

pseudo-first order kinetics (Figure 5).

Figure 5. Competitive enzyme reaction. Pseudo first-order kinetics where there is an excess of enzyme and

competition between the substrate and competitive inhibitor for binding to the enzyme binding pocket.

23

1.2.5 Lipinski's 'Rule of Five'

In 1997, Christopher Lipinski (at the time working with Pfizer) devised the mnemonic, ‘Rule

of Five’ relating physicochemical properties of a molecule to whether or not it may be

considered 'drug-like'32. In the context of pharmaceutical products, there are a number of

properties that may indicate being 'drug-like', including good solubility in biological tissues,

being able to pass into cells (such as the eukaryotic cell depicted in Figure 3) or being small

enough to interact with reactive pockets in proteins and enzymes33. Rather than being five

rules, there are in fact only four, but the parameters of each have cut-off values containing

five, as summarised in Table 2.

Table 2. Lipinski's 'Rule of Five' for drug-like molecules. The rule is perhaps useful as a starting point in drug

design, but is by no means perfect. There are many drugs marketed in recent years that do not follow the 'Rule

of Five' and would not have made it to market if the rules were followed explicitly.

Number of Hydrogen bond donors < 5

Molecular Weight < 500

Clog P < 5

Sum of nitrogen and oxygen atoms < 10

1.2.6 Microtitre Plate Enzyme Inhibition Assays

In industry, in vitro P450 inhibition assays were initially conducted in test tubes and more

recently in 96 well microtitre plates34, however, increasing demands upon the industry has

resulted in further miniaturisation to 384- and 1536-well formats (Figure 6) to increase

throughput35. In conjunction, laboratory automation for plate and liquid handling has become

commonplace to ensure good reproducibility, high levels of assay throughput and unattended

operation25,27,36-38. Assays conducted in 96-, 384- and 1536-well microtitre plates typically

feature incubate volumes of 200 µL, 30 µL and 5 µL, respectively17.

24

Figure 6. 96-, 384- and 1536-well microtitre plates used in drug discovery screening.

A microtitre plate-based assay may follow a procedure similar to that outlined in Figure 7 for

an optimised assay where the enzyme kinetic profile has been previously determined. A

protocol of this format lends itself to drug discovery screening and droplet microfluidics, as

many, if not all of the reagents, can be prepared in advance.

Figure 7. Typical protocol for cytochrome P450 inhibition assay.

As P450 cytochromes have a sensitivity to DMSO, which can significantly inhibit

activity39,40, routine assay procedures may often include DMSO controls which are used to

NAPDH solution is added to wells (concentration excess).

Dissolve substrate in suitable solvent at relatively high concentration.

Test compounds and controls are solubilised in DMSO.

Concentration response (CR) curve working solutions are prepared for all compounds.

A specific volume of each compound CR solution is dispensed into the assay plate.

P450 enzyme stock is thawed and diluted in pH7.4 phosphate buffer.

Fluorogenic substrate is spiked to the enzyme solution.

NADPH is solubilised to the required concentration in pH7.4 phosphate buffer.

Compounds dispensed to the enzyme solution, mixed, and pre-incubated for 5 minutes at 37 °C.

Incubate for required duration at 37 °C (incubation time depends on activity of enzymes)

Quench all wells with acetonitrile to stop reaction (or perform in situ real time analysis)

Read plates in fluorescent intensity mode at the emission wavelength for the substrate metabolite

and calculate IC50.

25

correct the maximum rate of the enzyme minus any inhibitory test compound. Equations 1 to

12 can be applied to calculate compound IC50 data assuming the same concentration of

DMSO is present in each test well (Equation (1)):

Mean Background = ∑ Ctrl(DMSO)n

1n

n (1)

Control Rate= Ctrl(DMSO)n- Mean Background

Incubation time (min) (2)

The assay signal-to-noise ratio is calculated using Equation 3.

Assay S:N=∑ [

Ctrl(low)nCtrl(DMSO)n

⁄ ]1n

n (3)

Where Ctrl(low) is the lowest concentration point of control drug

Compound S:N= Cpd(low)

Cpd(0) (4)

Where; Cpd(low) is the signal obtained at the lowest test compound concentration

and Cpd(0) is the signal obtained at the top compound concentration (added after

incubation).

Corrected fluorescence = Raw Fluorescence-Mean Background (5)

Individual Cpd Rate=Corrected C(n)c fluorescence

Assay incubation time (min) (6)

Compound signal-to-noise ratio is found by dividing the signal for the lowest test compound

concentration by the signal for the compound top concentration, where this top concentration

sample is added after incubation, as described by Equation (4). Differences between this S:N

and the Assay S:N (Equation (3)) are used as an indicator identifying the presence of

fluorescent quenching or enhancement, often due to compound native fluorescence at the

emission wavelength being monitored .

26

The maximum rate of the enzyme-substrate reaction is found by Equation (7):

Maximum rate (C(n)c=

∑ (Ctrl(i)corr

Ctrl(0)corr⁄ )n

i=1

n (7)

Individual compound rates are found using Equation (8):

Individual maximum rate (C(n)c)=

([Ctrl(n)1

Ctrl(0)1⁄ ]+[

Ctrl(n)2Ctrl(0)2

⁄ ])

2 (8)

Individual compound rates are converted to a percentage of the maximum corrected control

activity using Equation (9):

% Maximal Rate= Individual rate*100

Maximal rate (9)

To calculate IC50 values for each compound, the log of the % rate difference is plotted against

the log compound concentration and linear regression used to determine the gradient and

intercept. IC50 data can then be calculated using a pseudo-Hill analysis, plotting:

the dependent variable; Log (% maxrate

100-% max rate) vs. (10)

the dependent variable; Log (concentration) (11)

Linear regression analysis is then used to calculate the gradient and Y-intercept of the line

(Equation 12).

IC50= 10(-Intercept

Gradient⁄ ) (12)

27

1.2.7 Droplet Microfluidic Chip Assays

Despite the widespread use of P450 enzyme inhibition assays in drug discovery, few reports

directly consider the use of droplet microfluidic technology to aid this screening process and

even fewer consider the implications of leakage, or partitioning, from droplets can have in

drug discovery environments. There exists a comprehensive body of work describing various

enzymatic and cellular droplet-based reactions and consideration of other specific areas such

as Directed Evolution41,41,42,42 and droplet PCR43 where microfluidic technology can be

considered to have some impact. Gu et al. reported a droplet based system for enzyme

inhibition utilising electrochemical detection44. In this paper, Gu describes the potential for

surfactant to interfere with enzyme assays and reports that for the inhibition of hydrolytic

transformation of acetylcholinesterase to thiocholine, surfactants did not have a significant

effect on the assay. Recently, a compartmentalised absorbance end-point assay to determine

the Michaelis–Menten parameters of 4-nitrophenyl glucopyranoside hydrolysis by sweet

almond β-glucosidase was reported by Gielen et al.45. Gielen reports the use of 0.5%

perfluorooctanol in the fluorous oil phase necessary to aid droplet formation. Interestingly,

neither author comments on the potential for partitioning and its relevance to general drug

screening.

28

Table 3. Summary of example assays reported in droplet microfluidic formats.

Droplet-based Assay / System Description Reference Type

Cell proliferation and encapsulated multi-cellular organism toxicity 46 Cell

System for high throughput cytotoxocity screening of encapsulated single

mammalian cells 47 Cell

3D tumour simulation using breast tumour cells, drug loading and cell

viability testing 48 Cell

Sepsis diagnosis due to bacterial infection by measurement of minimal

inhibitory concentration of antibiotics 49 Bacteria

Bacterial population dynamics 50 Bacteria

Drug screening; droplet storage, mixing & detection – toxicity assays 51 Cell

Multidimensional concentrations of bacteria to evaluate toxic effects 52 Bacteria

High throughput screening of enzymes by retroviral display 53 Virus

Quantitative cell reporter gene assay 54 Cell

Directed evolution of biocatalysts for biofuel cell application 55 Biochemical

Dose-response assay of enzyme inhibitors using electrochemical detection 56 Biochemical

Discovery of promiscuous enzymes using functional metagenomics 57 Biochemical

Measuring enzyme kinetics 58 Biochemical

High throughput PCR 43 Biochemical

Recently, a number of articles have detailed the potential for compound leakage from and

between droplets and have attempted to characterise the mechanisms which govern such

transfers59,60. In addition, the impact of surfactant on droplet leakage has been studied61-63.

However, little work has been published linking such proof-of-concept studies, which often

use fluorescent dyes, to the case of drug screening where a much wider range of chemistry is

encountered.

29

Other work, such as that by Gupta64 and Pan65 consider approaches taken to counteract the

potential for droplet leakage through the use of nanoparticles to ‘block’ transport at the

interface.

1.2.8 Microfluidics, Industrial Screening and the ‘Killer App’

Over the last twenty years, biotechnology and academic institutions have contributed on a

global footprint to the discovery and development of a vast array of microfluidic solutions.

Figure 8 – Gartner’s Hype Cycle depicting the last 25 years of microfluidic expectation. Figure adapted from

published work by the Gartner Group64.

As Becker stated in his 2008 review on microfluidics66 and in his article from 2010 on

‘Collective Wisdom’67, we have seen a trend based on Gartner’s Hype Cycle68 that reflects a

fairly accurate picture of how microfluidics has been regarded over the last two decades

(Figure 8). Despite advances being made, with a great many functions having been

performed in a variety of microfluidic approaches58,69-72, it follows there may still remain a

number of significant challenges around seamless integration of microfluidics into a scientific

community that is very much microtitre plate-centric73-76. The fact that many drug hunting

Hype

1990 1995 2000 2005 2010 2015

Time

Inflated expectations

Technology trigger

Disillusionment

Slope of enlightenment

Plateau of productivity

30

organisations continue to rely almost exclusively upon the microtitre plate for compound

collection handling, suggests this is a key area for microfluidics development to address; that

is, devise ways to seamlessly integrate the existing technology with the new. There is a

perception that when a new technology arrives, it will replace the ones preceding it; there is

some truth to this idea. There are examples scattered across the history of science of

techniques being refined replaced with the ‘latest’ technology77-80. The consumer electronics

market is an obvious example where new developments are seized upon and displace the

previous generation of devices. However, the view that microfluidic technology is ‘the next

thing’ is perhaps misguided and the oft-mentioned microfluidic ‘killer app’81 that would

generate massive worldwide sales may not have materialised, evidenced perhaps from the

lack of sales to the pharma industry17. In the context of the pharmaceutical industry and drug

discovery, for microfluidics to be truly successful, not only must it provide advantages and be

robust, but it must also be implementable on a wide-scale, and have wide application appeal.

1.2.9 The Challenges for Microfluidics in Industry

As discussed before, there are financial benefits for the inclusion of microfluidics in drug

hunting organisations. Other reasons may include geographical footprint, where microfluidic

systems may require less laboratory space than the equivalent conventional microtitre plate-

based activity.

To achieve successful implementation, it is imperative that any microfluidic technology is

able to initially interface with existing technologies. To expect a complete removal of

previous technology from established processes is highly unrealistic, however, one may

expect a paradigm shift over time as re-investment allows processes to accommodate new

approaches.

31

This project seeks to address important areas required for microfluidics to prosper as a viable

screening tool within drug discovery. Firstly; the leakage of compounds from droplets

(Figure 9 a-c). Secondly; the interaction of reagents at the droplet interface and how this may

affect the efficacy or scalability of pharmaceutically relevant drug screening assays (Figure 9

d & e). and thirdly, how to mitigate the challenges presented.

An additional significant challenge, although out of scope for direct investigation in this

project, is how to get large numbers of test compounds from the test tube or microtitre plate

to the microfluidic chip or device. Many examples in the literature consider testing only

relatively small numbers of compounds administered to microfluidic chips using either

custom-designed chip systems or selecting from the limited range of commercialised

microfluidic products (for example, droplet chip products from Dolomite Microfluidics,

Royston, Cambridgeshire), however there is currently no major product solution available

that is perfectly suited for generic screening in microfluidics within higher throughput

screening in the pharmaceutical industry (Figure 9f). This will be considered further in

concluding remarks towards the end of this report.

32

Figure 9. Representation of some of the main challenges to overcome for successful implementation of

microfluidics in industrial drug screening; leakage of compounds from droplets (a-c), interfacial interaction of

biology at the droplet surface (d & e) and how to realise the transformation of compound testing from storage

tube to microtitre plate to microfluidic chip (f).

f.

33

1.2.10 The Cost of Screening

In early phase drug discovery, small molecule drug science relies extensively upon the

screening of novel drug entities against either identified validated targets, or within

phenotypic assays, in microtitre plate formats. To increase throughput, there is an ever

increasing dependency upon 1536-well microtitre plates16. However, not all assays can be

successfully miniaturised to 1536-well volumes, perhaps because of non-specific binding

problems associated with changes in surface area to volume ratios encountered in different

plate formats (larger surface area to volume ratio in the 1536-well compared to 384-well), or

evaporation issues at the smaller volume, hence the 384-well plate may be a good

compromise. In other cases, such as Structure-Activity Relationship (SAR) screening, the

throughput demand is lower and 96-well plates are more commonplace17,82,83.

In high throughput early-phase screening, the process generally includes screening compound

collections of interest (which may run into millions of compounds) at a single concentration,

referred to as the ‘primary assay’. However, as false positives can occur from biochemical or

technology artefacts, there is usually a requirement to run additional confirmation assays to

remove false positives. Following repeats to confirm active compounds, statistical analysis

allows a selection of compounds having a high confidence of being genuinely active against

the target to be progressed to full concentration response assays, whereby full IC50 profiling

can be completed. Structure Activity Relationship screening (SAR) which relates

pharmacologically relevant activity of a compound against the target biology to the molecular

structure is usually conducted as post-high throughput screening operation. As fewer

compounds may be tested at this stage, it is common to find greater use of 96- or 384-well

plate assays17,82. Table 4 highlights the typical plate costs associated with 384- and 1536-

well formats when running a single concentration primary assay.

34

With respect to Table 4, droplet microfluidic technology is highly desirable to lower assay

costs, especially if cheap injection moulding construction is used84,85. For a system using

droplets of 4 to 5 nL, this represents a 400-fold assay volume reduction per test, based on a

one-well to one-droplet comparison.

Table 4. The typical microtitre plate cost for primary screening 2 million compounds in 384 and 1536-well

plates.

In cell-based screening, plate format can be even more critical, with some screens not proving

robust enough in the 1536-well format; cells may not proliferate well, or the smaller number

of cells present do not provide a large enough total-well signal86,87. Typically, around 5000-

10000 cells per 384-well are required in cell-based assays, falling to 1000-2000 in 1536

format17. Clearly, the difference in the cost of cells between plate formats can be quite large,

especially in the case of specialised or primary cell types. Droplet microfluidics would

enable a substantial reduction in the quantity of cells required, which may be in the order of

100-1000 fold lower than 1536-well microtitre plates; however, reduction to fewer than

several cells per droplet may introduce other issues associated with maintaining cell viability

and controlling variability between droplets arising from differing cell densities.

Plate format Cost per plate† Number of plates* Total plate cost

384 £2.80 5682 £15,910

1536 £3.80 1327 £5,040

* assumes 32 wells contain controls in 384-well plate and 64 wells contain controls in 1536-well plate. †based on June 2014 Greiner Bio-One Ltd catalogue as a typical plate type.

35

1.2.11 Integration with Existing Technology

An important aspect of microfluidics is the ability to integrate with existing technology and

processes commonly in use within drug discovery. A critical step to achieve this is to

develop generic ways in which reagents and test compounds are introduced to chip-based

technology for screening. Whilst it is desirable to provide direct compatibility for analysis

(such as with plate readers), it is not critical, as it may be feasible to devise bespoke detection

systems for interrogating microfluidic chip-based systems, similar to that designed in this

work.

The majority of proof of concept (PoC) microfluidic research is often reported to rely upon

syringes or other external vessels and may use a variety of novel and complex on-chip pumps

and valving assemblies by which to introduce reagents to the devices88-90. These are

acceptable for research purposes, however are complicated to produce. To take these

innovations forward and develop microfluidics into a more mainstream commercialised

format that industry can use efficiently and effectively, other techniques should be considered

for reagent introduction. Negative pressure regimes to ‘sip’ solutions91-93, or provide open

micro-well intake ports for acoustic dispensing of compounds94 directly into the microfluidic

chip may be of benefit in this respect. MF devices designed around the design criteria of the

Society for Biomolecular Sciences (SBS) defined format may enable the use of existing

laboratory automation equipment and provide the crucial integration of differing

technologies.

1.2.12 Droplet Assays

The techniques for generating and manipulating droplets in microfluidic devices represent a

‘toolbox’ enabling specific processes to be designed to serve a single or many purposes.

36

Assays used within the pharmaceutical industry are generally either biochemical or cell-

based. The specific requirements of the microfluidic system are thus tailored towards either

environment.

In biochemical screening, it is commonplace for reagents to be dispensed in turn to a

microtitre plate that may include, but is not limited to, process steps for reagent addition, test

compound addition, mixing, incubation and analysis2,17,83.

In the microfluidic format, droplets containing test compound can be fused with other reagent

droplets to produce a reactant droplet that can then be incubated and subsequently analysed

downstream. Other strategies, as reported by Abate et al.95, use pico-injectors located along

the droplet channel to inject reagents from reservoir wells located on the chip directly into

passing droplets. As described by Song et al.96 reagents can be flowed to the droplet

generator (whether this be of T-junction or flow-focus design) where they mix intimately

immediately before being sheared off as reactant droplets. This approach simplifies the

microfluidic system by not requiring complicated droplet fusion methods, however may

become limiting if the number of reagents is large and may also give rise to higher system

dead volumes from the increased channel lengths required in the chip design.

In cell-based screening, cells from the growth culture are added to the reaction mixture and

mixed with test compounds and other required reagents, prior to incubation, etc. In droplet

microfluidic formats, it is thus necessary to encapsulate cells into droplets with known

populations without damage and add other reagents to the cell-containing droplets in a

quantitative manner. Many of the droplet handling techniques discussed above apply equally

to biochemical and cell based microfluidics. However, cells have additional requirements

such as needing nutrients, gas exchange and may need a support on which to survive and

grow, as is the case with a large number of adherent-type cell lines. Despite these

37

requirements necessarily complicating the design of cell-based microfluidic systems, a

number of reports in this field have demonstrated success in maintaining viable cells47,97,98.

1.2.13 Facilitation of Biochemical Screening in Microfluidic Devices

As reagents are dissolved in various buffers or media, it is usually a matter of adding

compound solutions to the biochemical reagents, incubating and then analysing the reactant

droplets17. Reagents can be individually supplied to the chip, or where possible, some

reagents may be pre-mixed ahead of the microfluidic chip environment to simplify the

channel network of the device.

1.2.14 Cell Screening - Encapsulation

As discussed previously, cell-based assay can be problematic for a variety of reasons in

existing microtitre plate assays, including the forcing of cell growth into a primarily 2D

environment (single monolayer of cells)99. Droplet microfluidic technology can be desirable

for cell assays as the environment within droplets can be sterile and largely unaffected by

external influence. The 3D nature of cells encapsulated within droplets offers the potential

for a more physiological relevant environment, more similar to that found for cells in

vivo100,101.

When a suspension of cells is encapsulated using a flow-focusing droplet device102,102-104 the

number cells per droplet follows the Poisson distribution. In this model, changing the density

of the supplied media cell suspension relative to the flow rates employed will change the cell

encapsulation distribution. Whilst the absolute number of encapsulated cells can be tightly

38

controlled, as the supplied cell concentration decreases, the likelihood of droplets containing

no cells increases.

Dk=λkexp(-λ)

k! (13)

Fraction of droplets containing k cells, where Dk is the distribution of cells and 𝝀 the

average number of cells per droplet.

Edd et al.105 reported a method to encapsulate at the single cell-per-droplet level with 98%

certainty using channel geometry to force cells to two evenly-spaced streams whose

longitudinal order is half the cell-cell spacing.

1.2.15 3D Cell Assay & Hydrogels

Some cell types, particularly those of adherent-type, require the use of a growth ‘scaffold’ to

survive and encourage subsequent healthy proliferation. Recently, a number of 3D plate

based innovations have emerged, providing a pseudo-tissue environment, including plates

such as Alvetex™106, Nanofiber™107 and Mimetix™108 which utilise an integral

microfilament polymer scaffold at the base of the plate. Hanging drop plates from

Insphero™109 and the Drop Array plate from Curiox™110 have shown promise as useful

tissue-like environments in which to study 3D cell culture and assay.

However, all of these systems are at best only pseudo-tissue and also have the additional

challenges of evaporation and media replenishment for extended incubation times. In

addition, nanofibre scaffolds obscure imaging of the cells, which is often required when

studying cell assays in 3D-like environments and high content biology (HCB) screening17.

By contrast, droplet microfluidic cell encapsulation can provide a 3D environment, free from

evaporation and contamination, and allowing visualisation of the encapsulated cells by

39

microscopy. In an approach reported by Workman et al.111,112, sodium alginate solution,

CaCO3 and the cell media were co-flowed with acetic acid and carrier oil in separate channels

to a shielded flow-focussing dropletiser to effect hydrogel formation upon release of Ca2+ on

mixing with the acid (Figure 10).

Figure 10. Representation of the shielded junction to generate alginate encapsulated cell droplets. The

sunflower oil in the red channel acts as a shield to prevent premature hydrogel formation which occurs once the

acidified oil contacts the CaCO3 in the media stream, releasing cross-linking Ca2+ ions.

In other work by Crowther et al.113, the target biology was encapsulated in agarose beads. A

temperature difference was used in the encapsulation chip to render liquid agarose (of

typically 1-2%) into semi-solid beads.

Sunflower oil

Sunflower oil +

acetic acid

Cells, sodium

alginate and CaCO3

40

1.3 Microfluidic Devices

1.3.1 Fabrication

Several substrate materials have been used for microfluidic device fabrication, including

glass114, silicon115, polymers116-120 and ceramics121. Silicon was one of the first materials to

be used, since photolithographic and wet etching techniques previously developed in the

electronics industry could be used to build channel features. Silicon is brittle, expensive and

optically opaque in the UV/VIS region, it is thus unsuitable for most of the spectroscopic

detection techniques used in biochemical applications. In the photolithographic / wet etching

process, the substrate is coated with a layer of metal, such as chromium (a few nanometers

thick) by vacuum deposition. A layer of photo-resist, which becomes soluble after exposure

to UV light, is spin-coated on top of the metal layer. A mask of the channel design is placed

over, the assembly exposed to UV, and the unmasked areas washed out and the metal

underneath etched away. The substrate surface is now protected except where features are

required, which can be made by etching with hydrogen fluoride/nitric acid (HF/HNO3)122 or

HF/ammonium fluoride (NH4F)123. The remaining metal and photo-resist can be etched off

and the chip bonded to another piece of substrate to form the final device.

Glass is a more common substrate material as it has good optical properties, is cheap, and has

well-characterised surface properties, although, like silicon, can be brittle. In addition, the

fragility may suggest glass is not appropriate for applications requiring very high density

surface features of small dimensions.

Polymers are commonly used for microfluidic devices, because of the low cost and the much

wider range of mechanical characteristics, surface chemistries and optical properties

available. Several methods have been exploited for fabricating polymer microchip devices,

41

including soft photolithography124,125, injection moulding126, laser ablation127 and milling

using direct computer numerical controlled machining (CNC)128.

Laser ablation techniques involve directing laser energy to the substrate material either via a

mask or by using X-Y control of the laser. The laser pulse length, light frequency and

properties of the material to be ablated determine the mass of material removed. In the case

of plastic laser ablation, the laser energy breaks the long-chain polymer bonds (over a

confined distance measured in micrometres), ejecting material from the surface to leave

features that can be smooth with little thermal damage, although only a limited number of

polymers are suitable. The technique is relatively slow, potentially limiting its usefulness for

producing large numbers of microchip devices.

Injection moulding is a rapid method for microchip production. In this process, a device is

made by melting polymer granules and injecting the resulting liquid polymer into a mould

under high pressure. Individual devices can be produced every 5-15 seconds, meaning the

technique is suitable for mass-production of consumable devices, as well as being attractive

to commercial applications requiring sterility (each device can be considered disposable)

and/or high throughput. The process is reliant on a suitable mould being made first by either

photolithographic or CNC techniques.

Table 5. Approximate scoring of microfluidic device construction methods.

Fabrication

method

Production Cost

(1 = cheap, 5 =

expensive)

Production Speed

(1 = slow, 5 = fast)

Feature Size

(1 = small, 5 = large)

Accuracy of Features

(1 = Low; 5 = High)

Photolithography 5 3 1 5

CNC 3 1 5 3

Laser ablation 4 2 4 4

PDMS moulding 2 4 2 1

Injection

moulding 1 5 3 2

42

In instances where reproducibility of many identical devices is required, positive moulds can

be CNC machined in a variety of metals, which can then be used to rapidly produce injection

moulded devices.

An alternative option is to mould devices directly from a curable material such as

polydimethylsiloxane (PDMS), although this approach may not be suitable for all

applications as organic oils can be absorbed by the PDMS causing it to swell and deform129.

PDMS is relatively cheap and has been used effectively in MF research130-132.

Several polymers are very well suited to rapid prototyping by CNC machining, such as

PMMA and polycarbonate (PC), thus removing many of the expensive and complicated

processes that are required for other substrate materials and techniques. Direct machining

often cannot produce features smaller than about 50 µm due to limitations in CNC machine

accuracy and available milling tool size, however, the relative simplicity and convenience of

the technique allows devices to be produced on a scale appropriate for proof-of-concept

microfluidic research. Production of high numbers of replicate devices remains a challenge

using this approach.

1.3.2 Sealing

To render them operable, the majority of chip devices require sealing after the features have

been formed. Sealing should not block the channels and ideally not alter the properties of the

device by exposing reagents in the channels to adhesive which may leach in and affect the

contents of the channels. Glass devices prove one of the harder materials to seal as a

consequence of the high bonding temperatures required133. Lower bonding temperatures

have been reported using sodium silicate134.

43

Self-adhesive labels have been used to some success for sealing devices119. This is cheap and

quick to effect, however is not suitable for very long channel devices as high back-pressure

often results in seal failure, and in some cases, the adhesive present on the seal directly above

the channel features can dissolve or detach into the fluids passing through, thus

contaminating reagents, or result in channel blockages. A related technique that achieves a

stronger seal is to use acetate sheet or fluorinated ethylene propylene (FEP) or film bonded to

the substrate with a UV-curable epoxy resin135. Although free of adhesive directly above the

channel, the seal can still fail at extreme back pressures. Care has to be exercised during

assembly to ensure resin does not overflow and block the channels.

Glass microscope slide cover slips have been investigated as part of this project for sealing

microfluidic devices. In this case, UV curable epoxy resin was applied as a thin layer to the

substrate surface via intermediate acetate sheets (to progressively thin the resulting layer of

adhesive transferred) before placing the slip and curing with UV light. Benefits of this

technique include optical transparency in the majority of the UV-VIS range and the absence

of adhesive over the channel features, however, mechanical differences between the polymer

substrate and glass seal were found to lead to the glass cover cracking and/or de-lamination.

In addition, the naturally hydrophilic surface of the glass can interact and disturb droplet

production and flow, thus pre-processing of the glass slips, such as fluoro-silanisation136 to

increase hydrophobicity may be advantageous.

Optically clear polyester microtitre plate seals, obtained from Corning Life Sciences, may be

used to seal polymer substrate devices. The low-tack adhesive is largely unaffected by a

range of fluids flowed through the microfluidic chip and appears to bond better over time to

PMMA substrates. Crucially, the sealed regions used for droplet detection remain clear and

transparent when exposed to the carrier fluids, and thus are suited to spectroscopic analysis.

44

1.3.3 Device Surface Functionalisation - Hydrophobicity

The surface of polymers can be treated for two purposes; i) to improve hydrophobicity and ii)

functionalisation to perform specific tasks, such as surface-activated droplet fusion.

The majority of common polymers, are hydrophobic, having contact angles with water of

approximately 65-80°. Fluorinated polymers, such as polytetrafluoroethylene (PTFE) do

have higher contact angles (up to 112°)242 but are too soft for CNC machining. High

concentrations of proteins and other materials having pronounced surface activity within the

dispersed phase can lead channel wall ‘wetting’ and hence disrupt or prevent droplet

formation resulting in bi-laminar flow of the dispersed and carrier phases through the

channel. Surface modification to increase the contact angle with polar materials can be

achieved through either applying a coating of water-repellent material via a solution (for

example, AquaPel™ or Certanol™; both fluoropolymers dissolved in a volatile organic

solvent that is allowed to evaporate, depositing a thin layer of the fluoropolymer to the

channel wall); or through chemical reaction of the surface to reduce surface molecular

polarity243. In the first instance, coatings tend to be temporary and can be washed off over

time, thus needing replacement.

Figure 11. Functionalisation of polycarbonate with dodecylamine to render a more hydrophobic surface.

Dodecylamine reacts at the carbonyl group, eliminating water and forming an amide.

Chemical reaction of the surface is permanent, but may only apply to a select number of

polymers that have suitable reactivity. For example, polycarbonate is particularly suited to

O

O

OOH O

O

O *m

δ+

O

O

NH

OH

n

EtOH, 60°C, ~ 4 hrs

O

O

O *OHk

+

NH2..

Polycarbonate

unit

45

chemical modification. Dodecylamine has been used successfully to yield greater surface

hydrophobicity137 (Figure 11).

1.3.4 Functionalisation of Polymer Chips

Modification of channel features can be applied to provide additional functionality, such as

droplet fusion regions. In this situation, a section of the channel is rendered more hydrophilic

(often through chemical reaction) such that droplets will enter the region and ‘stick’ allowing

carrier fluid to pass either side. When a subsequent droplet catches up, this will either

spontaneously merge with the immobilised droplet, or be coerced to merge using a disruptive

electric field. The increased physical size of the resultant droplet then provides sufficient

hydrodynamic resistance in the channel to be moved from the hydrophilic region138.

1.3.5 Fluid Connection

CNC machining of microfluidic features is a convenient method that allows rapid prototyping

of devices. Fluid connections to the chip can be addressed by two common methods. Un-

insulated bootlace ferrules (RS components, 211-4252) can be push-fitted into through-

drilled holes on the reverse side of the chip. This provides a simple means for connecting

narrow bore tubing, through which the required reagents can be flowed to supply the

microfluidic chip. In certain cases, there may be a need for greater mechanical connection

stability, such as when encountering higher back pressures. In these instances, the holes can

be tapped, and screw-in fluidic terminals can be inserted, such as those made by The Lee

Company (Figure 12).

46

Figure 12. Fluidic connection methods for microfluidic devices; screw-in Luer-lock fitting (left) and push-fit

bootlace ferrule (right).

Either of the connection methods described here tend to be compatible with fluid connections

typical found on a variety of instrumentation often found within scientific industry, such as

High Pressure Liquid Chromatography (HPLC), autosamplers and other liquid handling

workstations7,34,76,139.

1.3.6 Fluid Propulsion - Hydraulic Pressure

One of the easiest methods to move liquids within micro-channels is by applying pressure.

The necessary pressure can be achieved by elevating the reagents fed to the chip and relying

on potential energy to force fluids (‘gravity fed’). Whilst this approach is excellent with

respect to generating completely smooth (pulse free) reagent delivery, the pressure required

to overcome the higher hydrodynamic resistance in more complicated and/or longer micro-

channels can result in very high and impractical physical placement of the reagent vessels

relative to the microfluidic chip. Moreover, gravity flow systems can be difficult to set up

accurately, especially in the cases where the pressures for correct microfluidic device

function require careful, specific adjustment of relative flow rates. Additionally, multi-source

flows may suffer from hydrodynamic coupling244 making individual fluid flow rate

adjustment difficult.

Fluid flow is well characterised principally by the Navier–Stokes equations describing fluid

motion140-142. For incompressible fluids (liquids), the volumetric flow rate, Q (m3 s−1) is

47

proportional to the pressure drop, ΔP (N m−2) and fluidic resistance R (m5 N–1 s−1) as

described by the Hagen–Poiseuille law143, equation 14:

RPQ .

(14)

Fluidic resistance (R) is dependent on the channel geometry and fluid viscosity, which for a

channel approximating a capillary can be described by Equation (15):

tL

dR

128

4

(15)

Where d is the capillary inner diameter (m), is the viscosity of the fluid (N s−1 m−2)

and Lt is the total length of the capillary (m).

From these equations, it can be deduced that as channel dimension decreases, the pressure

required for a given flow rate increases, and as the channel length increases.

The Hagen-Poiseuille law applies to fluid flow through channels that exhibit laminar flow,

i.e. where there is no turbulent flow.

This is characterised by the Reynolds number (Re)144, equation 16:

uLRe

(16)

Where is the fluid density (g m3), is the viscosity (N s−1 m−2), u is the average

linear velocity (m s−1) and L is the characteristic channel length (m). This length is

the dimension that best describes the channel section in which the fluid flows. For

the majority of microfluidic devices, this is thus most likely to be the channel width

or height. Values of Re < 2000 are generally considered to exhibit laminar flow

whilst Re > 2000 exhibit turbulent flow.

The required pressure to flow liquids is more often achieved using pumps, either of the

syringe or peristaltic type connected to the device, although there are reported instances

where centrifugal force can be utilised to promote flow, such as in the experimental Lab-

48

CD™ system researched by Tecan139 and Gyros145 or the programmable microfluidic assay

system using ‘Virtual Laser Valves’, such as that developed by SpinX146.

A small number of devices have been reported to work successfully using negative pressure

regimes to pull fluids through the chip147. A benefit of this approach is the ease by which

materials can be drawn into the chip, potentially simplifying connection to microtitre plates

and other fluid sources as the system input works at atmospheric pressure. However, such

systems will be limited to a pressure differential of 1 atmosphere and may be prone to

instability from the release of dissolved gases in the fluids within the device when under a

negative pressure regime.

1.3.7 Fluid Propulsion - Electro-Osmotic Flow (EOF)

Fluids can be driven through micro-channels using electro-osmotic flow (EOF)148. EOF is

the volume flow of fluid containing charged species in a narrow bore capillary or channel that

has suitably charged walls. For example, in the case of a fused silica capillary containing a

buffer liquid, the cations from the buffer are attracted towards the negatively charged silanol

(Si-OH) groups on the inside walls of the capillary (Figure 13). For EOF to be wholly

effective in glass, the pH is ideally adjusted to around pH 9 to ensure all silanol groups are

ionised. This results in an immobilised layer of cations adjacent to the surface. However, the

negative silanol charge is not balanced, so a second layer of cations, less strongly bound,

adhere to the first layer.

49

Figure 13. Electro-osmotic flow in a fused silica capillary. Flow is induced in the liquid (indicated above the

shear plane line) when a tangential electric field is applied, by the polarisation of buffer relative to the cationic

‘wall’ resulting from the attraction of ions to the negatively charged silanol groups of the capillary.

The application of a tangential electric field results in the second layer cations being attracted

to the cathode, which in turn, promotes the flow of liquid with the cations (Figure 13)149.

With the requirements of specific pH for efficient EOF, this method of fluid propulsion may

not be compatible with assay systems required to run at physiological pH and will also not

work for non-ionisable, neutral compounds.

The shear plane referenced in Figure 13 represents the ionic imbalance and is termed the zeta

potential. This potential, , is given by equation 17.

e4

(17)

Where is the mobile layer thickness, e is the charge per unit surface area and is the

dielectric constant of the fluid (buffer).

The electro osmotic flow velocity can be calculated by equation 18.

EE

V EOFEOF

4 (18)

Where is the dielectric constant of the fluid (buffer), is the zeta potential, E is the applied electric field, is

the viscosity of the buffer and EOF is the electro osmotic mobility.

SiO-SiO-SiO-SiO-SiO-SiO-

SiO-

Shear plane

Neutral buf fer

Mobile layer

(net positive

charge)

Fixed layer

(net negative

charge)Capillary Wall

++

+

+ + ++

++

+

+

+ -Flow

++

+

++

+

50

1.3.8 Fluid Propulsion - Centrifugal Force

Generally applied more to the field of continuous or single phase microfluidics, fluids can be

moved through channel features using centrifugal forces. As previously discussed, a number

of commercial devices have been reported that utilise a spinning disc, or ‘lab CD’ format

whereby chambers on the ‘disc’ are filled with reagent and the disc spun to entrain liquids

through the chambers to various incubation and detection zones via a variety of valves and

capillary channels.

Capillary valves are often used to ensure fluids to do not spontaneously flow along the

channels prematurely due to capillary forces; thus making highly complex and multi-stage

processes possible whereby higher rotation speeds, hence higher centrifugal forces, can be

used to breach capillary valves designed with higher burst pressures150. Other valve

arrangements have been employed that utilise the change in level of fluids due to centrifugal

force to trigger capillary flow, thus making siphon valves possible that continue to drain fluid

from one compartment to another even as the speed of rotation is reduced. This format has

been reported to work successfully for a variety of blood-plasma151 and cell separation

processes152.

1.4 Detection and Analytical Techniques

A majority of assay analysis conducted in early to middle phase drug screening often uses

spectrophotometric techniques such as absorbance, fluorescence and luminescence for ease of

analysis and increased throughput16. In certain cases, it is necessary to employ more

advanced methods, such as fluorescent polarisation (FP)153, Förster resonance energy transfer

(FRET)154 or homogeneous time resolved fluorescence (HTRF)155 to enrich the detection

technique with a mode allowing for kinetic and spatial dependence of the analytes within the

51

sample. To help understand how these analytical methods work, it is necessary to first

understand the underlying principles of energy absorption and release within molecules, as

described in the following sections.

1.4.1 Jablonski Diagrams

The Polish academic, Aleksander Jablonski (1898–1980), devoted his life to the study of

molecular absorbance and emission of light . He developed the well-recognised generalised

representation of electronic energy states in molecules; the ‘Jablonski Diagram’156. Often

drawn schematically, the diagram (Figure 14) identifies electronic energy states (bold lines)

with these subdivided into multiple vibronic eigenstates (thin lines) coupled to the electronic

state. Not all states are represented, as a vast number of vibration states are possible within a

molecule. Each vibrational eigenstate can be sub-divided into rotational energy levels;

however, these are nearly always omitted for simplicity of the diagram. As electronic energy

states increase, the energy delta decreases, eventually reaching a continuum. Similarly, the

vibrational energy states within each electronic level get close together as the limit of the

electronic state is reached.

Figure 14. Jablonski diagram depicting electronic (S0-S3) and vibrational (v1-v5) energy states. Thick horizontal

lines are the electronic states and the thin lines vibrational energy states. The wavy line represents internal

conversion from singlet to triplet state. S0 is the ground singlet state and T1 the ground triplet state.

E

T1

S3

S2

S1

S0

E

v4

v1

v2v3

v4

v1

v2

v5

v3

52

In the diagram, straight arrows represent conversion between a photon of light and the energy

of an electron, whereas wavy lines show energy transitions of electrons without emission of

light.

1.4.2 Absorbance

The simplest representation in the Jablonski diagram, indicated by the blue arrows in Figure

15, is the absorbance of a photon of a particular energy (wavelength) by the molecule. An

electron is excited from a lower to higher energy state, where the energy of the photon is

transferred to the electron. This process is very fast, occurring in the region of 10–15 seconds.

Usually, the transition will occur from the ground electronic state, where according to the

Boltzmann distribution there is the greatest number of electrons occupying the lowest lying

state at normal temperatures. Absorption transitions can occur between electronic states and

between vibration states.

Figure 15. Jablonski diagram depicting absorbance (blue arrows) between different electronic eigenstates.

E

T1

S3

S2

S1

S0

E

v4

v1

v2v3

v4

v1

v2

v5

v3

53

1.4.3 Vibrational Relaxation and Internal Conversion

After excitation, there are a number of routes by which the energy will be dissipated. The

first is via vibrational relaxation, a non-radiative process where electrons drop to lower

vibronic eigenstates (Figure 16). The energy is lost either through transfer to other molecules

or within the same molecule as kinetic energy and can occur very quickly after absorbance.

As this is a vibrational energy loss, electrons do not generally change electronic state at this

point, unless there is an overlap between electronic and vibrational energy levels. In this

instance, transition from a vibration level in one electronic state to another vibration level in a

lower electronic state is referred to as internal conversion. The large energy gap between the

ground electronic state and the first excited state, translates to internal conversion being

relatively slow. This condition allows other transitive processes to dominate, such as

fluorescence or phosphorescence.

Figure 16. Jablonski diagram depicting absorbance (blue arrow), internal conversion (red arrow) and

vibrational relaxation (green arrow).

E

T1

S3

S2

S1

S0

E

v4

v1

v2v3

v4

v1

v2

v5

v3

54

1.4.4 Fluorescence

Another route by which molecules can lose absorbed energy is fluorescence, which releases a

photon. This is represented by a straight arrow (Figure 17) between electronic energy states.

As fluorescence is relatively slow (in the order of 10–9 to 10–7 seconds), it is unlikely that

electrons will dissipate energy in this manner from states higher than the first excited state.

The energy of the photon emitted in fluorescence is the same energy difference as that

between the eigenstates of the transition; however, the energy of the emitted photon is always

less than that of the exciting photon as a consequence of internal conversion and vibrational

relaxation. Due the number of vibrational levels that may be involved with the transition

between electronic states, the emitted photon energy is usually distributed over a range of

wavelengths.

Figure 17. Jablonski diagram depicting absorbance (blue arrow), internal conversion (red arrow) and

fluorescence (purple arrow).

E

T1

S3

S2

S1

S0

E

v4

v1

v2v3

v4

v1

v2

v5

v3

55

1.4.5 Intersystem Crossing (IS)

A further route a molecule may dissipate absorbed energy by is intersystem crossing. In this

process, the electron changes spin multiplicity from an exited singlet state to an excited triplet

state. On the Jablonski diagram (Figure 18), this is referenced by a wavy arrow crossing

between columns representing different electron spin multiplicities.

Figure 18. Jablonski diagram depicting absorbance (blue arrow), internal conversion (brown arrow) and

phosphorescence (yellow arrow).

This process is the slowest of all, being several orders of magnitude slower than fluorescence.

Although a forbidden transition, based strictly on electronic selection rules, by vibrational

coupling, the transition becomes weakly allowable and able to compete with the time scale of

fluorescence. IS results in a number of routes back to ground state including

phosphorescence, a radiative process where transition from excited triplet state to a singlet

ground state occurs, and delayed fluorescence, where decay back to the first excited singlet

level occurs followed by emissive transition back to ground state.

E

T1

S3

S2

S1

S0

E

v4

v1

v2v3

v4

v1

v2

v5

v3

56

1.4.6 Absorbance Detection and Microfluidics

Measurements of this type are easier to implement in continuous phase MF due to the relative

ease by which the sample can be presented within a channel to a fibre-optic guided

spectrophotometer157. In droplet MF, absorbance measurements can be considerably harder

due to the challenge of presenting a uniform sample path to the spectrophotometer; the

curvature of the droplet and its position within the channel can impact on the light path by

undesired refraction and reflection at the droplet-oil interface. One way to reduce this

artefact is by constricting the droplet within a channel narrower than the droplet diameter

such that the top and bottom of the droplet are perpendicular to the light path. However, due

the small size of the droplets in MF, absorbance may not prove sensitive enough for low

analyte concentrations and materials having a weakly absorbing chromophore.

1.4.7 Fluorescence Detection and Microfluidics

Generally, fluorescence is a more sensitive technique than absorbance as the background and

interference is often lower and is thus particularly suited to droplet MF, especially for

analytes having fluorophores with high quantum yields where the emissive light intensity is

high245. In addition, there are a number of applied fluorescence analysis techniques including

fluorescent polarisation246 (Figure 19a, b), where polarisation of the excitation and emission

light can be used to determine molecule rotation during the acquisition time and thus

ascertain whether a binding event may have occurred for example, and fluorescence

resonance energy transfer (Figure 19c) where physical close proximity of reagents having

different overlapping excitation-emission spectra fluorophores can signify other reaction or

binding events.

57

1.4.8 Luminescence Measurement and Microfluidics

Certain chemical or biochemical reactions can result in excited molecular states that then

decay to ground by emitting light known as chemi- or bio- luminescence158.

Where it is feasible, this technique is particularly suitable for microchannel techniques since

if offers very high sensitivity, largely due to lower background noise and separation of the

excitation-emission spectra. In addition, unlike conventional fluorescence techniques,

luminescence is not subject to background emission from other materials within the system

(unless there are materials present that are excited by the emissive luminescent light resulting

in background fluorescence). Conventionally, a luminescent substrate is added post-reaction

which then develops a light output that can be quantified to determine the progression of

reaction. Luminescence is an attractive option for screening processes as only one

wavelength of light is quantified for all assays of the same type. In the context of MF, the

detector setup can be very simple using PMT or CCD modules with ADC to convert the light

output to an analogue signal159.

58

Figure 19. (a) Fluorescent polarisation (FP) diagram. Fast molecular rotation during the acquisition time,

gives rise to roughly equal vertical and horizontal polarised emission. (b) In comparison, since the molecule has

bound to a large entity, fast rotation is not possible and as such, a bias towards vertical or horizontal

polarisation is observed. (c) Fluorescence Resonance Energy Transfer (FRET) spectroscopy. This mode of

fluorescence is dependent upon the emission spectra of one fluorophore overlapping the excitation spectra of a

second fluorophore. The effect requires close proximity of the fluorophores and thus can be used to measure

movement, spatial orientation or binding of molecules.

(a)

Monochromatic light

Vertical polariser

Vertically polarised light

Molecules with free rotation

Fast rotation

emission light

Vertical and horizontal emission

( b )

emission light Vertical and horizontal polarisers

Vertical emission

Monochromatic light

Vertical polariser

Vertically polarised light

Molecules with l i m i t e d r o t a t i o n

N o f a s t rotation

(c)

Horizontal and vertical polarisers

59

1.4.9 Mass Spectrometry and Microfluidics

In recent years, mass spectrometry (MS) has made significant steps forward with the

development of nano-electrospray sources160 allowing quantification of analytes from lower

source volumes and input flow rates. This reduction has enabled microfluidic integration to

MS161-163. One of the major challenges in droplet microfluidics in respect of MS detection is

removal of the carrier oil phase prior to introduction to the MS source. In recent years, a

number of research groups have demonstrated dielectrophoretic (DEP) methods of droplet

extraction from the carrier phase; in one case where droplets are ejected directly from a

channel via a 'spyhole' into a mass spectrometer sample cone using a high voltage electrode

adjacent to the channel164 and another example where the droplets are moved using DEP into

an analyte counter-stream and then injected into an electrospray ionisation mass spectrometer

source165. Despite gains in mass spectrometer sensitivity and efficiency of coupling droplets

to mass spectrometers, there remains a challenge to yield MS detection that is wholly

equivalent in sensitivity and reproducibility to spectrophotometry techniques.

1.4.10 Other Detection Methods and Microfluidics

Additional detection techniques that are commonly employed in microfluidics include

amperometry, potentiometry and conductivity. Amperometry is a sensitive technique which

involves measuring a current generated when an analyte produces electrons in a redox

reaction. A limitation of this approach is the requirement for electrodes to be directly in

contact with the reagents which can result in fouling and subsequent contamination. In the

case of potentiometry, the potential difference (voltage) is measured ideally keeping the

current across the system to zero166.

60

1.4.11 Advantages and Disadvantages of Analytical Techniques

Most, if not all, spectrophotometric techniques have been successfully incorporated into

microtitre plate based assays167-169. As the availability of cheaper, smaller and more sensitive

detector electronics have become available, it has been possible to reduce analyte sample

volumes, in parallel with the miniaturisation of assays, to speed up data acquisition. The

relative simplicity of most photometric methods results in it being possible to collect data

rapidly. Coupling to this the relative simplicity of assays used in early phase drug screening

where an ‘add-mix-read’ approach is common17, it is usual to find utilisation of

spectrophotometric outputs to make tests easier to perform17.

A possible limitation of spectrophotometric analysis is the requirement to measure a probe or

label which has to be incorporated into the biochemical or cell reaction if there is no native

measurement at the required wavelength(s). In certain cases, this may be detrimental to the

biology of the system concerned (interference with reaction) and may have implications to

the range of signal, sensitivity or discrimination available170. For example, when screening

for cytochrome P450 enzyme inhibition, the assay can be quantified either by MS or

spectrophotometry (see section ‘P450 Cytochrome Inhibition’). Mass spectrometry is much

slower and does not yield the same throughput of spectrophotometry, however does have the

potential to provide a more pharmacologically relevant data set by using materials that are

more ‘drug-like’.

61

1.5 Droplet Production and Manipulation

For a biphasic droplet MF system to be successful it is necessary to reproducibly form

droplets of a quantifiable size and ideally at a frequency of production that suits the overall

assay-analysis throughput requirement. These are essential minimum requirements for this

project. There are two common methods described in the literature that are appropriate for

the majority of research conducted into droplet MF; (i) the T-junction droplet generator182,

and (ii) the Flow focussing droplet generator142. The following sections describe the key

factors that help to determine how droplets may be formed in microfluidic systems.

1.5.1 Surface Tension

In bi- or multi- phase liquid systems, the surface tension of the fluids involved has a

significant central role on how those liquids behave, and in the context of droplet

microfluidics, surface tension largely defines the behaviour of the droplets in the system171.

To understand surface tension, we can visualise a single liquid droplet suspended in a gas.

Molecules of the liquid within the droplet have equal and opposite inter-molecular forces

between them in all directions (van der Waals forces), but at the surface, the forces only exist

in the direction towards the inside of the droplet and adjacent molecules. This results in a net

force of molecular attraction that manifests as a contraction of the surface of the droplet, that

is, the droplet’s surface tension172.

The surface tension of a liquid is a contractive force resulting from the force imbalance found

at the surface., This surface is often the boundary between the liquid and air (Figure 20) and

is inversely dependant on temperature and the action of surface-active molecules such as

62

surfactants. Dimensionally, surface tension, σ, is force per unit length, corresponding to Nm−1

or sometime by the unit dyne/cm (where 1 dyne/cm = 0.001 Nm−1).

Figure 20. Schematic representation of the molecular Van der Waals forces giving rise to the phenomenon of

surface tension. Molecules near the surface of the liquid experience an imbalance of net force such that

interactions are greater in the plane of the fluid surface. Surfactants can interact to reduce intra-molecular

attraction.

The stronger the inter-molecular bonding within the liquid, the greater the surface tension, as

there is an increase in the force per unit area at the liquid surface. Surfactants (surface active

reagents) are amphiphilic molecules, that is, having both hydrophilic and hydrophobic

ligands. This property enables surfactants to be adsorbed to the surface orienting according

to the polarity of the liquid concerned.

In the context of two immiscible liquids, such as water and oil, the interface between the

liquids is the zone where surfactants will diffuse to, forming a localised surfactant molecule

concentration. In doing so, the surface tension of the fluids is reduced. Antonov’s rule173

states the interfacial surface tension of two immiscible liquids can be found by equation 19:

γ = |σ1 – σ2| (19)

Antonov’s rule for interfacial surface tension. Where γ is the resulting interfacial tension,

σ1 and σ2 the surface tension of the two liquids in respect of the liquid–air boundary.

WATER

AIR

63

For biphasic MF, the action of reducing the surface energy of fluids is an important factor in

droplet formation. The majority of dispersed phases in biochemical microfluidics

applications are aqueous, thus this phase will tend to exhibit a relatively large surface tension.

If the carrier oil being used has a very low surface tension, it is quite possible (subject to

other factors, such as flow rates and channel geometry) that forming droplet emulsions

becomes impossible, or at least difficult, with the resulting emulsions not being very stable

due to the aqueous phase not breaking-up readily62.

The addition of surfactant to the liquids will reduce the interfacial surface tension sufficiently

so that droplets can form174. Furthermore, the surfactant will migrate to the droplet surface

stabilising each droplet from auto-coalescence as the hydrophobic surfactant tails will tend to

prevent droplets from becoming close enough to fuse.

In the context of biological or biochemical assay, it is important that surface chemistry and

protein adsorption at the liquid-liquid interface is carefully controlled. Surfactant selection is

an important criterion in this respect and, as reported by Holt et al., surfactants having

different polarity head groups, can significantly affect the adsorption of biological materials

to the droplet interface175. Measuring surface tension by drop tensiometry showed that

surfactants containing carboxylic acid and alcohol head groups gave an enhanced adsorption

of fibrinogen to the droplet interface compared to a surfactant containing a non-ionic

oligoethylene glycol functional group176. Furthermore, it has been previously reported by

Holtze et al.177 that short-chain perfluorinated surfactants containing polar head groups, such

as perfluorooctanol, do not provide sufficient long-term emulsion stability. Droplet formation

and the interaction of reagents at the droplet interface is explored in the context of this project

in later chapters.

64

1.5.2 Surfactants in Droplet Microfluidic Systems

The critical micelle concentration (CMC) of a surfactant relates to the concentration where

the surface tension is measured to significantly inflect. As the number of surfactant

molecules reaches a critical concentration, no more can be adsorbed to an interface and thus

the pronounced formation of micelles whereby surfactant molecules form colloidal clusters

containing a hydrophilic or hydrophobic centre, are formed.

There are a number of methods for determining the CMC of a surfactant; all of which rely

upon measuring the change in a characteristic of the fluid in which the surfactant is tested.

Fluorimetry247, absorbance248 and conductivity249 can be used, although measurements of

surface tension via hanging drop tensiometry or capillary rise are more often encountered110.

The challenges associated with measuring the CMC of the custom fluorosurfactant are three-

fold; firstly, the fluorosurfactant is designed to be soluble in the fluorous phase and only very

sparingly in the aqueous environment; secondly, because the surface tension of the fluorous

phase is already very low (~0.018 Nm−1 cf. water at 0.072 Nm−1) adding surfactant only

serves to further reduce this surface tension making measurements by capillary rise very

difficult as frictional forces may tend to dominate capillary forces. Conductivity

measurements may be difficult or impossible when using non-ionic fluorous oils as the

carrier phase.

An alternative means of determining the CMC is to measure the back scatter of light caused

by micelle formation. Dynamic Light Scattering (DLS) is a technique often used for particle

sizing in the sub-micron range by relying on time-dependant fluctuations in the intensity of

scattered light from a suspension of particles undergoing random Brownian motion178. From

analysis of the intensity fluctuations, the diffusion constant and the particle size can be

calculated using the Stokes–Einstein Equation (Equation (20)).

65

𝐷 = 𝑘𝑇

𝑁𝐴.

1

6𝜋𝜂𝑟 (20)

Where D is the diffusion constant, k the gas constant, T the absolute temperature, NA is Avogadro’s number, η

the fluid viscosity and r the radius of the particle.

Depending on the chemistry of the fluids and the nature of the work being conducted with the

microfluidic system, it may be necessary to incorporate surfactants to either or both phases.

This may be for the purpose of stabilising droplet formation (and prevention of auto-

coalescence) or for biocompatibility to prevent interaction of reagents at the interfacial fluid

boundary177.

For example, triglycerides, siloxane oils and octanol do not necessarily require additional

surfactants to robustly generate droplets due to their relatively low interfacial surface tension.

In these cases, however, it is noted the droplets are not stable with respect to auto-

coalescence upon collision (refer to section on ‘Droplet Formation’). With systems using

fluorinated carrier fluids, the use of surfactant is often required to reduce the normally high

interfacial surface tension. Perfluorooctanol has been used as a surfactant in a number of

experimental droplet systems179,180.

Other approaches have involved the use of more complex fluorosurfactants to provide

additional benefits, including improved biocompatibility. Holtze et al.177 and Roach et al.176

described the synthesis and use of a perfluoropolyether polyethylene glycol (PFPE-PEG) co-

block fluoropolymer surfactant designed to not only facilitate and stabilise droplet formation,

but also provide a higher degree of biocompatibility by reducing the affinity for non-specific

binding at the droplet interface by incorporating a non-ionic oligoethylene glycol group.

A material based on the work of Roach was prepared for this project and used throughout.

Droplet formation and also the impact on droplet leakage and interface behaviour of proteins

in solution are considered.

66

1.5.3 Reynolds and Capillary Numbers

In fluid dynamics, one of the most widely used dimensionless values is the Reynolds number

(Re) which compares inertial and viscous forces within a fluid. The size of the fluid system

largely dictates the nature of the forces and whether fluid flow is turbulent or laminar,

described by equation 21.

𝑅𝑒 = 𝜌𝑢𝐿

𝜂 (21)

Where ρ is the fluid density, u the velocity of flow, the characteristic length of the

system and η the dynamic viscosity of the liquid.

The capillary number is a dimensionless value that compares the surface tension forces to the

viscous forces acting across a liquid interface which is a significant factor in microfluidic,

low Reynolds number, situations. Briefly, the capillary number, Ca is defined by Equation

(22).

𝐶𝑎 = 𝜂𝑢

𝜎 (22)

Where 𝐶𝑎 is the capillary number, and 𝜎 the interfacial surface tension between

the fluids.

1.5.4 Droplet Detachment: Squeezing, Dripping and Jetting

Garstecki et al.181, Menech182 and Fu142 have described different modes of droplet formation:

squeezing, dripping and jetting and how flow in microfluidics systems may transition

between these regimes. In the first instance, the squeezing regime relies upon complete or

partial blockage of the channel by the dispersed phase, leading to a build of viscous forces

that eventually overcome surface tension forces and break off a droplet of the dispersed

phase, often slightly down stream of the channel junction (Figure 21a).

67

Figure 21. (a) droplet formation via the ‘squeezing’ regime where the dispersed phase partially blocks the oil

channel; (b) droplet formation via the ‘dripping’ regime and (c) droplet formation via the ‘jetting’ regime,

normally found in higher flow rate flow-focussing devices.

Capillary numbers (Ca) are usually very low in this mode. As the Ca number increases, it is

possible for the dripping regime to manifest; this relies upon a combination of build-up

pressure and shear stress to promote droplets into the carrier phase (Figure 21b). With the

jetting regime, not encountered as often in microfluidics, the capillary number and/or flow

rate is very high and is more usually associated with co-focussed flows (Figure 21c).

1.5.5 T-Junction and Flow Focussing Droplet Generation

The T-junction is the most frequently encountered arrangement used to produce fluid

segments (plugs) and droplets. Aside from requiring the right conditions for squeezing or

dripping droplet formation142,183, the contact angle of reagents (wetting properties) are also

highly important to ensure dispersed phases do not wet the channel wall, which otherwise

results in a co-flow of fluids that do not result in droplet break-off (Figure 22)184185.

(a) (b)

(c)

Direction of flow

Direction of flow

Direction of flow

68

Figure 22. Incorrect surface tension, viscosity and/or flow rate/geometry combinations can result in laminar

flow of immiscible fluids without dispersion in to droplets. Aqueous phase ‘wetting out’ of the chip material can

also lead to this condition184.

Figure 23. Typical flow focussing droplet generator. Flow rates tend to be higher than for the T-junction and

typically operate in the dripping to jetting regimes15.

Flow focussing devices (Figure 23) behave similarly to the T-junction and have three distinct

modes of operation: (i) droplets are formed immediately at the junction; (ii) droplets form

downstream after a period of laminar flow (Figure 21c) and (iii) a thread of laminar flow is

formed that does not break up into droplets. The capillary number and flow rate ratios are

important predictors of the flow regime that will prevail in any one system.

1.5.6 Oils for Carrier Phase Fluid

For droplet MF systems, the choice of carrier phase oil is important with respect to droplet

formation, in terms of interfacial surface tension between the dispersed and carrier phases. In

addition, the viscosity of the carrier oil is an important selection criterion. Oils having a high

viscosity will exhibit much higher back pressures, requiring higher pressure differentials to

Dispersed phase,

e.g. aqueous

reagents.

Carrier phase, e.g. oil.

Direction of flow

Direction of flow

69

obtain fluid flow and consequently be at higher risk of seal rupture and other pressure-related

failure. More importantly than this, it is imperative the carrier phase oil is chemically stable,

inert and non-miscible with the mainly aqueous dispersed droplet phase. In addition, the

carrier oil should not be a good solvent for the materials present in the droplets. Failure to

observe this would result in significant partitioning of materials between the droplet and

carrier phase, resulting in uncertainty of quantification.

Table 6. Carrier phase oils reported in droplet microfluidic systems.

Oil References Chemical Description

HFE7500 93 Fluorinated alkane

FC-40 186 Fluorinated tertiary amine

FC-70 187 Fluorinated tertiary amine

FC-77 188 Cyclic fluorocarbon (contains

oxygen heteroatom)

FC-3283 188 Fluorinated tertiary amine

Perfluorodecalin 189 Cyclic fluorocarbon

White Mineral oil 128 Long-chain alkane blend

Vegetable oil 190,190 Complex triglycerides

In certain applications, such as LogP (partition co-efficient) or logD experiments where the

distribution co-efficient D is calculated by determining the ratio of a molecule in aqueous and

organic phases, partitioning in microfluidic systems can be used to an advantage. The lower

compound volumes and a greater throughput offered is desirable over traditional large-

volume, low-throughput shake-flask methods250.

In the majority of droplet microfluidic systems reported, fluorinated oils are used as the

carrier phase due to being largely un-reactive, able to transfer gases to cells in encapsulation

systems and poor solvents for many molecules103,191,192. However, there are examples where

70

triglycerides such as vegetable oil or mineral oil are used119,128,193. Typical oils are

summarised in Table 6.

1.5.7 Droplet Sorting - Hydrodynamic Sorting

In the simplest case, droplets can be sorted passively based upon their size. By designing

bifurcating channel geometries with differing widths, droplets within a size range can be

entrained preferentially into a specific channel dependant on the shear force and flow

resistance. This is known as the Zweifach-Fung effect, and has been described by Yang et

al.151 to achieve separation of blood cells and plasma where the pinched flow fractionation

acts differently on blood cells forcing them into a new streamline. In a recent review, Biral et

al.194 investigated how purely hydrodynamic functionality could be employed within

microfluidic systems to achieve precise control and direction of droplets.

1.5.8 Droplet Sorting - Dielectrophoretic Sorting

Inclusion of embedded isolated electrodes within microfluidic devices provides a means of

creating non-uniform electric fields across droplet and suspended particle/cell flows. By

changing the field strength, differing vector forces can be applied to droplets directing them

to different regions of a chip or different channels, even if the hydrodynamic resistance of the

channels is higher as demonstrated by Ahn et al.195. In a similar way, a surface charge can

be imparted onto droplets flowing past an electrode. In this way, droplets can be attracted or

repelled from opposite or same charge respectively, allowing sorting of droplets or particles

into other channels196. Dielectrophoresis has been used for the separation of cells197.

71

1.5.9 Droplet Sorting - Magnetic Sorting

Nanoparticles with superparamagnetic magnetite cores have been used successfully to sort

droplets into different channels by the targeted application of perpendicular electromagnetic

fields. The use of such small magnetic particles avoids magnetic ‘memory’ effects and

reduces the possibility of particle aggregation198. Such approaches have been applied to

DNA purification199, separation of distinct droplet populations198,200 and as a tool to enable

the investigation of drug-protein interactions201.

1.5.10 Droplet Sorting - Optical Sorting (Laser Tweezers)

Focussed laser light can be used to generate localised thermocapillary currents within a

channel geometry, such that droplets can be entrained to follow particular routes174.

However, the relatively high power density of focussed laser light, suggests the technique

may not be suitable for materials within droplets that are thermally liable, or liable to

photolysis.

72

1.5.11 Droplet Sorting - Electrowetting-On-Dielectric (EWOD)

The key principle behind this technique is that the apparent surface tension of an electrically

conducting liquid in contact with a solid is altered by an applied electric field. This

relationship was first derived by Lippmann, and it has since been shown by Berge that the

Lippmann relationship results in a change of contact angle with the applied voltage202. This is

described by the Berge-Lippmann-Young (BLY) Equation (Equation (23)) and depicted in

Figure 24.

cosθ= cosθ0+ C

2γLGV2 (23)

BLY equation, where θ and θ0 are the actuated and Young contact angles, C the capacitance of the substrate,

γLG the liquid-air surface tension and V the electric potential. The contact angle decreases as the electric field

is applied as depicted in Figure 24.

Figure 24. Change of contact angle as a result of an applied electric field. Contact angle θ decreases with

increasing electric field (b).

The EWOD technique allows very specific fluid movement across substrate materials and is a

robust way to develop programmable digital microfluidics for cell manipulation203 and has

been used in DNA reaction204. However, the process described above requires the fluid to

have good conductivity. As many biological assays are conducted in ionic buffers17, it can be

assumed there is sufficient conductivity.

θ(a)

Ground ElectrodeElectrode (no voltage)

θ(b)

Electrode (voltage applied)

73

1.5.12 Geometry-Mediated Passive Droplet Fusion

Dependent on the stability of droplets within the microfluidics system (whether they have

been stabilised through the use of surfactants) droplets can be fused passively by draining

carrier phase fluid from between contiguous droplets. This may be achieved by modifying

the channel geometry at a specific point such that the flow rate is reduced and droplets fuse at

the point they meet from disturbances in the surface tension205,206. In order for geometrically

constrained devices to work, the droplet spacing (frequency of generation) is critical to ensure

droplets will come into contact in the fusion zone. If droplets have been sufficiently stabilised

by the use of a surfactant, it may not be possible to achieve passive fusion in this manner and

such techniques as electrofusion may be required to sufficiently disrupt the droplet interface

surface tension.

1.5.13 Electrofusion

Originally applied to research involving the fusion of plant protoplasts207, electrofusion or

electro-coalescence208, has been used successfully in several reports to accomplish droplet or

vesicle fusion209.

Figure 25. (a) Schematic of an oil drain concept, where oil is extracted from the carrier flow while channel

geometry prevents loss of the dispersed phase via the drain channels. Droplets may auto-coalesce on contact, or

through the use of electric fields to induce electrofusion. (b) An expanded channel region will slow linear flow

rate, resulting in closer droplet-droplet proximity. An electric field applied across the region can then de-

stabilise the droplet interfaces and promote electro-fusion.

Electric f ield

Electrodes

+ve- ve

(a) (b)

74

The technique takes advantage of embedded electrodes within the microfluidic device to

which an electric potential is applied. As most devices are very small, the actual voltages

need not be very high (in the order of fifty volts to a few hundred volts) to achieve the high

electric field density required to induce droplet fusion.

As with the passive method, this approach requires droplets to be brought into close

proximity, either by draining carrier fluid from between consecutive droplets (Figure 25a), or

by using an expanding region to modify the relative linear flow rates such that droplets are

brought into close contact (Figure 25b).

1.5.14 Droplet Fission

Droplets can be controlled within microfluidic devices by passive or active means. The

simplest control is to change the velocity of the carrier fluid, although where droplets are

produced by shear force, a change in velocity will effect a change in the droplet size, which

may be undesirable.

Channel geometry can be modified to alter the movement of droplets. For example, the

geometry can be increased, which, to conserve volumetric flow rate, will imply a reduction in

the linear velocity of the droplet stream, thus enabling on-chip residence times to be adjusted.

This may be useful for droplet incubation, or where reactions having slow kinetics are to be

studied.

Bifurcating junction design is an approach adopted for splitting one droplet into two smaller

droplets210,211. For this process to work correctly, the droplet diameter must be carefully

matched to the bifurcating junction geometry, such that the droplet entering the junction will

reach a point where the length of the droplet exceeds the initial circumference, resulting in

droplet fission (Figure 26). If the initial droplet is smaller than the channel, then this droplet

75

will not split, but rather, be randomly distributed into one or other output channels of the

bifurcating junction. An asymmetric design can be used to produce droplet fission where one

droplet has a different volume.

Cacr= ∝ε0 (1

ε02/3 - 1)

2

(24)

Where; 𝐶𝑎𝑐𝑟 isd the critical capillary number; ∝ is a dimensionless constant of the viscosity difference

between the two fluids and channel geometry; 𝜀0 is the ratio of the initial droplet length to

circumference.

Figure 26. Droplet fission at a bifurcating junction. If the droplet size is correct, relative to the channel cross

section, the droplet will be forced to cleave into two daughter droplets in both output channels. If the droplet is

smaller than the channel cross section, it will travel either to the left or right channel at random.

As there is a precise requirement of channel size to ensure droplet fission, this technique may

not work under all conditions of flow rate, solvent composition and viscosity. Equation 24

describes the relationship between droplet dimension (due to channel size) and critical

capillary number.

1.6 Partitioning and Droplet Surface Interactions

1.6.1 Partitioning in Drug Discovery

Candidate drugs are profiled to have a balance between good aqueous solubility (e.g. for

effective dosing formulation) and good bioavailability (for uptake to target tissues, etc.). In

76

immiscible biphasic solvent systems, substances may tend to partition between the phases,

with an equilibrium point reached dependant on the physical chemistry of the solvent and

solute. LogP, or more usually by measurement at a specific pH, logD, is a key parameter

used in drug design. In biological drug design, logD values are usually quoted at

physiological pH (pH 7.4) and using a well characterised shake-flask method using octanol as

the counter-phase solvent212. The process is illustrated by Figure 27 and described by

Equation 25.

D=[aq.t0]-[aq.t30]

[aq.t30].

Vaq.

Voil (25)

Calculation of D, the diffusion constant, where [aqt0] is the concentration in the

aqueous phase before partitioning and [aqt30] after 30 minutes. Vaq. and Voil are the

volumes of aqueous and oil phase, respectively.

Figure 27. Shake flask determination of logD.

1.6.2 Partitioning and Microfluidics

When moving from 96-, to 384- and then 1536-well microtitre plate formats, we see an

increase in the surface area to volume ratio. As the assay volume decreases further in droplet

microfluidics, this ratio increases significantly as droplet size decreases. The impact this may

Solubilise

compound, pH 7.4 buffer

i) Mix (30 mins)

ii) Separate

Add octanol

77

have on microfluidic systems can become quite profound, where physical chemistry

mechanisms such as partitioning and interfacial surface chemistry may become increasingly

dominant in the dynamics of the system. Table 7 highlights the ratio seen with different

droplet volumes, comparing 96-, 384- and 1536-well microtitre plates to droplet formats.

As the droplet surface area increases more rapidly with decreasing droplet volume,

partitioning of materials from the droplet may be expected to increase.

Table 7. Microtitre plate surface area to volume ratio comparison

Format Approx. surface area-to-volume ratio

96 well microtitre plate (well) 0.7:1

384-well microtitre plate (well) 1.2:1

1536-well microtitre plate (well) 3:1

300 µm diameter droplet (14 nL) 20:1

150 µm diameter droplet (1.8 nL) 40:1

50 µm diameter droplet (0.5 nL) 60:1

1.6.3 Proteins at Liquid Interfaces

As the surface area-to-volume ratio increases in droplet microfluidics, the potential for

interfacial surface interactions may also be expected to increase. As Holtze et al.177 reported,

when requiring dispersed phase stability where droplets do not auto-coalesce upon collision,

it is necessary to use biocompatible surfactants that do not adversely affect biological

reagents contained within the droplets. The conformation change of proteins and peptides at

interfaces has been previously studied and is well known213-215; such changes in a droplet

microfluidic system may radically change the nature of any reactions taking place. The

potential for this artefact to affect the P450 enzyme inhibition assay described in this project

is considered in later chapters.

78

2 PROJECT AIMS

2.1 Objectives

Miniaturise an existing in vitro cytochrome P450 inhibition assay used in drug

discovery to a droplet microfluidic format. This assay generates multi-point IC50 data

for small molecule (150 to 700 Da mass) candidate drugs entities for a specific human

P450 isoform, 1A2, as described in Chapter 1.

o Design and fabricate a novel chip to enable the production of droplets suitable

for the miniaturisation of the cytochrome P450 inhibition assay.

o Design and fabricate a suitable analytical device for the detection of emission

light from fluorogenic substrates used in the chip-based cytochrome P450

enzyme inhibition assay.

Investigate a range of oils for use in biphasic droplet microfluidics and select the most

appropriate based on physical properties and the ability to readily form and sustain

droplets under the required flow conditions. Include tests to investigate the impact of

surfactants on droplet formation and synthesise a large molecule non-ionic surfactant

and compare to the behaviour seen when using a commercially available small-

molecule ionic surfactant.

Investigate reagent migration from aqueous to oil phase across a planar interface and

compare to in situ droplet-based measurements.

Following optimisation of the oil type in respect of ideal droplet formation and

stabilisation, design and conduct experiments to determine the extent of reagent

migration observed for a wider range of compound chemistry provided via the AZ

compound collection library using mass spectrometry to quantify compound

concentrations at known time-points. Attempt to build a model that can be used to

79

predict the potential for partitioning of reagent/drug from the aqueous droplet to

carrier oil phase.

Observe and investigate effects seen that may arise directly from the miniaturisation

of the assay system, such as the impact of droplet surface-area-to-volume ratio and

interfacial artefacts. Interfacial effects in biphasic systems are well known, but the

specifics of these may be particular to the microfluidic system concerned.

80

3 EXPERIMENTAL

3.1 Chemicals and Reagents

Table 8a summarises the reagents and chemicals tested throughout the project. For

Intellectual Property protection purposes, the compounds from the AstraZeneca compound

collection library tested in the extended partitioning tests cannot be disclosed and are referred

to numerically where applicable.

3.1.1 Preparation of Phosphate Buffer Solution

The following method was used for all experiments involved with enzyme inhibition. As

shown in previous studies17,27-29, phosphate buffers of this type are typical for biochemical

assays. In addition, this buffer was used for all tests investigating partitioning between

aqueous and oil phases, to help ensure no drift in spectrophotometric emission profile was

related to changes in pH.

1. Disodium hydrogen phosphate (35.9 g) was added to reverse osmosis (RO) water

(1 litre) and stirred thoroughly until dissolved. This was labelled ‘Solution A’.

2. Potassium dihydrogen phosphate (6.8 g) was added to RO water (0.5 L) and stirred

thoroughly until dissolved. This was labelled ‘Solution B’.

3. Solution B was added to Solution A whilst stirring until pH 7.4 was reached,

measured using a 3-point (pH 4, 7 & 10) calibrated electronic pH meter (Fisher

Scientific, Waltham, MA).

The final solution is referred to as ‘phosphate buffer’ (PB) throughout this project.

81

Table 8a. Summary of chemicals and reagents. All materials are of reagent grade quality having >95% purity.

Chemical/Biochemical Reagent Description / use Thesis Page ref. Supplier Supplier ref.

olive oil triglyceride/carrier oil Tesco n/a

octanol alcohol/carrier oil Sigma–Aldrich 472328-100ML-D

dodecane alkane/carrier oil Sigma–Aldrich D221104-100ML-D

polydimethylsiloxane (DC200) siloxane/carrier oil Sigma–Aldrich 378372-250ML

hexadecafluoro-1,3-dimethylcyclohexane fluorinated cycloalkane/carrier oil Sigma–Aldrich 282316

perfluorodecalin fluorinated heterocyclic alkane/carrier oil Fluorochem 003283

perfluoroperhydrophenanthrene fluorinated heterocyclic alkane/carrier oil Fluorochem 007152

FC-40 fluorinated tertiary amine/carrier oil 3M Free sample

FC-70 fluorinated tertiary amine/carrier oil Apollo Scientific PC3312G

NADPH enzyme co-factor/inhibition assay Sigma–Aldrich N7505-100MG

cytochrome P450 enzyme 1A2 enzyme Invitrogen (Life Technologies) P2792

disodium hydrogen phosphate buffer/inhibition assay & partitioning Sigma–Aldrich S9390-500G-D

potassium dihydrogen phosphate buffer/inhibition assay & partitioning Sigma–Aldrich 795488

3-cyano-7-ethoxycoumarin fluorogenic substrate/inhibition assay Invitrogen (Life Technologies) C684

fluvoxamine 1A2-specific inhibitor/inhibition assay Sigma–Aldrich F2802-10MG

acetonitrile solvent/dissolution of fluorogenic substrate Sigma–Aldrich 271004

dimethylformamide

di

solvent/fluorosurfactactant preparation Sigma–Aldrich 227056

dichloromethane

solvent/fluorosurfactactant preparation Sigma–Aldrich 676853

thionyl chloride

reagent/fluorosurfactactant preparation Sigma–Aldrich 230464

toluene solvent/fluorosurfactactant preparation Sigma–Aldrich 244511

tetrahydrofuran solvent/fluorosurfactactant preparation Sigma–Aldrich 401757

triphenylphosphine reagent/fluorosurfactactant preparation Sigma–Aldrich 93092

oxalyl dichloride reagent/fluorosurfactactant preparation Sigma–Aldrich O8801

ethyl acetate solvent/fluorosurfactactant preparation Sigma–Aldrich 270989

polystyrene-dimethylaminopyridine reagent/fluorosurfactactant preparation Sigma–Aldrich 359882

phosphorus pentoxide drying agent/fluorosurfactactant preparation Sigma–Aldrich 298220

KRYTOX 143FSH Surfactant precursor/fluorosurfactactant

preparation

GBR Technology Ltd n/a

82

Table 8b. Summary of chemicals tested in partitioning studies.

Chemical name λmax Mass (Da)* pKa† logD‡ Acid/Base/Neutral Supplier Supplier ref.

imipramine 251 280.41 9.4 2.49 base Sigma–Aldrich I7379

trazodone 310 407.13 7.1 2.54 base Sigma–Aldrich T6154

chlorpromazine 307 318.86 9.3 3.36 base Sigma–Aldrich C8138

lidocaine 223* 234.34 8.0 1.63 base Sigma–Aldrich L7757

3,5-dichlorophenol 280 163.00 7.9 3.58 neutral Sigma–Aldrich D70600

tolbutamide 307 270.35 5.2 0.43 acid Sigma–Aldrich T0891

indomethacin 319 357.79 4.5 0.95 acid Sigma–Aldrich I7378

salicylic acid 296 138.12 3.0 -1.43 acid Sigma–Aldrich 247588

AZ compounds not specified 200-700 Not specified not specified acid/neutral/base AstraZeneca n/a

* Data obtained from Sigma–Aldrich material safety data sheets (MSDS) or AstraZeneca internal databases. † Data obtained from http://www.DrugBank.ca/drugs and/or AstraZeneca internal databases. ‡ Data obtained from http://www.DrugBank.ca/drugs and/or AstraZeneca internal databases.

83

3.1.2 Preparation of NADPH Solution

The procedure followed for the P450 enzyme reaction in both microtitre plate and chip based

experiments used a 1:1 ratio of NADPH solution to enzyme–substrate (pg. 102), and a final

incubate NADPH concentration of 250 µM was required. Fresh NADPH working solutions

in phosphate buffer (500 µM) were made for each experiment and kept on ice until required.

3.1.3 Preparation of Cytochrome P450 Enzyme Solution

1A2 enzyme was supplied by Life Technologies (ThermoFisher, Waltham, MA) as a stock at

1 µM concentration. The procedure for the enzyme inhibition reaction details the

requirement for a final incubate enzyme concentration of 10 nM, thus with the 1:1 addition of

NADPH co-factor, the enzyme working solution was 999made up to 20 nM in phosphate

buffer and kept on ice for up to 4 hours until use. The volume of enzyme solution required for

any given test was spiked with the appropriate stock substrate solution prior to use.

3.1.4 Preparation of CEC Substrate Solution

Stock solution was prepared by dissolving CEC in acetonitrile to 2 mM and ensuring

thorough dissolution by vortex mixing rapidly for 20 seconds.

84

3.2 Apparatus

3.2.1 Chip Designs

Four chip designs were conceived for the purpose of generating stable droplets having

diameters of approximately 200–250 µm diameter. Each of the designs produced were

inspired by previous work118,119,135,216 and adapted to the specific purposes outlined in this

work. Chip designs were drawn using either Inventor versions 9 or 2009 (Autodesk)

whereby 2D sketches were extruded to yield 3D features of various depths and widths

(typically 100 µm to 1 mm width by up to 300 µm depth). The features were cut after CAD

to CAM conversion using EdgeCam (Pathtrace, Reading, UK) using a variety of end mills

having flute diameters ranging from 100 µm to 500 µm .

3.2.2 Chip Fabrication

All CAD to CAM conversions were performed using EdgeCam software. This was used in

conjunction with a macro supplied by the University of Manchester (Manchester, UK) to

convert 3D sketches to the required machine code format required to operate a M35 CNC

milling machine (Datron Dynamics Inc., NH). Figure 28 depicts the CNC machine used.

Clear general purpose grade polymethylmethacrylate (PMMA) (Sigma–Aldrich, UK) was

chosen for the chip material as this material was found to machine well by CNC (resulted in

clean cuts) and has been reported to have good overall chemical resistance217. All end mills

were purchased from Datron.

Aqueous dispersed phase channels were cut using 100 µm diameter double-flute drill bits

(#0068010), all other channels were cut using either 200 µm (#00680020) or 300 µm

85

(#0068003) diameter double-flute drill bits. In the case of 100 µm, 200 µm and 300 µm

mills, maximum cutting depth was roughly equivalent to the flute diameter. For larger holes

cut with a 500 µm bit (double-flute, #0068605) the maximum hole depth was 3 mm (milling

bit had an extended flute length). Fluid connection holes were drilled using 500 µm drill bits

of the longer 3 mm flute type enabling through–holes to be cut.

Fluid connection holes were drilled using a 500 µm diameter end mill and the channel-side

surface of the chip roughened using a ‘Very Fine’ grade (grit size 400) silicon carbide

abrasive paper (797-5960, RS Components, UK) to remove minor blemishes and to provide a

roughened surface for the chip seal to bind to.

Figure 28. Datron M35 CNC milling machine used to produce microfluidic chip devices.

To minimise the impact of extraneous light interference, each chip was covered with black

card on the reverse side, and also on the top side, either side of the detection channel, using

double-sided tape. In addition, all fluorescence measurements were made in subdued

ambient lighting to further limit the extraneous signal noise.

86

Figure 29. Microfluidic chips used in the project; (a) T-junction – for limit of detection and partitioning tests.

Oil channel contained a constriction of 200 µm width at the point where a 100 µm width by 100 µm depth

channel for the dispersed phase entered. Oil channel was cut to 200 µm depth; (b) Spiral chip for partitioning

tests and P450 inhibition experiments. The aqueous inlet channels (centre of spiral) were 100 µm wide x 100

µm deep. The oil channel (spiral) and oil spacer channels were 300 µm wide by 300 µm deep x 2m long; (c)

Serpentine chip for P450 enzyme inhibition experiments, and (d) dual dispersed phase input for testing

electrofusion of droplets. In each case, fluid connections were made by drilling holes of 1.3 mm diameter (not

shown in (a) or (c)) into which uninsulated bootlace ferrules (Figure 11) were push-fitted.

(a)

(b)

(c)

(d)

200 µm

wide x 250

µm deep.

200 µm wide x 250 µm deep

100 µm wide

x 100 µm

deep

11 mm

2.7 mm

700 µm wide x 250 µm deep

100 µm wide by 100 µm deep

300 µm wide by 300 µm deep

‘Spiral Chip’

‘Serpentine Chip’

‘T-Junction Chip’

‘Dual Input T-Junction Chip’

Direction of flow

Direction of flow

Direction of flow

Direction of flow

87

Microfluidic devices produced for the project were as follows:

A T-junction chip ('T-junction chip’) for testing the fluorescence detection apparatus

setup, limit of P450 substrate detection and off-chip absorbance measurements for

droplet-based partitioning determinations (Figure 29a).

A modified T-junction chip having two confluent dispersed phase (aqueous) inlets

arranged as a 'Y' with on-chip incubation to assess the preferred mode of NADPH

addition to enzyme substrate. Serpentine ('Serpentine chip') and spiral ('Spiral chip')

formats (Figure 29b & c) were designed to provide 13.5 and 6 minute on-chip

residence times. A dual input T-junction design using opposing inlets (Figure 29d)

was tested to consider the feasibility of fusing NADPH and enzyme-substrate

droplets for reaction initiation.

Chip optimisation was achieved by trial and error with a number of intermediate

geometries tested at different ratios of aqueous dispersed and fluorocarbon carrier oil

phase until the required droplet diameter of 200–300 µm and droplet residence time of up

to 15 minutes for the Spiral chip (Figure 29b) was obtained. The same iterative approach

was applied for all other chips used throughout this project. Initial channel geometry

was guided by literature examples117-120,135,193,216,218.

3.2.3 Chip Sealing

Three methods of chip sealing were assessed. The first method involved the use of UV-

curable epoxy resin applied in a thin layer to glass microscope slide covers, which were then

applied directly to the chip and cured using a 5000-EC 400W mercury vapour 365 nm UV

lamp system (Dymax Corp., Torrington, CT) for 30–40 seconds (Figure 30). The layer of

88

adhesive was prepared by first placing a small amount of Norland ‘NOA68’ UV curable

optical epoxy adhesive (Norland Inc., Cranbury, NJ) between two sheets of acetate film and

rolling this out between the two sheets as much as possible. The top acetate sheet was

replaced with a fresh one and the process repeated. This was then repeated a third time

before placing one of the final acetate sheets directly onto the glass chip seal and carefully

rolling flush to remove air. The seal was then placed carefully on the chip, carefully rolled

and then subjected to the UV lamp for 30 seconds. Although this provided a very robust seal

able to withstand fairly high back-pressures, after a few days’ use, the chip and subsequently

glass cracked, presumably a result of thermal expansion or contraction differences and/or

plastic swelling due to solvent absorption. This method was not pursued further.

Figure 30. Dymax 365 nm 400W UV curing lamp system for curing UV curable epoxy resin.

Method two, evolving from the first, replaced the glass with overhead projector acetate sheet,

following the same construction technique. This approach proved much more reliable over

time, but an even adhesive coating over the entire seal could not be obtained reliably.

Furthermore, it was found that small features were prone to being blocked by adhesive if too

much was applied. This was evident from adhesive spilling into the channels when observed

under a microscope. The correct amount of adhesive was determined by trial and error.

89

Method three was preferred for all future work, being quick and simple to apply and crucially

having good reliability. Sections of self-adhesive optically clear microtitre plate seals

(#6575, Corning, Tewkesbury, NA) were applied directly to the finished chip. The binding

side on the seals formed a firm attachment to the chip after at least 2 hours’ post-application.

This condition was ascertained by the observation of the seal becoming transparent as if

having ‘wetted’ fully into the keyed surface of the chip surrounding the channels. The

binding side of the seal directly above the chip channels did not appear to adversely affect

droplet formation or fluorescent detection.

3.2.4 Fluid Delivery

All microfluidic devices were compression push-fitted with 1.3 mm diameter uninsulated

bootlace ferrules (#211-4252, RS Components, Corby, UK). Short sections of silicone tubing

(7mm long) of 1mm internal diameter (#VERN760070, VWR, UK) were used to join PTFE

tubing of 0.5 mm internal diameter (#DENE3400501, VWR, UK) to the ferrules for fluid

delivery. Harvard PHD2000 syringe pumps (Harvard Apparatus, MA) were used for all

reagent delivery, using 10 mL and 1 mL glass Gastight® syringes (Hamilton, Bonaduz,

Switzerland). Glass syringes were considered to have a smoother piston action for low speed

operation than plastic syringes.

90

3.2.5 Analytic Apparatus for Fluorescent Detection

An aluminium optical breadboard (PGB52522, Thorlabs, Ely, UK) was used to assemble a

custom built laser diode-dichroic mirror-photomultiplier detector. Microfluidic chips were

supported on a X-Y twin-axis translation stage (20XT65, Comar Optics, UK), to enable

accurate positioning of the detection channel beneath the optics.

The detection apparatus (Figure 31, left) consisted of a 10× microscope objective lens to

focus light from a 3 mW 405 nm solid state laser (Laser Components, UK) into the detection

channel and to then focus the emission light back through a dichroic mirror (Long Pass cut

off 425 nm, Laser Components, UK) to a H10722-01 photomultiplier tube (PMT)

(Hamamatsu Photonics Ltd., Herts., UK). The laser diode-PMT apparatus and microfluidic

chip were both mounted to the breadboard using translation stages (Thorlabs, Cambridge,

UK) providing four degrees of movement; X, Y and Z and additionally a rotation module for

the diode-PMT assembly to allow fine adjustment of the excitation/emission light position

(Figure 32).

Figure 31. Schematic of laser diode, dichroic mirror block, PMT module and Pico data logger used for

acquiring fluorescence data from the droplets. Photomultiplier from Hamamatsu (H10722-01); 405nm

Flexpoint 405nm laser from Laser Components, UK; custom-built dichroic mirror block to house 25mm long-

pass dichroic mirror from Comar Optics (420IY25). Olympus 10× microscope lens was used to focus light in

the chip channel to a spot approx. 1mm2. ADC-11 (Pico Technology, UK) was used to convert the voltage

output from the PMT to a digital signal captured by universal serial bus data transfer (USB).

Microscope objective lens

Photomultiplier module

Band-pass filter

Dichroic mirror block

Droplet

405 nm

laser

91

Excitation light beam was estimated to be approximately 0.5–1.0 mm diameter when

compared to the known channel width sections (300 µm) at sharp focus. This was judged by

moving the z-axis up and down to find the point at which the light beam was at its smallest

diameter, and thus deemed in focus.

Figure 32. General arrangement of laser diode, dichroic mirror block, and PMT module and microfluidic chip

mounted to a breadboard. The heater was formed from an aluminium block fitted with a self-adhesive heater

pad. 4 degrees of movement (x, y, z and rotate) via the translation stages provided accurate channel alignment

and light focussing.

The x and y translation stages were then adjusted to move the beam to the required detection

spot on the chip channel.

A Pico analogue-to-digital 12-bit data logger (Pico Technology, St. Neots, Cambridgeshire,

UK) was used to capture the output signal from the PMT (Figure 31, right).

Channel geometry was determined by iterative chip design-make-test cycles for all chip

designs used throughout the project. All designs incorporated a suitable region towards the

exit fluid connections where either a camera lens assembly was focused to enable capture of

bright-field images of droplets, or the laser-PMT assembly used to capture fluorescence.

Microfluidic chip

Heater block

PMT

405 nm laser

Dichroic

mirror block

92

3.2.6 Fluorescence Intensity Measurements

The target analyte for the project was the fluorescent metabolite 3-cyano-7-hydroxycoumarin

(CHC), produced by the oxidative metabolism of 3-cyano-7-ethoxycoumarin (CEC) by the

1A2 P450 cytochrome enzyme used in this project. CEC is an enzyme specific fluorogenic

substrate used in existing conventional microtitre plate drug-drug interaction studies2,28

having an absorbance maximum at 408 nm and fluorescence emission at 460 nm.

3.2.7 Direct Observation

Alternatively, visual observations (image snapshots or video clips) were aided through the

use of a high speed monochrome CMOS camera (#LU075, Lumenera, Ontario, Canada)

fitted with a fixed focal length C-mount lens. Alternatively, a colour USB 400x digital

microscope (Veho, Hampshire, UK) was used to provide higher magnification images at

lower frame rates.

3.2.8 High Voltage Power Supply

An electrophoresis high voltage power supply (Consort bvba., Turnhout, Belgium) was used

to provide the required 2–3 kV to electro-fuse droplets collected in the off-chip partitioning

experiments. Briefly, droplet emulsion samples were collected in a 0.5 mL Eppendorf tube

(Eppendorf, Hauppauge, NY). The tube was surrounded by aluminium foil connected to one

HV terminal, whilst an insulated probe connected to the opposite HV terminal was used to

fuse the droplet emulsion collected in the vessel. An electric field gradient was thus formed,

sufficient enough to disrupt the surfactant-stabilised droplets and effect electro-fusion.

93

3.3 Methods

3.3.1 Procedure for Oil Testing, Droplet Formation and Linearity

The range of oils detailed in Table 9 were tested in the T-junction chip shown in Figure 29a

to determine which would best promote droplet formation and whether these could be

considered suitable for use throughout the whole project in respect of droplet formation. For

each oil, flow rates of the oil and aqueous phase were incrementally increased using an

iterative approach until stable droplet formation was achieved. As described previously (pg.

46), droplet formation and droplet size is affected by the absolute and relative flow rates of

the oil carrier and dispersed phases. In all cases, phosphate buffer was the aqueous dispersed

phase for droplet production. This was selected based on the intended use of the buffer in

chip-based biochemical P450 enzyme reactions later on in the project (pg. 106).

Table 9. Oils tested in the droplet formation & stability tests.

Carrier Oil Chemistry

olive oil triglyceride

silicone oil polydimethylsiloxane

octanol alcohol

dodecane straight-chain alkane

hexadecafluorodimethylcyclohexane fluorinated cycloalkane

perfluorodecalin fluorinated polycyclic alkane

perfluoroperhydrophenanthrene fluorinated polycyclic alkane

FC-40 fluorinated tertiary amine

FC-70 fluorinated tertiary amine

FC-40 with PFO (10% v/v)

FC-70 with PFO (10% v/v)

94

Two methods were applied to assess droplet formation reproducibility; the first relied upon

recording attempted droplet formation using a USB high speed camera (Lumenera, Ontario,

Canada) and analysing individual frames. The images were imported to Adobe Premier Pro

(Adobe, CA) to extract individual frames which were then analysed in ImageJ (NIH,

Bethesda, MD). By scaling against the known geometry of the channel, the diameter and

circumference of 100 consecutive droplets was estimated and the variance calculated.

In the second method, the laser-PMT assembly (Figure 32, pg. 91) was used to obtain

fluorescent data from droplets containing CHC solution over a concentration range from 0.05

to 1 µM. The collected data was plotted in Origin 7.5 (OriginLab, Northampton, MA) and

Excel™ (Microsoft, Redmond, WA), droplet signal intensity identified and the average signal

and standard deviation calculated. Built-in peak baseline detection and integration algorithms

available in Origin were used to calculate peak width, height and area data.

Peak height was recorded for proportionality and linearity with respect to concentration,

whilst peak width was used to provide a measure of droplet diameter calculated from the

linear flow rate and time domain spanning the width of each peak. This approach assumed a

constant flow rate and correct alignment of droplets through the detection zone.

The aqueous flow was adjusted over the range 2 to 6 µL per min and oil flow rate over 5 to

20 µL and both incrementally increased until either a stable droplet stream was produced, or

wetting out of the aqueous phase occurred.

As discussed previously, fluorocarbon oils may require a surfactant to yield stable

droplets62,175,177 and thus these oils were also tested in the T-Junction chip using the

admixture of perfluorooctanol (10 % v/v) to the fluorous oil phase.

95

3.3.2 Synthesis and Characterisation of Fluorosurfactant

Surfactants are often necessary to enable stable droplet formation by reduction of the

interfacial surface tension between dispersed and carrier phase and/or provide additional

‘shielding’ of the droplet contents from undesirable interaction with the droplet interface or

oil. As reported by Holtze et al.177, non-ionic capped fluorosurfactants added to fluorocarbon

carrier oils were the most biocompatible in respect to controlling the degree of protein

adsorption at the droplet interface. A similar non-ionic fluorosurfactant was prepared and

tested to investigate this in the context of droplet P450 enzyme inhibition assays attempted in

this project.

The following procedure were developed to synthesize a complex perfluoropolyether

fluorosurfactant:

Step 1: Conversion of PEG 600 to 1-chloro-2-(2-chloropolyethoxy)ethane

(R1)

Figure 33. 2,2-oxydiethanol PEG 600 conversion to 1-chloro-2-(2-chloropolyethoxy)ethane.

Reaction R1 (Figure 33): 2,2'-oxydiethanol PEG 600 (60 g, 100.00 mmol) was heated at

80°C under vacuum for 1 hour to degas and remove moisture. The oil was cooled under

nitrogen, and thionyl chloride (21 mL, 287.72 mmol) added slowly at 20°C. The mixture was

heated to 50 °C for 18 hours. Reaction completion was determined by infrared spectroscopy,

observing disappearance of the O-H stretch at 3500 cm–1 and formation of C-Cl stretch at 730

cm–1 (data not shown). The intermediate was diluted with DMF and evaporated. This was

repeated three times to remove all traces of thionyl chloride, to afford 1-chloro-2-(2-

OOH OH

n OCl Cl

n

thionyl chloride

96

chloropolyethoxy) ethane (65.7 g, 103 mmol, 103 %) as a pale yellow oil and confirmed by

mass spectrometry (Figure 34).

Figure 34. Mass Spectrometry analysis of 1-chloro-2-(2-chloropolyethoxy) ethane.

Step 2:

(R2)

Figure 35. 1-chloro-2-(2-chloropolyethoxy) ethane conversion to 1-azido-2-(2-azidopolyethoxy) ethane.

Reaction R2 (Figure 35): 1-chloro-2-(2-chloropolyethoxy) ethane (65.7 g, 103.30 mmol) was

dissolved in dimethylformamide (DMF) (500 mL), and sodium azide (19g, 292.26 mmol)

added. The mixture was stirred at 85 °C for 18 hours under nitrogen and then evaporated.

Dichloromethane (DCM) (400ml) was added and the inorganic constituents removed by

ON3N3

nOCl Cl

n

sodium azide

97

filtration. The filtrate was evaporated to afford 1-azido-2-(2-azidopolyethoxy) ethane (61.85

g, 95 mmol, 92 %) as a brown oil (Figure 36).

IR confirmed the formation of the di-azide by the appearance of a sharp azide stretch at 2100

cm–1 and the disappearance of the C-Cl stretch at 730 cm–1 (data not shown).

Figure 36. Mass spectrometry analysis of 1-azido-2-(2-azidopolyethoxy) ethane.

Step 3:

(R3)

Figure 37. Conversion of 1-azido-2-(2-azidopolyethoxy) ethane to 2,2'-polyoxydiethanamine.

ON3N3

n ONH

2NH

2n

triphenylphosphine, water

98

Reaction R3 (Figure 37): 1-azido-2-(2-azidoethoxy) ethane (61.8 g, 95.08 mmol) was

dissolved in tetrahydrofuran (THF) (600 mL), triphenylphosphine (60 g, 228.76 mmol) added

and stirred at 25 °C for 4 hours. Water (8 mL) was then added and the solution stirred for 18

hours at 25 °C. This was evaporated to dryness and water (200ml) added to the residue. The

solid was removed by filtration and the filtrate washed three times with toluene (100ml). The

aqueous solution was evaporated to afford 2,2'-polyoxydiethanamine (50.2 g, 84 mmol, 88

%) as a brown oil which crystallized on prolonged drying.

Figure 38. Mass spectrometry analysis of 2,2'-polyoxydiethanamine.

IR analysis demonstrated the absence of an azide peak at 2100 cm–1 and peaks in the region

3300–3600 cm–1 for the NH groups (data not shown). Mass spectrometry confirmed

intermediate product formation (Figure 38).

99

Step 4:

(R4)

OO

F

F

FF F

F FF

F

F

FF

F

F

FF

F

OH

On

OO

F

F

FF F

F FF

F

F

FF F

F

FF

F

Cl

On

Figure 39. Conversion of KRYTOX 134FSH to 2,3,3,3-tetrafluoro-2-(1,1,2,3,3,3-hexafluoro-2-

(perfluoropropoxy)propoxy) propanoyl chloride.

Reaction R4 (Figure 39): Oxalyl dichloride (25 mL, 286.58 mmol) was added to a solution

of KRYTOX 134FSH (DuPont) (100 g, 20.78 mmol) in 1,1,2-trichloro-1,2,2-trifluoroethane

(250 mL) and stirred at reflux under a slow stream of nitrogen for 24 hours. This was

evaporated and the residue dried to afford 2,3,3,3-tetrafluoro-2-(1,1,2,3,3,3-hexafluoro-2-

(perfluoropropoxy)propoxy) propanoyl chloride (100 g, 100 %).

Step 5:

(R5)

Figure 40. Formation of product.

Reaction R5 (Figure 40): 2,3,3,3-tetrafluoro-2-(1,1,2,3,3,3-hexafluoro-2-

(perfluoropropoxy)propoxy) propanoyl chloride (100 g, 20.70 mmol), 2,2'-oxydiethanamine

(6.21 g, 10.35 mmol) and polystyrene supported 4-dimethylaminopyridine (PS-DMAP)

1.46mmol /g (25 g, 126.74 mmol) were charged to a 500ml flask and dried in a desiccator

over phosphorus pentoxide for two hours.

OO

F

F

FF F

F FF

F

F

FF F

F

FF

F

Cl

On O

NH2

NH2

n

ONHNH

OO

F

F

FF F

F FF

F

F

FF

F

F

FF

F

O OO

F

F

FFF

FFF

F

F

FF

F

F

FF

F

O

n

n

n

100

1,1,2-trichloro-1,2,2-trifluoroethane (200 mL) and (trifluoromethyl)benzene (100 mL) were

added and the mixture stirred at 60 °C under nitrogen for 24 hours. This was then cooled,

filtered and rinsed with 1:1 reaction solvents. The material was finally evaporated and

triturated with water then ethyl acetate. The thick oil was evaporated and dried to afford the

product (100 g, 95 %) as a pale cream, opaque viscous oil.

2,3,3,3-tetrafluoro-2-[1,1,2,3,3,3-hexafluoro-2-(1,1,2,2,3,3,3-heptafluoropropoxy)propoxy]-

N-[2-[2-[[2,3,3,3-tetrafluoro-2-[1,1,2,3,3,3-hexafluoro-2-(1,1,2,2,3,3,3-

heptafluoropropoxy)propoxy]propanoyl]amino]ethoxy]ethyl]propanamide is hereafter

referred to as ‘AZF’.

3.3.3 AZF Dissolution

The use of fluorosurfactants of the PFPE type has been reported at concentrations of

approximately 1–2% w/w62. AZF was first dissolved in FC-70 to 2% w/w using a positive

displacement pipette to carefully weigh the required amount in a small aluminium weighing

boat which was then added directly to about ¾ total required oil volume, whirly-mixed for 5

minutes followed by 5 minutes of sonication to ensure dissolution and then made up to the

correct volume. This yielded a clear to opaque solution which either contained non-dissolved

surfactant or possibly residual materials from incomplete purification. The solution was

passed through a 0.2 µm Luer-lock syringe filter which resulted in a clear and colourless

solution.

101

3.3.4 AZF Critical Micelle Concentration (CMC) by Dynamic Light

Scattering (DLS)

To determine the CMC of AZF using DLS, a series of dilutions of AZF in FC-70 were made

from a stock solution and analysed on a Brookhaven BI-200SM DLS instrument

(Brookhaven, Holtsville, NY) using a 90° scatter angle measurement. Briefly, serial 2-fold

dilutions of AZF fluorosurfactant were made from the filtered 2% w/w stock solution in FC-

70 described previously to the point where the signal-to-noise ratio deteriorated too much to

distinguish the sample. A plot of 90 ° scatter kilo counts per second against concentration

was then used to identify any deviation from linearity that could correspond to an aggregation

event or CMC point where a marked increase in larger micelles would be observed resulting

in a shift in the amount of scattered light.

3.3.5 Procedure for Microtitre Plate Cytochrome P450 Inhibition Assay

and Dependence on Reaction Temperature

The initial characterisation of the P450 enzyme reaction ('control activity') was performed

using a microtitre plate following the procedure outlined in Table 10. In this approach, a

working solution (50 µL) of 1A2 enzyme (20 nM) in phosphate buffer (0.1 M) containing

CEC substrate (3 µM) was pre-incubated in wells of a 384-well microtitre plate for 3 minutes

at a range of temperatures to determine the extent of useful linearity and max signal available.

After pre-incubation, a working solution of NADPH (500 µM) was dispensed using a multi-

channel pipette to ensure all reaction wells were started simultaneously. Measurement of

CHC formation was performed automatically using a kinetic-mode method to acquire data for

up to 20 minutes. This time was chosen based on the reported use of the 1A2 enzyme in

previous work2,17 and application notes from the enzyme supplier (Life Technologies, MA).

102

Table 10. Procedure followed to characterise the optimum temperature for enzyme reaction. Reagent

concentrations were based on literature information and manufacturer's application notes.

Step 1: Generate standard curve dilutions from stock CHC solutions

Step 2: Make up NADPH solution (500 µM) in phosphate buffer (0.1 M)

Step 3: Dissolve substrate in acetonitrile (2 mM)

Step 4: Dilute enzyme to working concentration (20 nM) and spike in substrate (3 µM)

Step 5: Dispense enzyme-substrate (ES) solution to assay plate (50 µL)

Step 6: Dispense CHC serial curves to assay plate

Step 7: Pre-incubate at required temperature

Step 8: Dispense NADPH solution to reaction wells (50 µL)

Step 9: Incubate at required temperature for 20 minutes

Step 10: Measure assay plate in incubator-reader in situ every minute.

An Infinite 200 microtitre plate incubator-reader (Tecan, Theale, UK) was used to perform

the incubations and kinetic reads and a 15-minute delay was used between temperature tests

to allow the incubator-reader to equilibrate to the next temperature.

Four temperatures were tested in order; 22, 25, 34 and 37 °C. Data was plotted in Excel™

and the optimum temperature determined based on the linearity of the signal over time and

the amount of metabolic product formed over time. Product formation was calculated by

measuring stock solutions of CHC (0.03 to 1 µM) and plotting a calibration curve. The

calibration curve range was based on literature data for the expected concentration of

metabolite produced from CEC substrate incubated at 3 µM. By reference to Figure 41,

columns prefixed ‘t0’ were analysed immediately after adding NADPH to those wells to

provide a pseudo-zero baseline. NADPH was then added to the signal wells (labelled by

temperature) and allowed to incubate for 20 minutes with analysis occurring every two

minutes. Eight replicates were simultaneously performed and averaged for each temperature

determination.

103

1 2 3 4 5 6 7 8 9 10 11 12

A

t0_25 25 t0_34 34 t0_37 37 t0_22 22

B

t0_25 25 t0_34 34 t0_37 37 t0_22 22

C

t0_25 25 t0_34 34 t0_37 37 t0_22 22

D

t0_25 25 t0_34 34 t0_37 37 t0_22 22

E

t0_25 25 t0_34 34 t0_37 37 t0_22 22

F

t0_25 25 t0_34 34 t0_37 37 t0_22 22

G

t0_25 25 t0_34 34 t0_37 37 t0_22 22

H

t0_25 25 t0_34 34 t0_37 37 t0_22 22

Figure 41. Microtitre plate layout used for enzyme-substrate reaction temperature dependence test. Wells

labelled ‘t0_x’ are measured immediately to provide a time=0-minute baseline; other labelled wells are the

end-point samples after incubation at temperatures 22, 25, 34 and 37 °C.

3.3.6 Design of Electrical Heater for Incubation of Droplet Chips

As the rate of an enzyme catalysed reaction is often dependent on temperature219,220, an

appropriate means to heat droplet chips to specific temperatures for studying P450 enzyme

inhibition assays was required.

An aluminium heater block was made using 5 mm sheet aluminium. To this, a self-adhesive

electrical heater pad obtained from RS components (#245-512, 12 3.75 W), measuring 50 ×

75 mm, was stuck on the reverse side (Figure 42) and a hole drilled into the edge of the metal

to accept a PT-100 temperature probe (#348-2427, RS Components, Corby, UK). The

thermal characteristics of the block were calculated and the required heater power determined

to enable microfluidic chips be heated to between 34 and 37 °C. Given the size of the heater

block and density of aluminium we can calculate the mass of aluminium using Equation (26),

and by using the specific heat capacity, the energy required to raise the temperature of the

104

block to 37 °C is found by Equation (27) and the time taken to reach this temperature

determined by Equation (28).

m=vρ (26)

Where; 𝑚 is mass, 𝑣 is volume and 𝜌 density.

E=Cpm∆T (27)

Where; E = energy in Joules, 𝐶𝑝 specific heat capacity, 𝑚 mass and ∆𝑇 temperature

change required.

W=E/t (28)

Where; W = power in Watts, E = energy in Joules, 𝑡 time in seconds

Thus, for an aluminium block of dimensions 0.025 m x 0.050 m x 0.005 m, having a specific

heat capacity of 0.91 kJ kg–1 °C and density of 2700 kg m–3, by equation 26 we can calculate

the mass of aluminium to be 16.88 g. From equation 27, the energy required to raise the

temperature of this block from room temperature (assuming 21 °C) to 34 °C is ~462 J. Using

equation 28, and the calculated required energy, the time to reach 34 °C is ~369 seconds.

Allowing for radiative losses and imperfect thermal conduction from the heater pad to the

block and microfluidic chip, we can assume efficiency in the region of 60%, thus the actually

time to reach 34 °C may be somewhat more. However, this was a reasonable delay given the

device was allowed to come to full operational temperature well ahead of any experiments,

and was not required have an adjustable temperature, requiring fast equilibration times.

105

Figure 42. Side view schematic of chip and electrical heater block. The self-adhesive heater (ii) and PT-100

temperature probe (iii) were connected to a low voltage temperature regulator control module enabling precise

temperature control of the heater to within +/- 1 °C.

A 24 V DC wall plug power adaptor (RS Components, Corby, UK) was used to power a

temperature controller (#DTC410, Tempatron, Essex, UK) which together with the PT-100

probe controlled a 12 V DC wall plug adaptor (RS Components, Corby, UK) connected to the

chip heater pad. Temperature stabilisation was determined using a separate calibrated digital

thermometer (#620-1934, VWR, UK) using a thermocouple junction temporarily fixed with

tape to the surface of a chip placed on the heater assembly.

3.3.7 Procedure for Chip-based 1A2-CEC Inhibition Assay

Equal volumes of enzyme-substrate and NADPH solution were introduced into droplets

produced at a T-junction channel by using a binary Y-shaped input for the aqueous phases.

Enzyme-substrate and NADPH solutions were introduced to the chip using a dual Harvard

syringe pump via 1ml plastic syringes. The use of this pump type avoided differences in flow

rate that mechanical fluctuations between two physically separate pumps may create.

The first chip was designed to provide an incubation of up to 20-minutes to match the

incubation time used in the microtitre plate assay method. To enable design of a chip to

achieve this within the confines of the 75 × 75 mm PMMA chip area available, flow rates of

oil and dispersed phase that worked well for droplet formation in the T-junction chip (oil at

Aluminium block

Heater pad

MF chip

PT-100 temperature

probe

106

3–12 µL min–1, dispersed phase at 1–5 µL min–1) were used as a starting point from which to

calculate the length of channel required in the spiral that would be necessary for droplets to

have up to 20-minute residence time before exiting the chip.

A spiral channel working from the middle (starting after the droplet-forming T-junction) to

the outside of the chip was cut having a turn spacing of 1 mm (Figure 43). The overall length

of the channel was ~ 560 mm. The spiral channel had a width and depth of 300 µm. Final

pump rates were 3.7 µL min–1 oil, 1 µL min–1 aqueous) to minimise back pressure and

produce droplets that remained within the chip for ~ 13.5 minutes. A second syringe pump

fitted with a 10 mL Hamilton GasTight glass syringe was used for the carrier oil and a third

pump used to introduce additional oil at 3.5 µL min–1 to the droplet spacer via the second oil

inlet (Figure 43ii).

Figure 43. ‘Spiral chip’ for incubating 4.2 nL droplets over 16–20 minutes. Enzyme-substrate and NADPH

solutions are introduced via a binary syringe pump to the Y-T junction, (i). Second oil supply (ii) is connected

near the main outlet (iii) providing separation between droplets as they flow through the detection region, (iv).

Spiral turn was 1 mm separation.

(i)

(ii)

(iii)(iv)

‘Spiral Chip’ 20 mm

Direction of flow

107

Figure 44. ‘Serpentine Chip’ delay line incubation chip designed for a droplet residence time 6 minutes. The

large width of the main channel (0.7 mm wide × 0.25 mm deep), may give rise to a pronounced parabolic flow

along the channel due to friction with the side walls221. Droplets smaller than the channel width will tend to

flow in parallel streams. Those near the wall will move more slowly and have a longer incubation time than

those in the centre of the channel. As Frenz et al. described, constrictions along the channel ensure that on

average all droplets are resident for an equal time by re-ordering them as they pass through the

constrictions221.

The droplet spacer feature was incorporated to increase the spatial separation of droplets

passing through the channel at this point where the fluorescence detector was placed.

To quantify data from chip-based enzyme-substrate reactions, a CHC serial dilution curve

was prepared, consisting of five points; 3, 1, 0.3, 0.1 and 0.03 µm. The concentration curve

set of sample standards were run through the chip prior to any enzyme-substrate experiments

to allow correct setting of the gain parameter for the PMT detector. During this, it was found

the photomultiplier tube detector was found to have a relatively limited dynamic range. The

gain was thus set to enable quantification across the expected signal range expected for the

reaction of 1A2 with CEC at 3 µM for 20 minutes in the microtitre plate tests.

0.2 mm wide x 0.25 mm deep

0.7 mm wide x 0.25 mm deep

‘Serpentine Chip’

20 mm

Direction of flow

108

As discussed previously, the results of the microtitre plate assay, indicated that after an

incubation of 20 minutes at 34 °C, when calibrated against a standard curve, the amount of

fluorescent metabolite produced represented about 8% metabolic turnover of the substrate, or

~ 0.25 µM. For the chip experiments, a similar production of metabolite was initially

assumed, and thus the gain setting was set for a full scale deflection (FSD) at about 0.3 µM

top concentration of fluorescent CHC standard. Standard curve dilutions of CHC in 0.1 M

phosphate buffer were made and sequentially run from lowest to highest concentration in the

chip (to avoid carryover issues) to achieve this.

Control activity reactions of enzyme-substrate were used to determine if the reaction was

comparable to the microtitre plate-based control activity. Phosphate buffer was first charged

to the chip via the binary pump to generate a stream of droplets providing a background

response. A 15-minute delay was observed before collecting data to ensure device

stabilisation.

Enzyme-substrate and NADPH solutions were then co-administered to the chip via the dual

pump and the resultant droplet fluorescence acquired after an initial flow of 15 minutes to

provide device stabilisation. This data was then analysed in terms of droplet reproducibility

(droplet peak heights) and the concentration of metabolite determined.

A series of increasing inhibitor concentration tests were also performed to generate IC50 data.

Table 11. Fluvoxamine dilutions and volume addition to enzyme-substrate solution.

[Working

solution]

Volume of stock

solution

Volume of phosphate buffer

diluent

Final

[Incubate]

Final

[DMSO]

60 µM 60 µL 940 µL 0.3 µM 0.03 %

18 µM 300 µL 700 µL 0.1 µM 0.17 %

5.4 µM 300 µL 700 µL 0.03 µM 0.17 %

1.62 µM 300 µL 700 µL 0.01 µM 0.1 %

109

Fluvoxamine was used as the inhibitor of 1A2. The required solutions were prepared such

that addition to the enzyme-substrate working solution yielded enzyme-substrate-inhibitor

solutions having concentrations of fluvoxamine spanning previously reported IC50 values

(0.03–0.09 µM)2,222,223 (Table 11). Fluvoxamine was dissolved in 100% DMSO (1 mM) to

make a stock solution. This was serially diluted in DMSO to yield an intermediate dilution

series which was then pre-diluted in buffer before spiking into separate aliquots of ES

solution to yield ES containing a profile of inhibitor concentrations (Table 11).

The Spiral chip detailed in Figure 43 was used to generate data for a 13.5-minute droplet

(approximately 4.2 nL volume) incubation produced at the dual Y-input T-junction in the

centre of the chip.

A second chip was constructed, featuring a shorter serpentine design of much wider channel

dimensions (Figure 44). This design utilised the same dual Y-input inlet immediately before

the T-junction for enzyme-substrate and NADPH solutions, and also incorporated the droplet

spacer via the use of a second oil supply to the outlet channel. This additional oil feed was

used to provide greater separation between droplets exiting the incubation channel for greater

separation of droplet peaks in the analysis. Pump speeds were identical to those used for the

spiral chip. The increased channel width enabled a 6-minute droplet residence time in a

shorter length of channel. As reported by Frenz et al.221, pronounced parabolic flow in the

channel may occur when the channel is substantially wider than the droplet diameter,

allowing droplets near the channel wall to remain on the chip longer than those in the centre

of flow. The Serpentine chip thus included a design feature adapted from the work of Frenz

et al. 221 wherein the channel was constricted at set intervals along its length (Figure 44, detail

inset) to force a randomised 're-ordering' of the droplets.

110

3.3.8 Procedure for Partitioning Experiments in Glass Vial

Table 12 details the oils tested. A range of small molecule compounds were chosen having

relatively high logD values and representing acid, basic and neutral chemistry (Table 13). A

solution of pH 7.4 phosphate buffer (0.1 M) was prepared as described previously. Each

compound was independently dissolved in the phosphate buffer (100 µM).

Table 12. Oils tested in the glass tube static interface partitioning test. Oil structure and dynamic viscosity

obtained from manufacturer data sheets, with the exception of olive oil224.

Oil Oil Chemistry

Dynamic

Viscosity

(mPa.s) @

25 ºC

Formula

dodecane alkane 1.34 CH3(CH2)10CH3

olive oil triglyceride ester 81 CH3(CH2)COOR

Where R is ~80% C18

siloxane oil Polydimethylsiloxane

(DC200) 20 [-Si(CH3)2O-]n

octanol alcohol 7.36 C8H18O

hexadecafluoro-1,3-

dimethylcyclohexane

fluorinated

cycloalkane 1.92 C6F10(CF3)2

perfluorodecalin fluorinated

cycloalkane 5.1 C10F18

perfluoroperhydrophenanthrene fluorinated

cycloalkane 28.4 C14F24

FC-40 fluorinated tertiary

amine 4.1 C21F48N

FC-70 fluorinated tertiary

amine 24 C24F54N

Compound solutions were initially quantified by spectroscopy using a Shimadzu UV-2401

spectrophotometer. For each of the non-fluorous oils tested, an aliquot of compound solution

111

(1 mL) was added to a 4 dram (16 mL) glass bottle (#548-0675, VWR, UK). Oil was then

added (10 mL) by careful dispensing down the bottle side, ensuring a regular planar interface

formed between the phases (Figure 45a). The bottle cap was replaced and the experiment left

at room temperature for 1 hour, sampling at 15, 30 and 60 minutes.

For each time point, a syringe fitted with blunt wide bore aluminium needle was used to

carefully extract a sample of the aqueous phase. The outside of the needle was wiped clean

prior to the extracted sample being ejected into a clean quartz cuvette and analysed by

absorbance.

Table 13. Compounds of a range of molecular ion class tested in the static and chip-based partitioning tests.

Absorbance maximum (λmax) determined by UV-VIS analysis (200–450 nm) on stock solutions in phosphate

buffer at pH 7.4. A = acid; N = neutral and B = base.

chlorpromazine

(base)

imipramine

(base)

trazodone

(base)

lidocaine

(base)

pKa = 9.3

λmax = 307 nm

pKa = 9.4

λmax = 251 nm

pKa = 7.1

λmax = 310 nm

pKa = 8.0

λmax = 223* nm

tolbutamide

(acid)

3,5-dichlorophenol

(neutral)

salicylic acid

(acid)

indomethacin

(acid)

pKa = 5.2

λmax = 307 nm

pKa = 7.9

λmax = 280 nm

pKa = 3.0

λmax = 296 nm

pKa = 4.5

λmax = 319 nm

112

Figure 45. Schematic of static partitioning experiment for fluorous and non-fluorous carrier oils; (a)

arrangement for non-fluorous oils and (b) fluorous oil denser than aqueous phase.

For fluorous oil, the aqueous phase was added after the oil, as fluorinated oils are denser than

the aqueous phase (Figure 45b). From the initial results, time point data was plotted for each

drug in the best and worst oils in respect of partitioning.

3.3.9 Investigating the Impact of Surfactant on Partitioning

A comparison of the impact of surfactant on partitioning was made using PFO (10% v/v) and

AZF (2% w/w) surfactants, each dissolved separately into FC-70.

A solution of AZF in FC-70 (2% w/w) was prepared as described previously. Another

solution containing PFO (10%) was prepared by adding PFO (1 mL) to FC-70 (9 mL).

One tube for each drug solution was set up as previously described and left at room

temperature for 15 minutes before sampling and then again at 45 minutes. In addition, a t0

sample of each drug working solution was also sampled. All samples were analysed by UV-

VIS at the respective drug maximum absorbance (λmax) using a UV-2401 Shimadzu

spectrophotometer and low volume quartz cuvette of 1cm path length (0.7 cm3 volume).

113

3.3.10 Procedure for Partitioning Experiments Using AstraZeneca

Collection Library Compounds

An extended investigation into the possibility of compound partitioning to the oil phase was

conducted using a portion of the AZ compound collection library. In brief, the compounds

were selected from parts of the collection library known to have a wide range of chemical

diversity that would provide opportunity to study how much of an impact compound leakage

from droplets may have on biochemical assay.

1406 compounds were randomly selected from a sub-collection within the library frequently

used for routine assay validation purposes. The compounds were known to be stable and

having defined physicochemical properties, including; molecular structure, molecular mass

LogD, ClogP, pKa, number of rotatable bonds, number of fluorine atoms, polar surface area,

number of H-donors and number of H-acceptors. The available properties were hypothesised

to be helpful in defining correlation between solubility in the fluorous/non-aqueous phase.

The absorbance analysis approach used in the previous glass vial tests technique may limit

the choice of compounds to those having suitably strong chromophores. As some compounds

tested may not absorb readily, High Performance Liquid Chromatography Mass spectrometry

(HPLC–MS) was chosen to quantify samples from this set of experiments. Furthermore, the

HPLC–MS apparatus available at AZ was equipped with an autosampler to provide large

batch automated analysis. The majority of small molecule compounds found within the

pharmaceutical industry typically having masses between 200 and 700 Daltons are well

suited to Atmospheric Pressure Ionisation (API) mass spectrometry25,225,226. Table 14 details

the methodology applied for HPLC-MS analysis. Ian Sinclair (AZ) is acknowledged for his

assistance in running submitted samples.

114

Table 14. HPLC-MS analytical method used for rapid elucidation of aqueous partitioning experiment samples.

HPLC Method All HPLC separations were performed on an Agilent 1200 system utilizing two binary G1312B high-pressure gradient pumps connected, as has been previously

described in the literature227, with the solvent flow from pump 1 delivered through the auto-sampler and that from pump 2 delivered immediately after the separation

column. The typical solvent system was a combination of (A) HPLC grade water (Sigma–Aldrich, UK) containing 0.1% formic acid (Sigma–Aldrich, UK) and (B)

HPLC grade methanol (Sigma–Aldrich, UK) containing 0.05% formic acid (Sigma–Aldrich, UK).

Solvent was delivered from pump 1 at a flow rate of 700uL per minute with a composition at time zero of A=95% and B=5%, developing over a linear gradient such

that, after 2 minutes, B=100%, which was maintained for 0.5 minutes before being returned to starting conditions.

The solvent composition for pump 2 was delivered in exact opposition to that for pump 1 at all times, starting from A=5% and B=95% at time zero and traversing a

mirror-image gradient toward A=100% after 2 minutes, held for 0.5 minutes before being returned to starting conditions. The two streams were combined using a T-

piece after the separation column and, by this method, produced a constant 50:50 mix of A and B going forward, and thus minimized any solvent–related drift in signal

response. The separation column normally used was a 30x2mm Kinetex C18 3uM column (Phenomenex, Torrence, CA).

Peak Detection and Mass Spectrometry

The HPLC eluent was delivered through an Agilent G1315B diode array detector, scanning from 220–300nm at a rate of 5Hz, and then split between a single

quadrupole MS and a CAD.

Two configurations of MS systems were employed.

1. Agilent 6140 single quadrupole mass detector with G1367C well plate autosampler, G2255A well plate stacker, G1316B column oven and ESA Corona CAD

detector (ESA, Chelmsford, MA), and Agilent 35900E Analog to Digital Converter (ADC), all operating under Chemstation version B.03.01.

2. Waters ZQ quadrupole mass detector with integrated ADC, a CTC PAL autosampler (CTC Analytics, Switzerland) and an ESA Corona CAD detector, all

operating under MassLynx 4.0.

The corona discharge detector relied on droplet production and transformation into a particle beam, and was particularly sensitive to changes in solvent affecting the

size of the droplets, hence the careful control of post-column solvent composition. The particle beam was passed through a stream of charged nitrogen molecules where

charge was transferred to the analyte molecules contained within the droplets. The charge on these droplets was then detected via an electrometer, with a proportional

output acquired at a rate of 5Hz through the ADC. In this way a chromatogram was produced by plotting electrometer current against time.

The detector had an equivalent response for equal mass-per-volume concentrations of non-volatile analytes. The ratio of CAD peak area to sample molecular weight

was therefore proportional to the molar concentration of sample228.

The CAD detector was complementary to the DAD in that, in the main, it provided confirmation of the sample purity but could also provide information not available

from the DAD. This was particularly true when the sample structure did not absorb in the UV detection range, or where the sample was not retained on the

chromatographic column but was eluted in the solvent front along with its original solvent (normally dimethyl sulfoxide (DMSO)). In both of these cases, sample purity

was not measurable by the DAD and was assessed from the CAD alone. The two chromatograms produced were integrated within the respective operating systems of

the two instrument configurations and mass spectra, in both positive and negative ion modes, were produced for each detected peak. The processed data was passed into

our in house database where it could be reviewed and annotated as described in the literature229.

115

3.3.11 Testing AZ Compound Solubility

The 1406 compounds were first tested to determine which compounds were insoluble,

partially soluble or fully soluble in Phosphate Buffered Saline (PBS) solution (0.1 M) as this

was the aqueous phase representing the buffer that would be eventually used for the enzyme

experiments. Each compound was obtained from the AZ Compound Management Group as a

100 % Dimethylsulfoxide (DMSO) solution dispensed using an Echo acoustic dispenser

(Labcyte Inc., Sunnyvale, CA) to a polypropylene 384-well microtitre plate (500 nL)

(Greiner Bio-One, Stonehouse, UK). Each compound was present in 5 wells, with each

dispense being a different stock concentration. Phosphate buffered saline (PBS) solution was

added to the wells to dilute and generate standard curve solutions at 20, 40, 60, 80 and 100

μM. The plates were then sealed and left for 4 hours at room temperature prior to HPLC-MS

analysis, whereby the integrated peak area was recorded. Compounds having a linear

response across the full concentration range were flagged as soluble (S), those showing

partial linear response flagged partially soluble (PS) and those not having any mass peak

found at any concentration deemed not soluble (NS).

3.3.12 Partitioning Test – Round 1

FC-40 was chosen for the fluorous phase, which having a lower viscosity than FC-70 was

easier to pipette and settle in 384-well microtitre plates. Both oils were seen to perform

similarly in previous partitioning tests, and an assumption was made that data obtained using

this oil would rank similarly as FC-70 (both oils are perfluorinated aliphatic tertiary amines).

Stock solutions (500 nL, 10 mM) of compounds flagged as (S) and (PS) in the Compound

Solubility test were dispensed to flat bottom 384-well plates using an Echo acoustic

dispenser. Replicate compound set dispenses were made to provide independent samples of

116

each compound for time = 2 days (t2), time=4 days (t4) and time=0 (t0). Additional t0, t2

and t4 sets were also prepared for where FC-40 had AZF surfactant added (2% w/w).

PBS was added (50 µL) to each well to yield compound solutions at 100 µM, the plates

sealed with a gas impermeable foil seal and left for 3 hours at room temperature. After this

time, the plates were unsealed and FC-40 added (50 µL) to all plates, except the t0 plates. All

t0 plates and plates for subsequent t2 and t4 samples were resealed, briefly centrifuged to

settle the fluids and expunge air bubbles, and placed into an incubator at 37 °C, 98% relative

humidity.

On day 2, two sets of plates were removed from incubation, one for each where AZF was

absent and present in the FC-40. The plates centrifuged to remove any bubbles and the seals

removed. A PlateMate pipettor (Matrix Technologies, Hudson, NH) fitted with a 384 head

and 100 µL tips was used to pipette 40 µL aqueous samples from each well (t2 samples).

The samples were dispensed to fresh 384-well plates, sealed and submitted for MS analysis.

One set of t0 plates were also sampled to fresh plates and submitted for MS analysis. This

process was subsequently repeated on day 4 for t4 samples.

3.3.13 Partitioning Test – Round 2

Round 2 was identical in methodology to Round 1, except only t0 and t4 sample plates were

prepared. A Kohonen map230 process was used to help select compounds having a diverse

chemistry. A further 800 compounds were selected from other AZ compound libraries. As

access time to MS was limited, initial solubility experiments were not performed and rather a

commercial predictive model produced by ACD Labs (Toronto, Canada) was used to predict

aqueous solubility. Compounds having a predicted aqueous solubility at pH 7.4 of <80 µM

were excluded from the test.

117

3.3.14 Procedure for Predictive Partitioning Modelling

Following MS analysis and interpretation of the partitioning data from Round 1 and 2, a

model was developed in Pipeline Pilot (Dassault Systèmes, Cambridge, UK) using a

Bayesian231 pharmacophoric feature connectivity fingerprint (‘circular fingerprint”) approach

to predict solubility of compounds in the fluorous oil, based on molecular structure and

constituent atoms. For this, the rank ordering of compounds into ‘partitioning’ and ‘non-

partitioning’ categories was used to build training and test sets for the model. Compounds

that were observed to partition more than 50% in the fluorous oil were categorised as

partitioning. Recognising the potential for significant variability that may be associated with

HPLC-MS17,232, compounds having fluorous partitioning values of between -20 and 20%

were categorised as non-partitioning. Compounds having partition values of 20% to 50%

were initially excluded from the model to produce a separation between partitioning and non-

partitioning data sets. A script was run enabling each of the partitioning and non-

partitioning data sets to be randomly separated into 90% training 10% test sets for the model.

This was executed 5 times and an averaged model performance to be calculated. For each

iteration, the Pipeline pilot script was instructed to output a 'Receiver Operating

Characteristic' plot (ROC) which plots false positive rate (0 to 1) against the true positive rate

(0 to 1). In the event the model could accurately fit test compounds to the training set based

upon the parameters contained within the model (the 'circular fingerprint' analysis of

structure), the ROC curve was expected to generate a high value closer to 1, conferring a

good model fit.

To test the model, additional compounds were selected based on an output score. The output

score was labelled ‘Microfluid’. Positive numbers indicated a higher chance of partitioning

to the fluorous oil, whereas very low and negative numbers would be indicative of

compounds not likely to partition.

118

The model was applied across the whole AZ compound collection (~ 2 million compounds)

to generate a list containing the top score compounds having Microfluid scores >5. A similar

list was generated using the model in reverse to provide 10000 compounds not expected to

partition, having scores >–5. From each list, compounds were examined to determine

predicted aqueous phase solubility. Those having solubility <80 µM were excluded. 384

compounds (192 top and 192 bottom) were selected for inclusion in a third round microtitre

plate experiment to attempt validation of the predictive model.

3.3.15 Partitioning Test – Round 3

Round 3 was identical in methodology to Round 1 and 2. Compounds that were selected

from the output of the model after round 1 and 2 were run in round 3 and analysed to

determine if the experimental results were in agreement with the prediction of the model.

3.3.16 Procedure for in situ Chip Partitioning Experiments

The T-junction channel chips described previously were used to produce droplets of

approximately 250 µm diameter (~8 nL) providing on-chip droplet residence times of ~ 0

minutes and 13.5 minutes, respectively, when using an oil flow rate of 3.7 µL/minute.

Calibration plots of standard solutions of CHC metabolite were used to quantify the loss of

material from the aqueous phase by absorbance measurement and converted to a percentage

loss from the aqueous droplet.

Each chip was tested in turn with the outlet of the main channel terminated in a short length

of plastic tubing to collect droplets in a 0.5 mL Eppendorf vial. This vial was placed in ‘cup'

formed out of aluminium foil to which the high voltage power supply was connected.

119

Oil was flowed into the chips at 10 µL/min (without aqueous phase delivery) to purge air

from the channels. After 10 minutes, oil flow was stopped and the aqueous drug solution

loaded.

Figure 46. Chips used for chip-based droplet partitioning; (a) T-junction to provide 0 min droplet residence

time; (b) 13.5 min droplet residence time. Oil flow rate was 14 µL/min with a dispersed phase flow at 7 µL/min.

Droplets were collected in a 1.5 mL Eppendorf tube for 1.5 minutes (1) prior to subjecting the droplet emulsion

to a high voltage electric field (2) resulting in fusion of the droplets to a uniform layer above the carrier oil (3).

3–4 µL of this aqueous layer was rapidly sampled and analysed by UV-VIS on a Nanodrop Protein analyser.

The carrier oil phase was switched back on (3.7 µL/min) and the aqueous phase flowed (1

µL/min) until all air was expelled from the chip and a steady stream of droplets was achieved

prior to starting sampling. For both experiments, droplets were collected for precisely 1.5

+ 2-3 kV

1 2 3

Droplet emulsion

Aluminium foil shroud Oil phase

(a)

(b)

Coalesced droplet pool

(a)

(b)

+ 2-3 kV

1 2 3

Droplet emulsion

Aluminium foil shroud Oil phase

(a)

(b)

Coalesced droplet pool

(a)

(b)

Direction of flow

Direction of flow

120

minutes and the collected liquid subjected briefly to a 2 kV electric field inducing electro-

fusion of the collected droplet emulsion (Figure 46). Approximately 3 to 4 µL of pooled

droplet phase was available for sampling using a 10 µL positive displacement pipette. A sub-

sample of this (2 µL) was subsequently analysed on a Nanodrop protein analyser

(ThermoFisher, MA) for each drug tested by this method.

3.3.17 Procedure for Shake-Flask Experiment

As an orthogonal approach to determine unwanted partitioning of the substrate (CEC) or

metabolite (CHC), a modified shake-flask approach was used where intimate mixing of the

two phases was achieved by constant inversion19,212. The aim was to provide a much bigger

surface area between the two phases to determine if there was a marked increase or tendency

of either material to partition to the oil phase. To test the partitioning potential of CHC and

CEC over different time courses, working solutions were made (200 µM). CHC was

dissolved initially to a 1mM stock in phosphate buffer (0.1 M). As CEC had limited aqueous

solubility, it was dissolved to 400 µM in 100% ethanol and then diluted 1:1 with phosphate

buffer. The test comprised of two parts. Firstly, each solution was added to 2ml Eppendorf

tubes (1 mL); one containing 1 mL FC-70 + AZF (~2 % w/w) and another containing FC-70

alone. All tubes were constantly inverted for 5 minutes to provide thorough mixing of the

two phases. After this time the tubes were centrifuged for 1 minute and an aqueous sample

extracted from each tube. The samples were analysed by UV-VIS absorbance at the λmax for

each (CEC at 360 nm and CHC at 405 nm) on a Nanodrop protein UV-VIS

spectrophotometer.

Secondly, 0.2 mL and 1 mL CHC (200 µM) was added to fresh tubes containing 1 mL FC-70

+ AZF (2% w/w). The tubes were inverted for 5 minutes, centrifuged for 1 minute and a

121

small aliquot (15 µL) taken before resuming inversion for a further 10 minutes after which

time the tubes were centrifuges again and a final sample taken (15 µL). All samples were

analysed on a Nanodrop protein UV-VIS spectrophotometer.

3.3.18 Procedure for Labelling Proteins/Enzymes

A series of experiments were conducted to investigate the potential for proteins/enzymes to

undergo some form of adverse reaction or physical effect at the oil-droplet interface. Protein

behaviour at liquid-liquid interfaces have been described184,185 and discussed previously.

Given the potential for proteins to undergo non-specific binding events and/or conformational

change at such interfaces, it is perhaps not completely unexpected that such artefacts may

well effect the 1A2 P450 cytochrome employed in this research leading to an apparent loss of

enzyme activity and hence reduced reaction potential and metabolic output. Ideally, one

could measure the native fluorescence of the protein by measuring emission from

tyrosine/tryptophan residues within the protein structure. To achieve this, a suitable

excitation source at ~280 nm is necessary which was unavailable for this project. An

alternative approach was to fluorescently label the protein with a tag that can be excited by

longer wavelength light sources.

Casein (Sigma–Aldrich) and a protein (“Protein A”) supplied by AZ having a mass of

approximately 68 kDa were used as surrogate enzyme reagents. Although both were smaller

than the overall structure of a P450 cytochrome17 the assumption was made that, being

smaller, either material would have greater mobility and hence migrate to the interface more

quickly, enabling any droplet surface interactions to be studied over a shorter time period.

Furthermore, the structure of Protein A and casein were known, which allowed a more

deterministic view of fluorescent labelling success. A fluorescein isothiocyanate (FITC)

122

labelling kit (ThermoFisher) was used to prepare buffer solutions of fluorescent FITC-tagged

Protein A and casein to be prepared. This kit also included dye removal columns to ensure

no unbound fluorescent tag was present in the labelled protein solutions.

Kit instructions (see Appendix A) were followed using Protein A and casein at 2 mg/mL.

Absorbance measurements were taken at 495 and 280 nm to determine the extent of

fluorescent labelling (F/P ratio) as defined by Equation (29).

Molar F/P = 𝑀𝑊

389.

𝐴495 195⁄

𝐴280−[(0.35×𝐴495)] 𝐸2800.1%⁄

(29)

Where MW is the molecular mass of protein; 389 is the molecular mass of FITC

label, 195 is the absorption 𝐸2800.1% of bound FITC measured at 490nm pH 13.0; 0.35

x A495 is the correction factor due to absorbance of FITC at 280 nm.

3.3.19 Procedure for Determination of Droplet Labelling Ratio

A reservoir droplet chip was designed and fabricated from clear PMMA (Figure 47) to allow

many droplets to be collected and observed in a concentrated area in the chip. After flowing

droplets into the reservoir until nearly full, the pumps were turned off and the pressure

allowed to equilibrate across the chip prior to taking epifluorescent and laser scanning

confocal images of droplets in the chip. Epifluorescence and laser scanning confocal

microscopy were conducted at an excitation wavelength of 405 nm. Epifluorescence images

were acquired through the eyepiece (afocal imaging233) using a webcam. In the case of laser

scanning microscopy, a careful balance between high enough excitation power and

acquisition time to avoid fluorescence bleaching was necessary to obtain useful data. Focal

plane slices through the droplets at 8 µm intervals were used to assess the variation of

fluorescence relative to the droplet perimeter wall.

123

Figure 47. Reservoir chip used to observe droplets under a confocal laser scanning microscope. The large

reservoir channel area has the same depth of 250 µm as the other channels. The expanded area (A) slows the

linear fluid flow rate through the chip resulting in slow-moving, close-packed droplets.. Aqueous inlets (B and

C) were used to supply protein solution to the chip. The central area between the four elongated supports was

used for microscopy image collection.

A stock solution of fluorescein in phosphate buffer at pH 7.4 was prepared and diluted to a

similar concentration as the FITC label present in 2mg/mL protein solutions (~1 µM). This

enabled comparisons of fluorescence to be made at the same optical magnification and gain

settings during acquisition of epifluorescent and laser scanning data. Epifluorescent droplet

data was interrogated using ImageJ software to assess pixel intensity across the droplet

diameter. Both FITC-labelled Protein A and FITC-labelled casein were tested.

3.3.20 Procedure for Blocking Enzyme Adsorption at the Interface

The concentration of enzyme typically used in such P450 studies is very low. The method

previously described in this work follows a methodology commonly used within AZ where

the final incubate P450 enzyme concentration is only 10 nM. At this level, microtitre plate-

based experiments have been observed to work2,28,36. A droplet of ~14 nl volume has a

diameter of 300 nm and a corresponding surface area of 282 nm2. At 10 nM, there are 6.022

(A)

800 µm ‘Reservoir Chip’

(B) (C)

Direction of flow

124

× 1015 molecules, thus in a droplet volume of 14 nL with surface area of 282 nm2 (high

surface area to volume ratio), it follows that there is ample opportunity for a vast majority, if

not all of the molecules to rapidly localised/bound at the droplet surface. Compared to a 96

well plate where the surface area might be 190 mm2 and volume of liquid 200 µL (low

surface area to volume ratio) molecules in the liquid are less likely to saturate the surface

quickly. If a substantial number of enzyme molecules diffuse to the droplet interface and are

then subsequently made inactive, either through conformational change or other steric

interference effect, it follows that insufficient active enzyme may then reside within the

droplet for reaction with the substrate. To investigate this possibility, blocking proteins were

titrated at high concentration to the normal P450 enzyme-substrate mixture and the metabolic

fluorescent signal observed to see if an increase in signal corresponded to an increase in

blocking protein concentration. An increase in signal would then suggest that protein is

successful in blocking the artefact effects that may lead to reduced signal.

Three blocking proteins were investigated; casein, gelatin and bovine serum albumin (BSA).

In each case, the blocking proteins were made up to 50, 500 and 1000 µg/mL in phosphate

buffer (0.1 µM) and the required quantity of 1A2 enzyme and substrate stock added (Table

15).

Table 15. Proteins used in blocking experiments.

Blocking Protein (5 mL total final volume)

Casein BSA Gelatin

Approx. wt. (kDa) 23 67 95

Stock conc. (mg/mL) 5 5 5

Final conc. (µg/mL) 50 500 1000 50 500 1000 50 500 1000

Volume to add (µL) 50 500 1000 50 500 1000 50 500 1000

125

Each solution of blocking protein was tested for its impact on droplet formation prior to

replicate incubation (n=3) in a 384-well microtitre plate as previously described at 6 and 13.5

minutes. Fluorescence was measured on a Tecan Infinite 200 plate reader (Tecan UK, Theale,

UK). The identical test for each concentration of blocking protein was then repeated in the

Spiral chip to determine the effect each blocking protein had on the fluorescent signal.

126

4 Results and Discussion: DROPLET FORMATION

The first consideration for the translation of any assay process, and indeed any attempt to

quantify the issue of partitioning and leakage of material from aqueous droplets to the oil

carrier phase, is to determine the conditions best suited to form stable droplets under

controlled conditions. This section considers the results for a range of conditions tested to

understand the best oil selection to use for stable droplet production. Droplet reproducibility

was assessed by a combination of optical imaging and PMT output using a concentration

curve series of CHC solutions. Accurate and precise droplet formation and analysis is

essential to enable correct measurement of the miniaturised P450 cytochrome inhibition

discussed in subsequent chapters.

4.0.1 Droplet Formation and Linearity

Of the neat oils tested for droplet formation, only octanol and olive oil formed reproducible

droplets free from aqueous phase wetting of the chip (Table 16). Neat fluorous oils did not

readily produce droplets, but rather resulted in parallel laminar flows of oil and aqueous

phase through the main channel at all flow rates tested. Dodecane did not produce droplets

under any of the flow conditions tested. PDMS produced droplets that were erratic and not

stable with a tendency to 'stick' to the channel walls.

The higher viscosity of olive oil3 required lower oil flow rates to produce droplets and also to

ensure the chip seal was not ruptured from high back-pressure. Fluorocarbon oils containing

10% perfluorooctanol produced stable droplets readily, although they were observed to merge

upon contact. Concentrations of PFO at 1% did not reliably form droplets in FC-40 or FC-70.

FC-70 with AZF (2% w/w) produced droplets that were stable for at least four hours.

127

Droplet production frequency was higher for lower viscosity oils, which is to be expected

from the impact of surface tension discussed previously and reported work on droplet

formation (as discussed in chapter 1).

Table 16. Droplet formation using various oils in the T-junction chip.

Carrier Oil (100%) Droplet

Production Droplet formation stability

Olive oil yes very good†

Polydimethylsiloxane (PDMS) erratic erratic‡

Octanol yes very good†

Dodecane no* -

Hexadecafluorodimethylcyclohexane no* -

Perfluorodecalin no* -

perfluoroperhydrophenanthrene no* -

FC-40 no* -

FC-70 no*

FC-40 with PFO (10% v/v) or AZF (2% w/w) Yes very good

FC-70 with PFO (10% v/v) or AZF (2% w/w) Yes very good

†Aqueous flow: 3 µL/min; oil flow 7 µL/min

‡Aqueous flow: 2 µL/min; oil flow 8 µL/min

* Where droplet formation did not occur, or was poor, pronounced wetting out of the chip occurred leading to

laminar streaking of the dispersed phase over the range of oil and aqueous flow rates tested.

FC-70 fluorocarbon oil with surfactant (either PFO (10% v/v) or AZF (2% w/w)) produced

droplets easily without wetting out the chip material. This was confirmed by frame analysis

of a section of video capturing droplet formation, such as that depicted in Figure 48. Here,

the transition from emerging bulge of aqueous phase (Figure 48a) to free droplet in the main

channel (Figure 48c) can be seen. Importantly, there is no evidence of the aqueous phase

smearing or leaving aqueous residue adhered to the channel wall or junction. This suggested

128

the conditions for droplet formation were optimised in respect of channel geometry, oil type

and flow rate of oil and dispersed phase for this design of T-junction and experiment.

Figure 48. An example of correct droplet formation where no aqueous wetting of the main channel occurs; (a)

the droplet emerges from the aqueous inlet; (b and c) no evidence of aqueous phase left behind (from wetting

out of the chip) at the junction or adjacent channel wall.

Figure 49. Droplet reproducibility as determined by optical observation using a video camera. Six frames

cover the emergence of a new droplet formed at the T-junction. All droplets demonstrated very similar size and

shape, indicating a high degree of formation precision. The detail image depicts a droplet (highlighted yellow)

chosen for analysis in ImageJ. By scaling against the known width of the channel (300 µm) and applying this in

ImageJ, the droplet 'length' may be determined. By repeating this for a number of consecutive droplets, it is

possible to calculate variance of droplet size and hence volume by observing changes in the droplet dimension.

300 µm

(a) (b) (c)

Direction of flow

Direction of flow

129

Table 17. Droplet reproducibility as determined by manually fitting perimeter guides to droplet images in

ImageJ using the known channel width as a calibration guide. The results suggest the method is reasonably

precise yielding a low deviation across 31 consecutive droplets, however, the subjective nature of fitting

measurements to image pixels that may be affected by optical artefacts may question the accuracy of this

method.

Droplet Droplet Area (sq. mm) Circumference (mm) Diameter

(mm)

Volume

1 0.08 0.98 0.312 0.016

2 0.08 0.97 0.309 0.015

3 0.09 1 0.318 0.017

4 0.09 1.08 0.344 0.021

5 0.09 1.05 0.334 0.020

6 0.08 1.04 0.331 0.019

7 0.09 1.05 0.334 0.020

8 0.09 1.04 0.331 0.019

9 0.07 0.93 0.296 0.014

10 0.06 0.85 0.271 0.010

11 0.08 1 0.318 0.017

12 0.08 1.02 0.325 0.018

13 0.08 1.03 0.328 0.018

14 0.08 0.99 0.315 0.016

15 0.08 0.99 0.315 0.016

16 0.07 0.95 0.302 0.014

17 0.07 0.94 0.299 0.014

18 0.08 0.97 0.309 0.015

19 0.08 1.02 0.325 0.018

20 0.07 0.92 0.293 0.013

21 0.08 0.99 0.315 0.016

22 0.07 0.95 0.302 0.014

23 0.09 1.02 0.325 0.018

24 0.09 1.06 0.337 0.020

25 0.08 0.99 0.315 0.016

26 0.09 1.05 0.334 0.020

27 0.08 0.98 0.312 0.016

28 0.08 1.02 0.325 0.018

29 0.08 0.98 0.312 0.016

30 0.08 0.99 0.315 0.016

31 0.09 1.08 0.344 0.021

Avg. 0.08 1.00 0.318 0.017

SD 0.008 0.050 0.016 0.002

% CV 9.6 5.0 5.0 15.0

Two methods were used to assess droplet reproducibility. The first involved recording a

video segment of droplet formation and using image frame analysis in ImageJ to determine

droplet dimensions. Figure 49 depicts six contiguous frames captured during stable droplet

130

production. The detail box indicates the area of each frame interrogated in ImageJ. The X-Y

dimensions of the droplets were found by scaling against a known dimension (channel

width). Table 17 summarises the results obtained from 31 consecutive droplets sized by this

method of droplet interrogation.

All droplets appeared to be of a similar size, however, this method proved relatively

unreliable as there was an inherent subjective inaccuracy when selecting pixels in the image

representing the periphery of the droplet or channel. For example, as highlighted in the detail

box of Figure 49, the droplet edge is difficult to determine accurately due to refractive

artefacts of the incident light making the exact dimensions of the droplet indicated by the red

arrows harder to discern. This approach may have been more successful by using a camera

of higher resolution and/or magnification, which was not available for these experiments.

The precision of droplet volume calculated by this approach was found to be fairly low at

15% CV.

The second method to assess droplet reproducibility used spectroscopy. The data obtained

describe the variability of signal between adjacent droplets of identical composition. These

data were acquired from the data logger, and, when plotted, rendered a Gaussian-shaped peak

similar to that depicted in Figure 50. Baseline integration algorithms in Origin provided a

means to quantify peak heights and calculate peak areas. Although this technique was unable

to directly measure droplet physical dimensions, it was possible to correlate the signal for

each droplet to its properties. The (known) droplet concentration was related to the peak

height and the peak width corresponded to the transition time of the droplet, from which the

width could be determined.

131

Figure 50. Example of peak data acquired from logger. Droplets are represented as Gaussian-shaped peaks

when the acquired logger data is plotted. Peak leading and trailing edges correspond to the leading and

trailing 'sides' of a droplet as it passes through the detection zone. Peak height correlates to the concentration

of analyte within the droplet, as measured by PMT output voltage, V, whereas peak width is primarily

representative of the droplet width passing through the detection zone measured in seconds, T.

Variations in peak height and peak width described the overall variation seen between

droplets. For a given flow rate, smaller droplets would pass through the detection zone more

quickly resulting in smaller peak widths. Conversely, larger droplets had a wider peak shape.

In both cases, peak height was seen to vary less than the calculated peak area (Table 18). As

peak height is proportional to the PMT output, which is in turn proportional to the analyte

concentration, under steady droplet formation and flow conditions (as shown earlier in the

droplet formation experiments) the analyte concentration in each droplet was not expected

vary. Peak area is reliant on an accurate baseline and good peak resolution for accurate

integration, which may be more variable given the baseline noise seen in some of the data

plots (Figure 52) which deteriorates as the signal to noise ratio decreases. Positional

variation of a droplet within the channel and/or alignment of the detection apparatus may also

contribute to droplet signal variance. Figure 52 shows excerpts (~10 s) of the data acquired

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0.000 0.100 0.200 0.300 0.400 0.500

V

Time (s)

Droplets of CHC (1 µM)

Trailing edge Leading edge

132

for each concentration as plotted and integrated in Origin. Firstly, a baseline detection was

performed to account for any baseline offset (electronic background signal) and to provide a

starting point for peak detection (detected peak maximums were automatically highlighted by

a red cross by the Origin peak identification algorithm). Following peak detection, peak area

integration was performed.

Table 18. Variability associated with different concentrations of CHC in the T-junction chip.

Conc.

(µM)

Mean Pk Ht.

(std. dev.) %CV

Mean Pk. Width

@ half ht. (std. dev.) %CV Time (s) n

0.05 0.06 (0.01) 9.19 68.19 (11.93) 17.49 84.5 252

0.5 0.75 (0.05) 6.10 44.57 (8.27) 18.56 101 204

1 1.65 (0.11) 6.72 37.53 (3.72) 9.92 106 235

By performing the aforementioned droplet variability tests at different concentrations, it was

possible to obtain an indication of linearity. Figure 51 shows the correlation of the three

concentrations tested and the means shown in Table 18. For both peak height and area

linearity was excellent, both with a correlation co-efficient above 0.9, suggesting there was

little or no signal bias dependant on concentration across the range tested.

133

Figure 51. Three serially diluted solutions of CHC were run through the chip and quantified. A linear fit was

obtained over the dynamic range investigated, as indicated by R2 > 0.9. With reference to Table 18, variability

was well controlled with peak height variability ranging 7– 9 % (where number of droplets, ~n=230).

Corresponding variability determined by peak area calculation exhibited a similarly good fit.

y = 59.659x - 0.7949

R² = 0.9997

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

0 0.2 0.4 0.6 0.8 1 1.2

Pk.

Are

a

Concentration (µM)

CHC Droplets: Peak Area

Peak Area

Linear (Peak Area)

y = 1.6803x - 0.0493

R² = 0.9981

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

0 0.2 0.4 0.6 0.8 1 1.2

Pea

k H

eight

(V)

Concentration (µM)

CHC Droplets: Peak Height

Peak Height

Linear (Peak Height)

134

(a) 1 µM CHC (b) 0.5 µM CHC

(c) 0.05 µM CHC

Figure 52. Excerpt droplet data covering 10 seconds of acquisition time for 1, 0.5 and 0.05 µM CHC. In (b),

the sixth detected droplet is much smaller than all other droplets in this series. This is most likely to be due to

an erroneously small droplet that did not pass through the detection zone correctly and hence did not result in

the same RFU output. Notably in (c), the signal to noise ratio has decreased, signified by the relative increase

of baseline noise apparent. As baseline noise increases, it becomes harder to accurately integrate each peak

and hence overall variation is seen to increase.

There is nothing apparent in the data to suggest detector saturation at the maximum

concentration used in this test (this would be evident from a pronounced decrease in response

0 2000 4000 6000 8000 10000

0

2

RF

U

Time (ms)

0 2000 4000 6000 8000 100000.00

0.25

0.50

0.75

1.00

RF

UTime (ms)

0 2000 4000 6000 8000 100000.06

0.07

0.08

0.09

0.10

0.11

0.12

0.13

0.14

RF

U

Time (ms)

135

with increasing analyte concentration), thus the particular detector setup was deemed accurate

for conducting the cytochrome P450 inhibition assay described previously. It was not

possible to adjust the beam width of the excitation light, other than to bring it into focus

which resulted in a spot of light over the detection channel of 0.5–1 mm diameter.

4.0.2 Dual Aqueous Input Electro-Fusion T-junction Chip

The dual aqueous chip having opposing aqueous phase inlets was investigated to assess how

suitable this would be as a means to introduce the two solutions that would ultimately be

required in the miniaturised droplet P450 cytochrome inhibition assay, that is, the enzyme-

substrate and NADPH solutions. The success of this approach relied upon the principle of

electrofusion to induce rapid coalescence and mixing of the reagent droplets from the

opposing T-junctions. In this process, it was essential that reagent concentrations in the

subsequent reactant droplet remained consistent to obtain useful results.

The binary inlet chip produced reproducible droplets when using fluorocarbon oils containing

10 % PFO. Hydrodynamic coupling between the two inlets resulted in alternating droplet

formation from each aqueous inlet, giving droplet pairs slightly spaced apart. Slight

differences in channel geometry were considered responsible for the volume difference seen

between droplets produced from either side of the chip (Figure 53). Whilst this did not

present a major problem, as the supplied reagent concentration could be adjusted to

compensate for differing droplet volumes, critically, when voltages over the available range

of 100–1000 V were applied to the dispersed phase inlets the expected droplet coalescence

did not occur. Possible explanations for the lack of droplet fusion included the gap between

droplets being too large, insufficient charge imparted to the droplets, or too much surfactant

having the effect of over-stabilising the droplets. Attempts to reduce surfactant concentration

136

led to difficulties in forming droplets (as seen before in the single aqueous input T-junction

chip) with linear streaking resulting from wetting out of the chip by the aqueous phases.

Figure 53. Binary aqueous input chip. In this single frame image taken from a video clip, although the droplets

produced by both inlets appear reproducible, hydrodynamic coupling between the inlets results in alternate

droplet formation from the inlets. Application of a voltage potential (100–1000 V) via connection to the inlet

electrodes did not result in reliable droplet coalescence. Small channel geometry differences and an imbalance

of flow rates was suggested by differing droplet volumes between both sides of the channel.

Attempts to change droplet spacing by altering the ratio of dispersed phase and oil flow rates

resulted in substantial changes in droplet size which destabilised the whole flow regime and

resulted in erratic droplet formation and also wetting out of the chip.

Electric field-induced droplet fusion by the method demonstrated by Raindance

Technology186 may be a better approach; an expanded channel region downstream of the

droplet pairs would enable droplets to move closer, facilitating electro-fusion, however the

spacing of droplet pairs and the overall frequency of droplet production would need careful

adjustment to ensure correct droplet fusion. As the correct conditions for the binary inlet

electro-fusion chip were difficult to determine, further work on this was abandoned in favour

of the dual input Y-junction type chips described in chapter 3 ('Spiral' and 'Serpentine' chips).

These were seen to provide more controllable droplet formation.

Direction of flow

137

4.0.3 Surfactant CMC Determination by Dynamic Light Scattering

As part of the droplet formation investigations, the use of the AZF material at 2% w/w was

successful in yielding stable droplets as described earlier in this chapter. As discussed in the

introduction, use of fluorosurfactants of the PFPE type, at concentrations in the 1–2 % w/w

range, has been described in the literature. A useful means of assessing a surfactant is to

consider its Critical Micelle Concentration (CMC), describing the point at which surfactant

monomers spontaneously form micelles. As described earlier, the formation of micelles may

have a role in the transport of material between phases. This section is thus included here

ahead of the following chapter concerning the partitioning of material from the droplet to the

oil phase to help understand the role fluorosurfactant may have on droplet leakage.

Figure 54 shows a plot of backscatter count (kilo counts per second) against concentration of

AZF fluorosurfactant. Between 0.5 % w/w and 0.75 % w/w, there was a subtle ‘knee’ having

a different count-concentration relationship, seen as a change in the slope. Fitting linear

regression plots to both halves of the data set reveals high R2 values, suggesting a good fit to

the data. The 'knee' may be evidence of the CMC or other complex aggregation event of the

fluorosurfactant monomers, however is not wholly conclusive given the limited number of

concentration points tested and subtlety of the inflection. The large molecular mass of the

AZF fluorosurfactant may imply a different CMC and micelle structure when compared to

the conventional low molecular weight surfactant molecules234,235.

138

Figure 54. Plot of kcps vs. concentration reveals a knee point between 0.5 %w/w and 0.75 %w/w. If not the

CMC, this may be a concentration where other surfactant molecule aggregation events have occurred giving

rise to a change in the number of scattering events observed at 90°.

4.1 Summary & Conclusions

4.1.1 Droplet Formation and Linearity

In respect of droplet formation, several of the oils tested showed potential to readily form

droplets in the T-junction chip described. Fluids having a degree of polarity such as octanol

and olive oil were able to form droplets readily without the addition of surfactant, whereas

dodecane and DC200 silicone oil either did not produce droplets or were erratic at best under

the conditions tested. Fluorous oils required the addition of surfactant to yield very

reproducible droplets.

y = 90.673x - 2.179

R² = 0.9997

y = 59.159x + 6.0182

R² = 0.9976

-100

-50

0

50

100

150

200

250

300

350

400

-1 0 1 2 3 4

Par

ticl

e E

ven

ts (

kcp

s)

AZF concentration (% w/w)

upper

lower

Linear (upper)

Linear (lower)

139

When testing standard CHC solutions, variability of the droplet signals obtained were

acceptable being at approximately < 10 % and only increasing above this value at lower

concentrations, which was expected from a decreased signal-to-noise ratio. For each CHC

solution tested, the concentration in each droplet had to be equal, thus the variation seen must

be attributable to subtle changes in droplet dimensions and relative position of the droplet in

the channel, or fluctuations in the signal acquired from the PMT. Analysis of the data

suggested that the chip construction and fluid delivery methods outlined in the experimental

are appropriate for conducting the PoC droplet microfluidics described in this project.

4.1.2 Dual Aqueous Input Electro-fusion

The chip was able to produce regular reproducible droplets alternatively from each aqueous

input, however all attempts to merge using an applied voltage did not result in droplet-pair

coalescence. This was thought to be due to either over-stabilisation with surfactant or that

insufficient electrical charge was imparted to the droplets. As in situ droplet merging may

add to the overall variability, it was decided this approach should not be pursued further and

rather focus effort on adding multiple reagents to the chip via multi-junction channels

allowing mixing of reagents immediately prior to droplet generation.

4.1.3 Fluorosurfactant CMC Determination

Using DLS to obtain an accurate measure of the CMC for the custom fluorosurfactant proved

challenging. Results possibly revealed an aggregation event, evidenced as a change in the

slope of the count to concentration plot (Figure 54) occurring between 0.5 % w/w and 0.75 %

w/w AZF. However, the deflection is very small and could be due to artefacts other than the

140

formation of micelles. Previous work has used PFPE type fluorosurfactants at 1–2% w/w176.

If the data presented here correctly identified the CMC, this would imply the use of AZF at a

concentration above the surfactant CMC when added to the fluorous oil at 2% w/w. In this

case, large numbers of micelles and/or aggregate structures could exist in the oil phase. As

discussed in the next section, large numbers of micelles could be a possible explanation for

observed partitioning, for reasons similar to those reported by Baret62, Chen59, Courtois60 and

Debon63.

141

5 Results and Discussion: COMPOUND PARTITIONING

This chapter relates to the issue of material leakage from the aqueous droplet to carrier phase

discussed from pg. 27 and describes how this impacts on the miniaturisation of the P450

cytochrome inhibition assay explored in a later section. For a droplet microfluidic screening

assay to be successful and quantifiably useful, it is essential any loss of reagent from one

phase to another is either mitigated through prevention or by understanding the rate or finite

amount that may be lost over a known period of time under known assay conditions.

The following four sections consider the results from; (i) glass vial tests for a range of

compounds in different oils; (ii) the same range of compounds using fluorous oil with and

without surfactant, and; (iii) a relative comparison of the partitioning kinetics observed for

the same compounds using the oils with the greatest and lowest extent of partitioning.

5.0.1 Partitioning from Aqueous Phase to Oil Phase (Glass Vial Tests)

For each of the nine oils tested, the percentage loss was calculated based on normalisation of

the aqueous sample against the initial absorbance data collected for the drug tested.

The percentage loss from the aqueous to oil phase seen across an unstirred interface for each

of the eight drugs tested in different oils was recorded (Table 19). Darker shading represents

a higher percentage loss from the aqueous droplet to oil.

Partitioning was observed to generally increase as the lipophilicity of the compound and

polarity of oil used increased. Thus partitioning was thus seen to be more significant for

compounds having higher logD and when octanol (relatively high polarity) was used as the

oil phase. Fluorocarbon oils showed the lowest partitioning, which being highly hydro- and

oleo- phobic, it was expected that materials would not readily dissolve in these oils.

Compound LogD was observed to have no appreciable effect on partitioning into the fluorous

142

phase. FC-70 marginally demonstrated the lowest partitioning for any of the compounds

tested.

Table 20 details the results for the eight drugs tested in FC-70 using either PFO (10% v/v) or

AZF fluorosurfactant (2% w/w) with a control of neat FC-70 (15-minute data available only

due to lack of samples at the time of assay). Similar data highlighting the loss to the oil phase

was obtained using both surfactants individually and it is notable that basic drugs were

affected more than acidic compounds. Of the compounds tested, none were shown to

partition readily to the fluorous phase alone, which was expected due to the hydro- and lipo-

phobic nature of fluorocarbon fluids. However, for some compounds, particularly those that

remain largely unionised at pH 7.4, a substantial increase in partitioning was observed when

surfactant was added to the oil phase. This observation suggested that transport mechanisms

other than free diffusion could account for the passage of material from aqueous to oil phase,

as has been discussed in respect of droplet leakage work by Chen59, Courtois60 and Baret62.

From the first test in this section, partitioning was greatest with octanol, and the lowest with

FC-70 for most of the compound set. Figure 55 depicts the relative rate of loss from the

aqueous phase to FC-70 and octanol. Data is normalised to the t0 concentration in the

original aqueous samples prior to testing. In the case where total partitioning to the oil phase

is seen to be substantial, the initial fast rate of partitioning slows rapidly which may be due to

the concentration of material on either side of the liquid-liquid interface approaching

equilibrium.

This data is relevant for a number of key reasons. Although partitioning is seen be rapid for

some compounds in some oils, assuming chemical reactivity is not a problem and droplet

formation is reproducible, other oils can still be used for droplet studies. In some cases, such

as designing microfluidic logD assays where movement between phases would be required,

partitioning could be used to advantage.

143

Table 20. % Loss of each drug from the aqueous phase to oil phase with and without PFO and AZF surfactants

present over a total of 45 minutes for a static planar interface. pKa data from www.drugbank.ca.

% Loss to oil (+ surfactant)

Ion

Class

pKa time FC-70 + 10% PFO FC-70 + AZF (2% w/w)

FC-70

alone

chlorpromazine B 9.2 15 55 57 3

45 65 68 -

trazodone B 7.09 15 9 13 11

45 16 30 -

imipramine B 9.2 15 10 15 0

45 19 27 -

lidocaine B 7.75 15 10 7 0

45 32 20 -

3,5-DCP N 8.27 15 81 81 0

45 82 83 -

indomethacin A 3.8 15 4 4 4

45 5 6 -

tolbutamide A 4.33 15 0 0 0

45 0 0 -

salicylic acid A 2.97 15 0 0 0

45 0 0 -

The increase in partitioning observed for some compounds when using fluorosurfactants in a

fluorous oil (necessary to produce stable droplets as seen in chapter 4) presents a challenge of

how best to mitigate the effect. Furthermore, the small set of compounds used in the tests

reported thus far, whilst offering some chemical diversity, are not wholly representative of

the vast range of chemical diversity that would be encountered in drug discovery screening.

144

Table 19. Static interface partitioning percentage loss from the aqueous phase determined by absorbance for a range of compounds where no surfactant is present in the oil

phase. Ion is B = base, N = neutral and A = acid. Data is normalized to t0 absorbance. Darker shade relates to a higher percentage loss from the aqueous phase. Included

is comparative data for FC-70 where a pronounced increase in partitioning of basic compounds was seen when using surfactants in the oil phase. 15, 30 and 60 minute time

point data is shown. Each drug was tested in each oil once. For surfactant tests, the time points were at 15 minutes and 45 minutes.

% Loss to oil (no surfactant)

10%

PFO

2% w/w

AZF

Ion

Log

D

Time

(mins) DOD OCT PDMS

Olive

oil

PFP-

HP PFD

HDFD-

MCH FC-40 FC-70 FC-70 FC-70

Chlor-

promazine B 3.36

15 51 70 32 70 0 14 15 13 3 55 57

30 60 80 39 77 13 17 17 14 9 65 68

60 72 89 53 79 25 23 25 17 13

Trazodone B 2.64

15 21 71 7 25 17 6 6 5 11 9 13

30 38 73 10 29 16 6 6 6 15 16 30

60 53 79 24 47 24 7 6 6 4

Imipramine B 2.49

15 30 53 10 20 17 6 2 3 0 10 15

30 39 67 13 35 13 6 2 3 0 19 27

60 44 85 41 64 0 7 2 4 8

Lidocaine B 1.62

15 0 0 0 0 2 1 0 0 0 10 7

30 0 0 0 0 0 0 0 0 0 32 20

60 1 1 0 0 1 0 0 0 0

3,5-DCP N 3.58

15 11 79 12 63 32 2 0 0 0 81 81

30 19 88 28 76 34 3 0 2 0 82 83

60 33 91 55 78 32 5 0 4 3

Indomethacin A 0.95

15 3 49 3 4 1 5 5 4 4 4 4

30 3 61 3 7 5 4 6 4 9 5 6

60 2 84 3 11 10 5 5 5 13

Tolbutamide A 0.43

15 0 1 0 0 6 0 1 0 0 0 0

30 1 1 0 0 0 1 0 1 0 0 0

60 1 2 0 0 0 - 0 0 -

Salicyclic

acid A -1.43

15 0 0 0 0 0 2 0 0 0 0 0

30 0 0 0 0 2 1 0 0 0 0 0

60 0 0 0 0 - 1 0 0 0

145

Figure 55. Rate of loss from the t0 drug solutions in octanol and FC-70 oil phases (no fluorosurfactant).

Partitioning is observed to be greater for most of the compounds tested when using octanol, possibly resulting

from higher phase polarity and the molecular ionisation state of the compound in question. Where partitioning

is observed to be substantial, the partitioning rate is initially fast, appearing to reduce over later time points.

This is thought to be due to a reduction in free diffusion across the liquid-liquid interface as the drug

concentrations on either side of the boundary move towards equilibrium.

0

40

80

120

0 15 30 45 60

% t

0

Timepoint (mins)

Chlorpromazine

Octanol

FC-70 0

40

80

120

0 15 30 45 60

% t

0

Timepoint (mins)

Imipramine

Octanol

FC-70

0

40

80

120

0 15 30 45 60

% t

0

Timepoint (mins)

Trazodone

Octanol

FC-70 0

40

80

120

0 15 30 45 60

% t

0

Timepoint (mins)

Dichlorophenol

Octanol

FC-70

0

40

80

120

0 15 30 45 60

% t

0

Timepoint (mins)

Indomethacin

Octanol

FC-70 0

40

80

120

0 15 30 45 60

% t

0

Timepoint (mins)

Salicylic acid

Octanol

FC-70

0

40

80

120

0 15 30 45 60

% t

0

Timepoint (mins)

Lidocaine

Octanol

FC-70 0

40

80

120

0 15 30 45 60

% t

0

Timepoint (mins)

Tolbutamide

Octanol

FC-70

146

5.0.2 AstraZeneca (AZ) Library Compounds

This section concerns the further investigation of compound partitioning from aqueous to

fluorous oil phase for a much wider chemical diversity, randomly sub-sampled from the AZ

compound collection library. This was intended to provide a more informed view as to the

extent of partitioning that might be expected when using droplet microfluidics. The final part

of this section details the attempt to develop a model that could be used to predict for

fluorous solubility, thus providing a way to flag 'problem' compounds.

Aqueous Solubility

1406 compounds were analysed by mass spectrometry after 4 hours room temperature

incubation over the range 20 to 100 µM in PBS pH7.4. By comparing the integrated peak

response data, 347 were found to have responses suggesting the concentration had reduced to

below 80% of the expected value and were thus flagged as Not Soluble ('NS'). These

compounds were removed from subsequent partitioning experiments as any decrease in

concentration observed in the aqueous phase would not necessarily be a cause of partitioning

in the aqueous phase, but may be wholly or in part due to the compound coming out of

solution during the experiment.

The remaining 1054 compounds were flagged as soluble to 100 µM and re-ordered from the

AZ compound collection library for the round 1 and round 2 tests.

5.0.3 Partitioning - Round 1 & Round 2

Data were imported to Spotfire 6.5 (Tibco, Boston, MA) and plotted according to cut-off

values where < 20 % was categorised as Non-Partitioning in the fluorous phase; > 20 < 70 %

147

was Partially-Partitioning in the fluorous phase and > 70 % Partitioning in the fluorous

phase. To assess the extent of partitioning, all data was normalised against the time = 0 days

(t0) sample response values and converted to a percentage uptake in the fluorous phase. The

t0 samples to be used in analysis of the time = 2 day (t02) and time = 4 day (t04)) were

compared and the average of these used if the t02 and t04 data were within ± 20 % CV. This

variability level was in recognition that mass spectrometry may have a drift in response over

a series of analyses when not using an internal standard to control the data.

Where t02 and t04 data varied greater than 20 % CV, the samples were excluded from the

final data set to remove ambiguity.

Of the 1054 compounds tested in Round 1, a number of failed injections, or no spectra found

at the specified mass (thermal lability resulting in possible degradation during assay, or

molecular fragmentation during analysis) were excluded in the reported data (autosampler

blockages, etc.),. Data deviating more than more than –20 % in the % t4 fluorous data were

also excluded from the reported set. These data may again have been due to compound

degradation and/or insolubility in the HPLC mobile phase.

Data for 475 valid sample injections were reported. For t2 and t4 samples, the percentage in

the fluorous phase was calculated from the t0 normalised data. Of the t2 data, 1% of the

compound set partitioned >70 %. This data is represented by the green set in Figure 57.

Comparing the same compounds at the later t4 point (Figure 58) showed that fewer of these

compounds remained at >70 % which may be a consequence of either a shift in the

equilibrium of solubility between aqueous and oil phase, or due to random experimental

error. The variability of MS analysis peak area quantification17 can vary by as much as

approximately ± 20 % which may be enough to result in reclassification of compounds lying

near either the 20 % or 70 % cut-off boundaries.

148

A more pronounced effect on partitioning was seen when comparing the t2 and t4 data where

AZF (2% w/w) was added to the oil phase (Figures 59 and 60). This is consistent with

reported work previously discussed, where fluorosurfactants are likely to increase leakage

from the aqueous phase via a number of mechanisms.

A number of plots are presented in Figure 56A–C derived from analysis of the t2 data which

attempt to correlate the percentage in fluorous phase with a number of key compound

parameters obtained from the AZ compound database. There was no immediately obvious

correlation between the observed partitioning result and compound property. That said, as

most compounds did not appear to partition, even when using fluorosurfactant, it was

difficult to derive a large enough population of compounds soluble in the fluorous phase that

would allow any emerging trend or subtle correlation to be deduced.

As very good miscibility was seen between different fluorous oils, such as PFO and FC-70 in

the earlier oil tests, it was proposed that compounds containing fluorine may partition more

readily to the oil phase through increased solubility from strong non-covalent fluorine-

fluorine (F-F) interaction. However, as the plot of percentage compound in the fluorous phase

against the number of fluorine atoms shows (Figure 56h), there was little correlation

supporting this hypothesis. Only 3 compounds out of 475 had 4 or 5 fluorine atoms. Gladysz

et al.236 have reported it is likely that greater solubility in fluorous phases would occur with a

higher level of overall molecular fluorination to result in sufficient F-F interaction.

149

Figure 56A. Poor correlation seen for 475 compounds between solubility in the fluorous phase and; (a)

molecular weight; (b) polar surface area and (c) number of rotatable bonds.

0

100

200

300

400

500

600

-40.000

-20.000

0.000

20.000

40.000

60.000

80.000

100.000

0 50 100 150 200 250 300 350 400 450 500 Mo

lecu

lar

Wei

ght

% I

n F

luo

rous

Phas

e

Compound Index

Correlation Percentage In Fluorous Phase vs. Molecular Weight

% In Fluorous Phase 2 days Molecular Weight

0

50

100

150

200

-40.000

-20.000

0.000

20.000

40.000

60.000

80.000

100.000

0 50 100 150 200 250 300 350 400 450 500

Mo

lecu

lar

Po

lar

Surf

ace

Are

a

% I

n F

luo

rous

Phas

e

Compound Index

Correlation Percentage In Fluorous Phase vs. Polar Surface Area

% In Fluorous Phase 2 days Molecular Polar Surface Area

0

2

4

6

8

10

12

14

16

-40.000

-20.000

0.000

20.000

40.000

60.000

80.000

100.000

0 100 200 300 400 500 # R

ota

tab

le B

ond

s

% I

n F

luo

rous

Phas

e

Compound Index

Correlation Percentage In Fluorous Phase vs. # Rotatable Bonds

% In Fluorous Phase 2 days # Rotatable bonds

150

Figure 56B. Poor correlation seen for 475 compounds between solubility in the fluorous phase and; (d) number

of H-donors; (e) number of H-acceptors and (f) logD.

0

1

2

3

4

5

6

7

-40.000

-20.000

0.000

20.000

40.000

60.000

80.000

100.000

0 50 100 150 200 250 300 350 400 450 500

# H

-do

no

rs

% I

n F

luro

us

Phas

e

Compound Index

Correlation Percentage In Fluorous Phase vs. # H-Donors

% In Fluorous Phase 2 days H Donors

0

2

4

6

8

10

12

14

-40.000

-20.000

0.000

20.000

40.000

60.000

80.000

100.000

0 50 100 150 200 250 300 350 400 450 500

# H

-acc

epto

rs

% I

n F

luo

rous

Phas

e

Compound Index

Correlation Percentage In Fluorous Phase vs. # H-Acceptors

% In Fluorous Phase 2 days H Acceptors

-4

-2

0

2

4

6

-40.000

-20.000

0.000

20.000

40.000

60.000

80.000

100.000

0 50 100 150 200 250 300 350 400 450 500

Lo

gD

% I

n F

luo

rous

Phas

e

Compound Index

Correlation Percentage In Fluorous vs. logD

% In Fluorous Phase 2 days LogD

151

Figure 56C. Poor correlation seen for 475 compounds between solubility in the fluorous phase and; (g) ClogP

and (h) number of fluorine atoms.

-2

-1

0

1

2

3

4

5

6

7

-40.000

-20.000

0.000

20.000

40.000

60.000

80.000

100.000

0 50 100 150 200 250 300 350 400 450 500

CL

ogP

% I

n F

luo

rous

Phas

eCorrelation Percentage In Fluorous vs. CLogP

% In Fluorous Phase 2 days C LogP

0

1

2

3

4

5

6

-40.000

-20.000

0.000

20.000

40.000

60.000

80.000

100.000

0 50 100 150 200 250 300 350 400 450 500

# F

luo

rine

Ato

ms

% I

n F

luo

rous

Phas

e

Compound Index

Correlation Percentage In Fluorous vs. # Fluorine Atoms

% In Fluorous Phase 2 days # Fluorine Atoms

152

Figure 57. Percentage of compound dissolved in the fluorous oil phase after a 2 day incubation for 475 valid compound results. Error bars are ±20 %.

153

Figure 58. Percentage of compound dissolved in the fluorous oil phase after a 4 day incubation for 475 valid compound results. Error bars are ±20 %.

154

Figure 59. Percentage of compound dissolved in the fluorous oil phase in the presence of 2% w/w AZF after a 2 day incubation for 475 valid compound results. Error bars

are ±20 %.

155

Figure 60. Percentage of compound dissolved in the fluorous oil phase in the presence of 2% w/w AZF after a 4 day incubation for 475 valid compound results. Error bars

are ±20 %.

156

5.0.4 Predictive Modelling of Partitioning

From the data of Rounds 1 and 2, the Receiver Operating Characteristic (ROC) curves, as

depicted in Figure 61, suggested reasonably high levels of model accuracy may be possible,

although caution was exercised given the variability of ROC output over 5 consecutive

randomised training-test data splits (Figure 59). An ROC score of between 55% and 95%

(average ~76%) suggested there was agreement between predictions for compounds most

likely to partition and the experimental data for such compounds. Ideally, >85% would be

considered a highly accurate predictive model; however, to achieve this value would most

likely require a much larger total set of data, which was not possible because of resource

constraints at the time of investigation. The output from the Bayesian model training was

used in designing Round 3.

5.0.5 Partitioning - Round 3

Applying the Bayesian FCFP model to the 2 million compounds in the AZ compound library

yielded a list of these compounds arranged in order of predicted fluorous solubility. 192

compounds were selected from the top and 192 from the bottom of the list and tested in

Round 3. Of the 192 compounds selected from the top of the list, 187 provided valid

experimental results, however, of these, only 9 were found experimentally to partition >20%

to the fluorous phase and be predicted to be in this category by the model, whereas the

remaining 178 also predicted to be in the partitioning category were found experimentally to

be distributed over the –20 to 20 % non-partitioning range. The data was also tested for the

reverse case examining the model for the compounds tested that were expected to be in the

157

non-partitioning category. From this, 5 compounds were experimentally found to partition

>20 % and thus reside in the partitioning category.

Figure 61. ROC plot. A value of greater than 0.8 (80 %) may be considered an accurate model prediction

indicating a low false positive rate. Randomised training-test set splits of the data from round 1 and 2 was used

to generate 5 different iterations of ROC calculation.

From this, it is reasonable to suggest that the model accuracy was actually rather misleading

and supports the hypothesis the model was unable to resolve the connection between an

observed result and the physicochemical properties underlying the cause of the observation.

158

Figure 62. (a) Predictive accuracy of the model is indicated by plotting the false positive rate (x-axis) against the true positive rate (y- axis). 0.5 corresponds to 50% which is

no better than the chance of tossing a coin; that is, the model has no predictive ability to determine the outcome of an event having two possible outcomes.

159

5.0.6 Partitioning from Droplets to Carrier Oil in Droplet Chip

To this point, the results obtained from the various partitioning experiments have studied

leakage from the aqueous to oil phase in simple models with a planar interface between the

two phases, rather than partitioning in situ from the droplet to oil phase. The following

section describes the results obtained from the attempt to determine partitioning from the

droplet in situ using the same eight compounds as were initially tested in the glass vial

partitioning experiment.

The percentage loss of drug from the droplet aqueous phase to the carrier oil was substantial,

especially for compounds of base chemistry (Table 21), which is largely consistent with data

obtained in the glass vial test. However, little difference was seen between the data sets for

the T-junction and Spiral chips. This was surprising as it was expected that a quantifiable

difference would be seen for compounds run in the T-junction chip (droplets had effectively

0 minute on-chip residence time) compared to those run in the Spiral chip (droplets had

13.5 minutes on-chip residence time). Two explanations can possibly explain this

observation; i) the high degrees of partitioning observed may not be predominantly due to the

residence time in the droplet chip environment, but rather may be more influenced by the

physical change of environment during the 1.5-minute collection period. In this part of the

experiment, droplets exiting the chip move from a laminar, low-turbulence condition with

low shear force interaction to a highly turbulent state during chip outflow to the collection

vessel which may exacerbate the diffusion of material to the oil phase, or; ii) due to the much

higher surface area to volume ratio in the droplet environment, for materials with a

propensity to partition, the rate of diffusion may be so fast that an almost instant loss is

observed, exacerbated by the turbulent mixing during collection.

160

Table 21. Summary of percentage drug loss from the aqueous phase after 0 and 13.5 minutes on-chip residence

time in the droplet environment using FC-70-AZF carrier oil. Losses are found to be significant for both t0 and

t13.5 chips suggesting that a larger extent of partitioning may be occurring during the 1.5 minute collection

period. This may be a cause of the high turbulence giving greater mixing and promotion of diffusion to the oil

phase.

Compound % loss (t0 chip) % loss (t13.5 chip)

Chlorpromazine 96 96

Imipramine 95 95

3,5-DCP 67 79

Trazodone 64 75

Indomethacin 27 25

Salicylic acid 7 0

Lidocaine 0 23

Tolbutamide 0 0

5.1 Summary & Conclusions

5.1.1 Partitioning - Glass Vial Tests

Results from the static interface partitioning experiments indicate that fluorocarbon oils

perform well with respect to low levels of partitioning across a non-stirred liquid interface for

neutral and acid and basic compounds, which may be expected for a hydro- and oleo-phobic

organic phase. However, when surfactants are added to the same static interface tests, a

pronounced increase in partitioning is observed. This is most likely as a result of increased

mass transport via micelle formation whereby the drug can be rendered more soluble in the

fluorous phase4. The level of partitioning observed when using surfactants is alarmingly high

at greater than 50 % loss to the oil phase for FC-70, however the selection of compounds

tested is small and only represents a limited chemistry. A much wider set of data would be

required to more fully understand how significant partitioning in the presence of surfactant is

and how this may impact on either the P450 inhibition assay objective in this project, or

indeed on the wider appeal for droplet microfluidic drug discovery screening.

161

5.1.2 Partitioning - Droplet Chip Tests

In the earlier droplet formation tests using fluorous oil carrier phases, it was established that

when the oils were tested in their pure form droplets were not produced under the conditions

of channel dimensions and flow rates described. The droplet chip-based partitioning

experiments therefore had to be explored using a surfactant. The results from the chip-based

tests reveal that partitioning can be very high, especially for basic compounds, with a rank

order similar to that for the eight drugs tested in the glass vial method. Furthermore, the

results indicate that a majority of partitioning occurred either in the droplet collection period,

or is occurring rapidly after droplet generation. This is broadly in agreement with the large

partitioning seen in the glass vial tests when using surfactant.

The chip-based method employed here was unable to distinguish clear differences of

partitioning based solely on chip residence times, suggesting a more robust method was

needed to evaluate loss of compound from droplets in situ.

5.1.3 Partitioning - AZ Library Compounds & Predictive Modelling

The results from testing ~2000 AZ library collection compounds of varied chemistry (not

disclosed for IP reasons) indicated that very few compounds tended to partition from the

aqueous phase to fluorous phase in the absence of surfactant and when AZF surfactant was

added to the oil, although partitioning was seen to increase overall, the number of compounds

moving from a low % in fluorous phase category to a high % category remained fairly low.

Over the broader range of chemistry tested, fewer drug-like compounds were found to

partition in the fluorous oil phase than were first expected based on the evidence of the glass

vial and droplet chip partitioning test, however, the impact of surface area-to-volume ratio on

162

partitioning especially in the presence of fluorosurfactants would require further investigation

to determine a more realistic outcome.

Correlation of known physicochemical properties to the extent of fluorous solubility was poor

and did not reveal an obvious combination of physicochemical properties that described

fluorous solubility. This, and the low number of compounds in the fluorous soluble set can

also explain the failure of the model to accurately predict partitioning to the fluorous phase.

163

6 Results and Discussion: CYTOCHROME P450 REACTION

In this section, the results from the microtitre plate cytochrome P450 inhibition experiment

are reported. This provided an essential 'gold standard' to which the droplet microfluidic

assay results, described in chapter 7, were compared. The data includes how the enzyme

reaction rate varied with temperature and how linear the reaction is over the incubation. Data

was compared to a calibration curve which enabled calculation of the concentration of

fluorescent metabolite produced during the reaction.

6.0.1 Microtitre Plate: Reaction Dependence on Temperature

As seen by the reaction profiles in Figure 62, over eight replicates, variability was well

controlled and a substantial increase in reaction velocity was obtained at higher temperatures.

The reaction proceeded fastest, producing the greatest signal, at 37 °C, which is consistent

with reports in the literature discussed in Chapter 1. Notably, regarding the 37 °C plot in

Figure 63, although variation between the individual replicates was acceptably low at less

than ~10 % variance (Table 22), there was a pronounced shift in reaction rate, as evidenced

by a change in the plot profile. At the beginning of reaction, the slightly slower rate is

probably due to the addition of ice-cold NADPH cooling the incubate briefly, resulting in an

initially slower rate of reaction. This is quickly recovered as the well contents return to the

correct incubation temperature. Towards the end of the 20-minute incubation, the rate was

observed to decrease again slightly, either as a result of the beginning of reagent depletion or

the effect of product formation on the reaction rate (deviation from pseudo-first order

reaction kinetics).

164

Figure 62. Average signal data plot for 1A2 enzyme incubated with CEC at different temperatures. Higher

temperatures yield a significantly higher rate of reaction and thus larger signal window after 20 minutes. All

four temperatures provided very good to excellent linearity over 20 minutes incubation. Error bars are 7%.

From the data summarised in Table 22 and plots in Figure 63, 34 °C offered the best

compromise between good signal linearity and maximum signal. There was very little

evidence of a slower rate at either the beginning or end of the 20 minute incubation.

In the next section, the results for a further investigation are described where the temperature

of a repeat experiment was set to 34 °C and cross-reference to a CHC standard curve is

employed to determine the concentration of fluorescent metabolite that was produced by the

enzyme reaction.

R² = 0.99

R² = 0.99

R² = 0.99

R² = 0.99

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

0 5 10 15 20

RF

U

Incubation Time (mins)

1A2-CEC Reaction Rate Dependence on Temperature

Avg. Blank - 22 degs Avg. Signal - 22 degs

Avg. Blank - 25 degs Avg. Signal - 25 degs

Avg. Blank - 34 degs Avg. Signal - 34 degs

Avg. Blank - 37 degs Avg. Signal - 37 degs

Linear (Avg. Signal - 22 degs) Linear (Avg. Signal - 25 degs)

Linear (Avg. Signal - 34 degs) Linear (Avg. Signal - 37 degs)

n=8

165

Figure 63. Microtitre plate-based 1A2-CEC reaction profiles at different temperatures. Zero baseline not

shown.

R² = 0.9996

0

1000

2000

3000

4000

5000

6000

7000

0 5 10 15 20

RF

U

Time (mins)

22 °C

R² = 0.9992

0

2000

4000

6000

8000

10000

0 5 10 15 20

RF

U

Time (mins)

25 °C

R² = 0.9982

0

5000

10000

15000

0 5 10 15 20

RF

U

Time (mins)

34 °C

R² = 0.9975

0

5000

10000

15000

0 5 10 15 20

RF

U

Time (mins)

37 °C

166

Table 22. Data obtained from eight replicate incubations of P450 enzyme 1A2 with CEC at 22, 25, 34 and 37

°C. Final incubation concentrations are; 1A2 enzyme=10 nM; CEC=3 µM; NADPH=250 µM. Variance

across the data set is acceptably low and comparable to data seen previously17.

22 °C

25 °C

34 °C

37 °C

Time

(mins)

Avg.

(n=8) SD %

CV

Avg.

(n=8) SD %

CV

Avg.

(n=8) SD %C

V

Avg.

(n=8) SD %

CV

0 379 21 6

389 46 12

503 26 5

509 32 6

1 602 26 4

697 42 6

924 44 5

1025 33 3

2 857 36 4

1038 56 5

1427 68 5

1657 71 4

3 1128 45 4

1396 60 4

1967 106 5

2353 99 4

4 1402 58 4

1752 88 5

2546 145 6

3096 139 4

5 1693 63 4

2130 111 5

3173 209 7

3913 186 5

6 1969 78 4

2524 141 6

3831 229 6

4791 229 5

7 2269 78 3

2934 165 6

4525 289 6

5684 268 5

8 2566 79 3

3351 184 5

5233 327 6

6575 338 5

9 2852 97 3

3782 211 6

5973 361 6

7541 338 4

10 3164 112 4

4208 208 5

6694 394 6

8453 394 5

11 3469 118 3

4635 231 5

7414 447 6

9347 385 4

12 3774 128 3

5067 248 5

8162 444 5

10220 418 4

13 4088 136 3

5487 271 5

8874 475 5

11097 466 4

14 4381 128 3

5904 283 5

9620 501 5

11955 457 4

15 4666 150 3

6309 301 5

10314 499 5

12705 494 4

16 4960 146 3

6733 292 4

10983 504 5

13460 444 3

17 5276 166 3

7121 300 4

11686 482 4

14170 444 3

18 5546 177 3

7529 329 4

12322 532 4

14815 444 3

19 5826 163 3

7933 324 4

12963 524 4

15438 444 3

20 6122 175 3

8320 351 4

13579 502 4

16035 444 3

6.0.2 Microtitre Plate: Linearity at 34°C and Standard CHC Curve

The microtitre plate data from reaction at 34 °C was plotted against a CHC standard curve

over the range 0 to 1 µM (Table 23). The standard curve allowed calculation of CHC

metabolite concentration across the 20 minute incubation. This information was then used as

a 'gold standard' benchmark to which the droplet chip based experiments were compared.

167

Figure 64. CHC standard curve replicate measurements (n=8) show excellent correlation over the

concentration range studied (~5 % CV, see Table 22). As highlighted above, reaction at 6 minutes and 13.5

minutes were noted in particular as these incubation times were equivalent to the design of droplet chips to be

tested.

Using the linear fit equation from Figure 62 (Equation (30)), the RFU values obtained at 6

minutes and 13.5 minutes were calculated.

𝑦 = 680.7𝑥 − 11.14 (30)

where x is the time point and y the RFU value obtained.

From this, it follows that:

RFU at 6 minutes = 4073.06

RFU at 13.5 minutes = 9178.31

These values were then applied to the rearranged linear fit equation in Figure 65 (Equation

(31)) to calculate the concentration of CHC metabolite corresponding to the RFU value.

168

Table 23. Four replicate standard curve measurements of CHC metabolite.

Concn.

(µM) Rep 1 Rep 2 Rep 3 Rep 4 Average SD %CV

0 66 19 42 25 38 21 55.4*

0.03 2001 2613 2659 2498 2443 302 12.4

0.1 5063 5054 5142 5211 5118 74 1.4

0.3 14790 15131 14732 15025 14920 190 1.3

1 45317 44725 45664 44512 45055 530 1.2

*background noise

Figure 65. CHC standard curve replicate measurements (n=8) show very good correlation over the

concentration range studied.

x = 𝑦−788

44499 (31)

where x is the CHC concentration (µM) and y the input RFU value.

From this, it follows using calculated data from Equation 31:

At 6 minutes, concentration of CHC = 0.07 µM

At 13.5 minutes, concentration of CHC = 0.19 µM

y = 44499x + 788.01

R² = 0.999

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

0 0.2 0.4 0.6 0.8 1 1.2

CHC Calibration Curve

CHC Calibration

Curve

Linear (CHC

Calibration Curve)

169

From literature data17,187,188, we find that the concentration of fluorescent metabolite produced

in the reaction is similar to that expected for an enzyme catalysed reaction of this type. In

metabolic terms, 0.19 µM product formation at 6 minutes from the starting substrate

concentration of 3 µM is a metabolic 'turnover' of 2.3 % and that at 13.5 minutes, 6.3 %,

again consistent with data seen in this types of assay17.

In the next chapter, the results obtained from the equivalent CEC reaction in the droplet chip

are presented and discussed, using the microtitre plate data as a guide to how well the chip

method compares.

6.1 Summary & Conclusions

6.1.1 Reaction Dependence on Temperature & Linearity of Reaction

Of the four temperatures tested, 34 °C offered the best compromise between reaction velocity

and linearity over a 20-minute reaction incubation. Linearity of reaction is critical to ensure

consistent data and offers a much easier route to calculate unknown concentrations of

reaction product using linear regression. Reproducibility at 34 °C for 8 replicate incubations

was well controlled at ~5 % CV across the 20-minute reaction time.

6.1.2 CHC Standard Curve

Repeated incubations at four concentrations over 0.03 µM to 1 µM covered the expected

range of metabolite expected to be produced (based on literature data) from a droplet P450

enzyme inhibition reaction of this type. This assumed the assay in the droplet format worked

and scaled in a ratio-metric manner, that is, reaction velocities were consistent and did not

change with volume or changes in the surface area-to-volume ratio. The CV obtained was

170

very well controlled at ~1.5 % for the majority of the range increasing to only ~12.5 % at

0.03 µM. There was no evidence of non-linear response in the plate reader data.

171

7 Results and Discussion: SPIRAL INCUBATION CHIP

This chapter concerns the results obtained from the initial attempt to scale-down the

cytochrome P450 inhibition assay to the droplet format. A standard curve profile was

initially generated using solutions of the fluorescent metabolite across the expected

concentration range as determined by the previous plate-based experiments.

7.0.1 Droplet Chip: CHC Standard Curve

The data collected from the CHC standard curve showed very good linearity across the

concentration range tested (0.03 to 3 µM), however, the lowest concentration was found to be

very close to the limit of detection with a considerable degree of noise masking the droplet

peak data. Equations 32 and 33 describe the limit of blank and limit of detection,

respectively.

𝐿𝑂𝐵 = 𝑏𝑙𝑎𝑛𝑘̅̅ ̅̅ ̅̅ ̅̅ + 𝑏𝑙𝑎𝑛𝑘𝑆𝐷 (32)

where LOB is the 'Limit of Blank' described by the sum of the mean blank value and standard deviation

of the blank.

𝐿𝑂𝐷 = 𝐿𝑂𝐵 + [𝐴𝑛𝑎𝑙𝑦𝑡𝑒]𝑙𝑜𝑤𝑆𝐷 (33)

where LOD is the 'Limit of Detection' described by the sum of the limit of blank and the standard

deviation of the lowest analyte concentration.

From the CHC droplet data obtained using the Spiral chip, the LOB was calculated using

Equation 32 to be equivalent to a peak height response of 0.074 V. From this, the calculated

LOD was calculated using Equation 33 to be a peak height response of 0.171 V, equivalent to

an analyte concentration of between 0.3 and 0.4 µM. There are random noise spikes in the

172

baseline which most likely explain this higher LOD. In practice, it was found possible to

reasonably discriminate the droplet signal as low as 0.1 µM as shown by the raw data plots in

Figure 65.

Figure 66. Linearity plot of the four CHC concentrations. Quantified CHC droplets show good correlation

across the range studied. Linear fit is forced through zero.

Plotting the log-transformed peak area and height data in Table 23, yielded a linear fit,

suggesting that droplet signal response was directly proportional to droplet analyte

concentration (Figure 66). As seen before in previous droplet formation tests, droplet height

was seen to have the lower measurement variability, consistent with the expectation that

droplet size and position in the channel during detection will tend to vary more than droplet

analyte concentration. Reference to chapter 4 will confer the relative stability of droplet

analyte concentration.

y = 191.84x

R² = 0.9975

y = 0.4959x

R² = 0.9988

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

0 0.5 1 1.5 2 2.5 3 3.5

Pea

k H

eight

(V)

Pea

k A

rea

Concentration (µM)

Droplet Chip: CHC Linearity (Peak Area and Height)

area

height

Linear (area)

Linear (height)

173

Table 24. Raw data of CHC droplets for the useable range over 0.1 to3 µM. Peak data at 0.03 µM was

essentially indistinguishable from baseline noise and excluded from the data set.

3 µM

1 µM

Peak Area Height HalfWidth

Peak Area Height HalfWidth

1 540.50 1.48 359

1 200.21 0.50 403

2 553.44 1.49 366

2 190.73 0.50 379

3 546.15 1.48 368

3 195.94 0.51 395

4 578.36 1.48 388

4 192.89 0.50 391

5 573.88 1.48 384

5 202.41 0.49 389

6 588.62 1.49 392

6 200.85 0.49 431

7 595.59 1.50 392

7 197.82 0.49 404

8 601.12 1.49 400

8 200.47 0.50 404

9 588.34 1.49 396

9 211.71 0.50 432

10 564.29 1.50 376

10 195.35 0.49 392

11 579.00 1.50 384

11 205.45 0.50 391

12 601.41 1.48 400

12 192.65 0.50 389

13 567.26 1.50 374

13 202.22 0.50 409

14 576.46 1.49 384

Avg. 575.3 1.49 383.1

Avg. 199.1 0.50 400.7

SD 19.3 0.01 12.9

SD 5.79 0.01 15.9

%CV 3.4 0.59 3.4

%CV 2.91 1.02 4.0

0.3 µM

0.1 µM

Peak Area Height HalfWidth

Peak Area Height HalfWidth

1 37.45 0.106 60

1 13.75 0.07566 0

2 37.58 0.112 65

2 16.14 0.05609 4

3 37.50 0.109 57

3 14.23 0.0563 4

4 36.52 0.106 54

4 14.70 0.04983 4

5 35.39 0.111 64

6 35.40 0.113 68

7 38.10 0.115 64

8 36.58 0.108 61

Avg. 36.82 0.11 61.6

Avg. 14.70 0.06 3

SD 1.02 0.00 4.6

SD 1.03 0.01 2

%CV 2.77 3.00 7.4

%CV 7.03 18.8 11.8

174

Figure 67. Raw data plot of CHC droplets (from 3 to 0.03 µM). Signal to noise ratio is very poor at 0.03µM

and was thus removed from the linearity plot. The data set for each concentration is truncated in this figure for

greatly clarity of peak shape against background noise.

0

1

2

0 5000 10000 15000 20000 25000 30000 35000 40000Res

po

nse

(V

)

time / ms

CHC at 3.0 µM

0

0.5

1

-2000 3000 8000 13000 18000 23000 28000 33000 38000Res

po

nse

/ V

time / ms

CHC at 1.0 uM

00.05

0.10.15

0.2

-2000 3000 8000 13000 18000 23000

Res

po

nse

/ V

time / ms

CHC at 0.3 µM

0

0.1

0.2

0 2000 4000 6000 8000 10000 12000 14000Res

po

nse

/ V

time / ms

CHC at 0.1 µM

0

0.05

0.1

0.15

0 5000 10000 15000 20000 25000 30000 35000 40000

Res

po

nse

/ V

time / ms

CHC at 0.03 µM

175

7.0.2 Droplet Chip: Cytochrome P450 Enzyme Inhibition

The evidence of quantified CHC metabolite data from the microtitre plate-based P450

enzyme assay and the agreement to this of the CHC standard curve data conducted in the

Spiral droplet chip, suggested the assay should yield reliable quantifiable data for the enzyme

assay conducted in the droplet format (Table 24 and Figure 67).

Initial attempts to deliver quantifiable 1A2-CEC reaction data from the Spiral chip were

surprisingly difficult. After a number of repeat attempts, only on one occasion was it

possible to acquire a definite signal indicating the formation of fluorescent metabolite (Figure

68).

Figure 68. 100% control activity determination by droplet chip. Each peak in the plot represents a droplet

passing the detector. The ‘twin peaks’ artefacts for each droplet may be caused by optical diffraction/reflection

attributed to the droplet leading and trailing edge causing a voltage spike in the detector as the light level

suddenly changes. Reproducibility is seen across the small sample set of 19 droplets with variance less than 9%

comparable to that seen in the microtitre plate control activity.

0 500 1000 1500 2000 2500

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Res

ponse

(V

)

Time (ms)

1A2-CEC Control Activity (no inhibitor)

176

Droplet peaks had pronounced leading and trailing edge ‘spikes’, possibly caused by spurious

diffraction of the light captured by the PMT as droplets passed through the detection zone in

the chip channel. These are not seen in the standard CHC curve tests.

The series of ES solutions containing a range of inhibitor (fluvoxamine) concentrations

yielded similarly poor results in the respect they were all exhibited the 'spike' artefact and

were not reproducible on other occasions (Figure 69).

As inhibitor concentration increased, the quantified droplet signal fluorescence decreased in

accordance with inhibition of 1A2 by the enzyme-specific inhibitor. Table 24 summarises

the average data obtained. Significant noise is apparent in the data as evidenced by the

relatively high SD and % CV when compared to the CHC standard curve previously obtained

using the droplet chip. Despite the difficulty in obtaining a whole set of data for four different

concentrations of inhibitor in the enzyme reaction (plus the control activity), Logit-Log22

pseudo-Hill regression analysis of the data (Figure 70) resulted in an apparent IC50 for

fluvoxamine that closely matched literature data within the range reported2,17,222,223 (0.008

µM to 0.06 µM), however it must be noted that the confidence of this experimental result is

low due to the low resolution of the curve (only four concentration points) and that it was not

possible to robustly reproduce the data on either the same day, or separate days. Referring to

the raw data plots of Figure 69, the high level of noise and low signal seen, suggested either

a very small amount of fluorescent metabolite was produced, or detector alignment and

sensitivity was critically important in being able to detect the signal; between experiments, it

was not always possible to guarantee consistent alignment of the detector due to removing

and replacing syringe tubing for the next concentration of reagent and also between tests

conducted on separate occasions.

177

Figure 69. Droplet data for concentrations of fluvoxamine over 0.01–0.3 µM.

0 500 1000 1500 2000 2500

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

Res

ponse

(V

)

Time (ms)

1A2-CEC with 0.01 µM fluvoxamine

0 500 1000 1500 2000 2500

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Res

ponse

(V

)

Time (ms)

1A2-CEC with 0.3 µM fluvoxamine

0 500 1000 1500 2000 2500

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

Res

ponse

(V

)

Time (ms)

1A2-CEC with 0.1 µM fluvoxamine

0 500 1000 1500 2000 2500

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Res

ponse

(V

)Time (ms)

1A2-CEC with 0.3 µM fluvoxamine

178

Table 24. Average data for the IC50 determination. Resultant IC50 falls in range of literature values.

Conc (µM)

Avg. Pk

area

SD

(area) %CV (area)

Avg.

Peak

height

SD

(height)

%CV

(height)

0 21.86 1.17 5.35 0.96 0.08 8.19

0.01 12.08 2.30 19.00 0.57 0.10 17.88

0.03 11.33 2.03 17.95 0.52 0.08 16.28

0.1 9.03 1.70 18.81 0.48 0.11 22.29

0.3 7.49 1.53 20.44 0.45 0.03 6.69

RFU µM %CA %Inh Log[Inh]

Log(CA/100-

CA)

Line

fit

7.49 0.3 34.2 65.8 -0.52 -0.28 -0.27 m = -0.26

9.03 0.1 41.2 58.8 -1.00 -0.15 -0.15 c = -0.41

11.33 0.03 51.7 48.3 -1.52 0.03 -0.01 x' = -1.56

12.08 0.01 55.2 44.8 -2.00 0.09 0.12 r2 = 0.97

IC50 0.027

µM

Figure 70. IC50 calculation using a pseudo Hill analysis Logit-Log plot. The IC50 obtained is within the

literature values for fluvoxamine.

The positional flow of droplets within the channel may randomly shift, resulting in further

observed variations in detector signal. However, discounting detector alignment issues, the

signal obtained was lower than expected (based on the results of the microtitre plate and

standard curve assessments discussed in earlier chapters). As described in the introduction,

and followed up with the work on partitioning of material from droplets, a possible

consideration is that one or more reagents from the reaction had leaked out before reaction

0.0

10.0

20.0

30.0

40.0

50.0

60.0

0.010.11

% M

axim

al r

ate

[Inhibitor] / µM

IC50 Plot

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

-2.50-2.00-1.50-1.00-0.500.00

Lo

g(C

A/1

00

-CA

)

Log[Inh]

Logit-Log (pseudo Hill) Plot

Regression

Pseudo Hill

179

and/or quantification. Other possibilities include reagent stability and also whether there is a

significant negative impact on the enzyme reaction from the miniaturisation of the assay to

droplet format. Possible causes and the resulting action taken to address the apparent lack of

signal are summarised in Table 25. Table 26 rationalises each of the hypotheses outlined in

Table 25.

Table 25. Possible causes of the very low and irreproducible signal obtained for the 1A2-CEC reaction in the

Spiral droplet chip.

(1) Enzyme activity loss over time (prior to chip injection):

Enzyme activity may have decreased over the length of time required to setup and stabilise each droplet chip

experiment, compared to the microtitre plate. Reagents were charged to the syringes for chip-based experiments

and left for 2 hours at room temperature. After this time, the syringe contents were used in the microtitre plate

experiment and data compared to that obtained in the original microtitre plate test where fresh reagents were

used.

(2) Impurities in the apparatus causing enzyme inhibition:

Impurities in the syringe and/or tubing/chip may have inhibited the enzyme decreasing apparent activity before

injection to the droplet chip.

To address this, all syringes, tubing and chip were rigorously flushed with water, ethanol and then water again to

remove any contaminants. The test was then repeated to observe any improvement.

(3) Mixing of enzyme-substrate solution with NAPDH solution during droplet formation:

Incomplete mixing of NAPDH with the enzyme-substrate solution at the point where droplets were formed may

have resulted in a delayed reaction or lack of reaction velocity. This may result in a slower reaction with lower

quantities of metabolite produced.

To address this, a stock solution of CHC (3 µM) was first quantified by flowing into both chip inlets. This was

then compared to the signal obtained when flowing phosphate buffer in one channel with CHC (3 µM) in the

other.

(4) Limit of detection too high using the apparatus described:

The detection limit for the apparatus was not low enough for the concentration of metabolite produced, hence a

low signal with poor signal to noise ratio was obtained. This may affected by incorrect alignment of the optics,

focussing of the excitation light into the channel and movement of droplets in the channel.

(5) Incubation temperature of the droplets not at the required temperature (34 °C):

The droplets in the chip channel may not have reached the required 34 °C to produce sufficient metabolite over

either the 6 or 13.5 minute incubations.

(6) Partitioning of the substrate and /or metabolite:

The CEC substrate may have partially or completely partitioned out of the droplet before any significant

reaction had occurred. Alternatively, the CHC metabolite may have partitioned out from the droplet during

incubation prior to detection. The following chapter, "Partitioning Follow-up" describes this in detail.

(7) Droplet environment not suitable for the reagents:

In this case, it was considered whether the nature of the droplet environment and concentration of reagents used

was fundamentally at fault.

180

Table 26. Results summary of the hypotheses outlined in Table 19.

(1) Enzyme activity loss over time (prior to chip injection):

After 2 hours at room temperature, enzyme-substrate and NADPH solutions were found to yield similar results

to that obtained previously in the micro titre plate experiments, thus not a likely cause of the low signal seen in

the droplet chip 1A2-CEC reaction.

(2) Impurities in the apparatus causing enzyme inhibition:

No improvement to reaction signal was seen after extensive flushing of the chip apparatus with water, ethanol

and then water again.

(3) Incomplete mixing of enzyme-substrate solution with NAPDH solution during droplet formation:

The signal obtained for droplets where CHC was flowed in one inlet and phosphate buffer in the other showed

a broadly 2-fold reduction in signal corresponding to the 1:1 dilution ratio. Although droplet signal variability

was increased compared to CHC alone, the result suggested good mixing of reagents. NADPH is used in large

excess so even incomplete mixing should still work.

(4) Limit of detection too high using the apparatus described:

The results of the droplet formation and response linearity tests indicate there should be sufficient sensitivity

from the apparatus to see CHC down to ~0.05 µM which is below the expected concentration of CHC formed

after both 13.5 and 6 minutes incubation at 34 °C.

(5) Incubation temperature of the droplets not at the required temperature (34 °C):

A rapid response thermocouple temperature probe was adhered to the top surface of the droplet chips to measure

the temperature rise and final surface temperature. From room temperature, the chip top surface was measured

at 34 ±1°C after 5 minutes. From this it was assumed that droplets in the channel would reach this temperature.

(6) Partitioning of the substrate and /or metabolite:

CEC and CHC were tested in the shake-flask method to confirm partitioning potential to the oil phase. The

results are detailed in chapter 8.

(7) Droplet environment not suitable for the reagents:

The potential for enzyme to undergo non-specific binding events or other process that may lead to inactivation

were investigated. The results are detailed in chapter 9 and 10.

Of the hypotheses considered in Table 25, (6) and (7) were considered for significant further

investigation fitting the project aims described in chapter 2 and aligning to concerns raised in

chapter 1.

To address time-dependent issues relating to either reagent degradation, partitioning or other

artefact, the inhibition tests were repeated with the shorter Serpentine chip (see chapter 9)

comparing the data against the microtitre plate data obtained at 6 minutes.

181

7.1 Summary & Conclusions

7.7.1 P450 Enzyme Reaction in Spiral Incubation Chip

The results obtained by conducting a miniaturised droplet format cytochrome P450 inhibition

assay were more challenging than expected, which was particularly surprising given the

relative simplicity of the reagent build required, biochemical reaction involved, and the

design of the Spiral reaction chip. Only one replicate set of data were obtained for the chip-

based reaction, however, despite this, the reported IC50 closely match literature data. This

provided confidence that the assay could be made to work in the droplet format, but

recognised the need to overcome the problem of low signal and reproducibility.

182

8 Results & Discussion: PARTITIONING FOLLOW-UP

This chapter reports the results from the 'shake flask' test used to investigate whether the CEC

substrate in the cytochrome P450 reaction, or CHC metabolite were likely to partition to the

fluorous oil phase. If this was found to be the case, it would provide an explanation for the

low signal seen in the previously discussed droplet-based P450 enzyme inhibition assay

attempt.

8.0.1 Shake Flask

The CHC droplet standard curve linearity test showed that over the concentration range 0.1 to

3 µM it should be possible to measure CHC production from the metabolism of CEC by the

1A2 enzyme if appropriate quantities are produced in the droplet format. If the reaction

proceeded at the same rate as in the microtitre plate, a concentration of about 0.2 µM should

be reached, quantifiable after a 13.5 minute incubation at 34 °C, as found in the microtitre

plate experiments (pg. 102). The lack of signal observed in the initial attempts of droplet

chip based 1A2-CEC reaction may have been a result of substrate and/or metabolite leaching

out from the droplet.

For the first test conducted over 5 minutes, CEC and CHC were not observed to partition

substantially to either FC-70 oil alone or FC-70 containing 2 % w/w AZF surfactant. As the

inversion process maximised the interfacial surface area between the two liquids by forming

many small droplets in an emulsion, if either CEC or CHC were prone to partitioning, a

larger loss in the aqueous phase might be observed. As shown in Figure 71, dissolution in the

oil phase was low with an absorbance measurement in the aqueous phase similar to that

183

observed from the starting stock solution, suggesting a minimal loss of either CEC or CHC to

the fluorous phase.

Figure 71. CEC substrate absorbance plot and CHC metabolite absorbance plot of aqueous phase at t=0 and

t=5 minutes, with and without AZF surfactant. In each case, t0 and t5 data overlap suggesting no loss of

material from the aqueous phase by partitioning o the oil phase.

As shown in Figure 72, the data from the second test over 20 minutes for three replicates

showed no loss from the aqueous phase for CHC with all peaks essentially equivalent if

random signal variation is ignored. The three red plots prefixed 'CHC 200 µM' refer to the

initial stock solution analysis and the other the plots prefixed 'CHC 1000 µL' refer to the

analysis of the reaction aliquots of the samples following constant inversion for 20 minutes.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

330 350 370 390

Ab

s

Wavelength (nm)

CEC (substrate)

200 µM CEC

t5 mins, no AZF

t5 mins, + 1.8 %

w/w AZF

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

320 370 420

Ab

s

Wavelength (nm)

CHC (metabolite)

200 µM CHC

t5 mins, no AZF

t5 mins, + 1.8 %

w/w AZF

184

Figure 72. Little partitioning from the aqueous phase to oil phase was observed for CHC after 20 minutes.

some analytical error is apparent as the signal at 20 minutes is reported to be slightly higher than that of the

stock 200 µM solution replicates at the maximum absorption wavelength. Labels prefixed ‘CHC 1000 µL…’

refer to the 1 mL samples that were analysed at 20 minutes.

From these data, it is reasonable to suggest that there is little of no impact of partitioning of

the metabolite from the enzyme reaction, however, due to the limited solubility of CEC in the

aqueous solution to a concentration high enough for reliable absorption spectroscopy, thus it

is not wholly conclusive evidence that no CEC is lost by partitioning during the enzyme

reaction in the droplet format.

0

0.1

0.2

0.3

0.4

0.5

0.6

220 270 320 370 420 470 520

Ab

s

Wavelength (nm)

CHC 200 µM std #1

CHC 200 µM std #2

CHC 200 µM std #3

CHC 1000 µl @ 20 mins #1

CHC 1000 µl @ 20 mins #2

CHC 1000 µl @ 20 mins #3

185

8.1 Summary & Conclusions

8.1.1 Shake Flask

The data from the shake flask experiments did not provide evidence of significant CHC

metabolite partitioning from the aqueous phase. The emulsion formed by constant inversion,

ensures a very large surface area-to-volume ratio for the two phases concerned and is thus

highly representative of the surface area-to-volume ratio encountered in the droplet-based

enzyme reaction. Loss of CEC from the aqueous phase to oil is not conclusively ruled out

due to the limited solubility of CEC in the aqueous phase. Additional orthogonal approaches

may be necessary to indirectly determine whether leakage of CEC is problem. Chapter 9

considers the use of the Serpentine chip having a 6 minute incubation. If CEC is lost by

partitioning, the results from this would also be poor, helping to confirm loss of CEC as a

primary cause of little or no reaction seen in the cytochrome P450 enzyme experiments.

186

9 Results & Discussion: SERPENTINE INCUBATION CHIP

In this chapter, the results are presented for the repeat P450 enzyme control activity reaction

test using the alternative shorter incubation time chip. The data obtained was quantified

against a CHC calibration curve and compared to equivalent data obtained at 6 min in the

microtitre plate experiments.

9.0.1 Cytochrome P450 Control Activity (no inhibitor)

As only a small signal was obtained from the Spiral chip, it was hypothesised that perhaps

some other artefact was responsible for the loss of signal that impacted more the longer the

incubation and that a shorter incubation may reveal a sufficient signal from the enzyme-

substrate reaction by minimising this additional effect. To achieve sufficient incubation time

in a serpentine that would fit onto the 75 × 75 mm PMMA sections used throughout this

research, and at the required flow rates for droplet formation, it was necessary to utilise a

wider channel. The use of a significantly wider channel (0.8 mm) resulted in a pronounced

parabolic flow where droplets near the channel walls moved more slowly and hence were

incubated for longer than droplets in the centre of the channel.

Table 27. Serpentine chip-based 1A2-CEC data acquired at 34 °C.

Average (n=107) SD %CV

Peak Area 13.7 2.81 20.5

Peak Height 0.5 0.09 18.1

187

Figure 71. Calibration plot for CHC in 6-minute chip based on peak heights and peak area.

To negate this effect, the final channel design utilised a series of constrictions as described by

Lucas et al.182 which had the effect of randomising droplet passage through the entire device

and thus normalising the average droplet residence time to a Gaussian distribution.

The Serpentine chip yielded average peak signal data summarised in Table 27 (n=107), which

when calculated against the standard calibration curve linear fit equation in Figure 71 gave a

fluorescent metabolite concentration of 0.09 ±0.02 µM, based on the error associated with the

peak height measurements. This value is similar to that obtained in the microtitre plate

y = 5.1665x

R² = 0.9997

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

-0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Pk H

eight

Rep

onse

(V

)

Concentration (µM)

CHC In-Chip Calibration Curve Based on Pk Ht (n=45)

CHC Calibration

Linear (CHC Calibration)

y = 343.56x

R² = 0.9886

-20.00

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

-0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Pk A

rea

Res

po

nse

Concentration (µM)

CHC In-Chip Calibration Curve Based on Pk Area (n=45).

CHC Calibration

Linear (CHC Calibration)

188

reaction at t=6 minutes, where the concentration of metabolite was calculated to be about

0.06 µM.

9.1 Summary & Conclusions

9.1.1 Serpentine Incubation Chip

The data provided a CHC control activity signal from the enzyme reaction after 6 minutes

incubation very close to that obtained in the microtitre plate experiment, suggesting the

shorter Serpentine chip was a useful option in which to conduct the P450 cytochrome assay.

However, the experiments did not test the reproducibility and functionality of the chip for

delivering quantifiable IC50 data using a range of inhibitor concentrations. The work

involving a full repeat IC50 determination using the Serpentine chip is detailed in chapter 10.

189

10 Results & Discussion: PROTEIN BLOCKING

In this chapter, the fluorescent tagging of a surrogate protein and the use of blocking proteins

is discussed. Results for tests using fluorescence microscopy to investigate the potential for

proteins to bind or localise at the droplet interface are described. As discussed in the

introduction, the interaction of proteins and enzymes at liquid interfaces has been well

documented in other areas of protein science and to a degree has be considered in

microfluidics, however each case is different, and as such, the impact this effect has on the

enzyme assay used in this project is largely unknown. The impact of using blocking proteins

co-administered to the enzyme reaction to affect the reaction signal is also described.

10.0.1 Determination of Fluorescent Tag

F/P ratio was determined to be approximately 0.15 which was considered high enough to

allow fluorescent measurements of the labelled protein. Absorbance data for the unlabelled

(green plot) versus labelled protein (red plot) is shown in Figure 72.

Figure 72 Absorbance spectra for unlabelled and FITC-labelled ‘Protein A’. The green plot represents the

absorbance profile of unlabelled ‘Protein A’ and the red plot that of FITC-labelled ‘Protein A’.

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

250 300 350 400 450 500 550 600

AB

S

Wavelength / nm

Blank

Average FITC-Protein A (n = 4)

Average Protein A(n=3)

190

10.0.2 Labelled Proteins at the Droplet-Oil Interface

Separately, solutions of fluorescein and FITC labelled 'Protein A' solution were flowed into

the Reservoir chip and the droplets analysed by fluorescent microscopy. Comparison of

epifluorescent detection between droplets containing just fluorescein in phosphate buffer

(PB) and those of FITC-labelled ‘Protein A’ solutions in PB yielded substantially different

results. As depicted in Figure 73i, fluorescein-only droplets had a uniform fluorescence

across the entire droplet diameter, whereas FITC-labelled ‘Protein A’ (Figure 73ii) showed a

pronounced bias of fluorescence around the circumference, seen as a ‘ring’ of light.

Figure 73(i) Droplets containing phosphate buffered fluorescein (1 µM) as measured by epifluorescence at 405

nm. (ii) Pixel analysis using ImageJ software of section indicated by the yellow line. (iii) Droplets containing

FITC-labelled ‘Protein A’ as measured by epifluorescence at 405 nm. The FITC label concentration was

determined to be approximately similar to the stock fluorescein droplets seen in (i) and thus able to provide a

signal of similar magnitude under the same acquisition conditions. Notably, droplets containing labelled protein

show a distinct ring of fluorescence compared to droplets containing just fluorescein. This is suggestive of

fluorophore localisation at the droplet interface due to surface protein adsorption, as highlighted by the ImageJ

analysis (iv).

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70 80 90

Inte

nsi

ty (

gre

y V

alu

es)

# Pixels

Pixel Luminescence

0

20

40

60

80

0 20 40 60 80Inte

nsi

ty (

gre

y V

alu

es)

# Pixels

Pixel Luminescence

(i) (ii)

(iii)

(iv)

191

The bias of fluorescence across the droplet diameter is represented in Figure 73(iii) and

73(iv). The images were scaled in ImageJ and the pixel intensity across the diameter plotted

against droplet size to yield a representation of the variance of fluorescence across the

droplet. Droplets in both cases were of equivalent size and the fluorescent signal of similar

overall intensity.

Figure 74. (i) Droplets containing phosphate buffered fluorescein (1 µM) as measured by confocal laser

scanning microscopy at 405 nm. No apparent change in fluorescent intensity is observed from one side of the

droplet to the other, especially in the middle images of the series representing the droplet maximum cross

section. Each image represents an 8 µm incremental shift in the focal plane acquired. Some evidence of a

droplet-chip contact patch is seen as highlighted by the blue box. (ii) Droplets containing FITC-labelled

‘Protein A’ as measured by laser scanning confocal microscopy at 405 nm. As the acquired data moves from

one side of the droplet towards its contact patch with the chip (blue box), a pronounced ‘ring’ is observed at the

outer edge of the droplet, suggestive of a localised FITC-protein fluorescence concentration.

The data shown in Figure 73(iii) suggests the localisation of fluorescent material around the

droplet perimeter, evidenced by the ‘ring’ of light around the droplet circumference (Figure

73(iv). If this was simply an artefact of light refraction, it is reasonable to assume this would

have been seen for fluorescein-only droplets, which was not evident. The brighter region

(i)

(ii)

192

seen at the centre of each FITC-labelled protein droplet was assumed to be observation of the

same fluorescence surface localisation effect visualised in the z-plane. Notably, this centre-

of-droplet highlight is not observed in the fluorescein-only droplets.

To provide confirmation of fluorescent localisation, the same droplet tests were performed

and quantified by laser-scanning confocal microscopy. Figure 74(i) depicts the images

acquired for fluorescein-only droplets and Figure 74(ii) for droplets containing the labelled

protein. Each image represents a 'slice' of 8 µm thickness in the Z plane of the droplet. The

images result from each droplet being scanned from the bottom to the top, relative to the chip

on the microscope stage.

In the case of the fluorescein-only droplets, no significant variation of fluorescent intensity

was observed from the centre to interfacial surfaces of the droplets, depicted as a uniform

green colour for each of the slice images in Figure 74i. For droplets containing FITC-

labelled ‘Protein A’ solution, a ring of higher intensity fluorescence is seen as the confocal

imaging slices through the droplet, indicated by the red box in Figure 74(ii). As with the

epifluorescence images, a secondary area of higher intensity is seen as the acquisition slice

moves towards the edge of the droplet in contact with the plastic of the chip, indicated by the

blue boxes in Figure 74(i) & (ii).

A similar result was observed for FITC-labelled casein, as depicted in Figure 75. A ‘ring’ of

higher fluorescence became apparent about halfway through the imaging sequence (red box).

Figure 75. Droplets containing FITC-labelled casein (FITC effective concentration ~ 8 µM) as measured by

confocal laser scanning microscopy at 405 nm.

193

Both epifluorescence and laser scanning confocal microscopy for labelled Protein A and

casein, both revealed a marked tendency for a ‘ring’ of concentrated fluorescence around the

droplet perimeter. This was not evident when observing fluorescein-only containing droplets

which suggested the ‘ring’ is a real effect and not an optical artefact resulting from light

scattering and/or refraction.

10.0.3 Droplet Formation in the Presence of Blocking Proteins

Data obtained from the labelled protein experiments suggested localisation of material at the

droplet interface. The blocking protein experiments were designed to attempt mitigation of

this effect by preferentially saturating the droplet interface with a surrogate protein at higher

concentration such that the material of interest (the cytochrome P450 enzyme) would not

bind to the droplet perimeter. Initially, it was important to confirm robust and reproducible

droplet formation when using blocking proteins in the enzyme-substrate solution.

Casein, gelatin and BSA were dissolved in PB at three different concentrations and tested in

the droplet reservoir chip (Figure 76). Only gelatin and BSA were found to be useable over

the full concentration range tested up to 1 mg/mL. At concentrations greater than 50 µg/mL

casein had a pronounced effect on droplet formation and stability in that droplets were

observed to deform significantly. The deformation was worst at 1 mg/mL (Figure 76, top

left). The droplet deformation seen with casein, was considered excessive and this material

was not pursued further for the experiments investigating the effect of blocking protein on the

1A2-CEC reaction and subsequent IC50 determinations.

The impact of blocking proteins on the enzyme signal and IC50 data is considered in

subsequent sections.

194

Figure 76. Casein at 1 mg/mL in phosphate buffer (top left). A pronounced deformation of droplets is seen

which was not the case in either BSA (top right) or gelatin (bottom) at 1 mg/mL.

10.0.4 Impact of Blocking Proteins on Cytochrome 1A2 Enzyme Reaction

The serpentine chip was used to acquire all data reported in this section, which concerns the

potential of blocking proteins to attempt the recovery of the cytochrome P450 enzyme

reaction signal.

Three concentrations of gelatin, and, separately, the same three concentrations of BSA were

titrated to the enzyme-substrate solution as blocking proteins. The data (Figure 78) showed

that when compared to the data at 6 minutes when not using blocking protein, gelatin and

BSA appeared to have a substantial positive effect on the control activity signal obtained with

an almost doubling of control signal at 1000 µg/mL gelatin. For comparison, the same

1 mm 1 mm

1 mm

195

experiments conducted in the microtitre plate (Figure 77), yielded no discernible difference in

signal output whether blocking protein was present or not.

Figure 77. Microtitre plate 1A2-CEC control activity reaction measured at t=6 and t=20 minutes without any

blocking protein. Data included for three concentrations each of gelatin and BSA as blocking protein. No

significant impact on control activity signal is seen for either of the blocking protein at any concentration,

which was expected in the small surface area-to-volume ratio for microtitre plate environments. Plate data at

t=20 minutes is included for comparison.

Microtitre plate: 1A2-CEC Control Activity (CA)

[Protein]

(µg/ml) Plate CA SD

Gelatin µg/ml t6 Plate 50 4113 246.8

500 4556 414.6

1000 3915 348.4

BSA µg/ml t6 Plate 50 3597 104.3

500 2966 127.5

1000 4182 464.2

No blocker t6 Plate - 3788 491.7

No blocker t20 Plate - 13579 502.3

0

5000

10000

15000

50 µg/ml 500

µg/ml

1000

µg/ml

50

µg/ml

500

µg/ml

1000

µg/ml

t6 mins t6 mins t6 mins t20 mins

Gelatin BSA No

blocker

No

blocker

RF

U

Control Activity (microtitre plate) Gelatin t6 mins 50 µg/ml

Gelatin t6 mins 500 µg/ml

Gelatin t6 mins 1000 µg/ml

BSA t6 mins 50 µg/ml

BSA t6 mins 500 µg/ml

BSA t6 mins 1000 µg/ml

No blocker t6 mins

No blocker t20 mins

n=8

n=3n=3 n=3

196

This was expected due to the much smaller surface area to volume ratio encountered in the

microtitre plate well, thus the blocking protein serves little purpose in respect of stopping

interaction of the enzyme at surface interfaces compared to in the droplet where the surface

area to volume ratio is large and thus the enzyme is more likely to undergo surface interface

interactions.

Figure 78. Serpentine 6 minute chip 1A2-CEC control activity reaction measured. Most attempts to obtain a

signal at t=20 (spiral chip) failed. Only the t=6 minute chip yielded a control activity signal that could be

reproducibly quantified to enable the effect of different concentrations of blocking protein to be observed. As a

result of the relatively large deviation between data at different blocking protein concentrations, it is not

possible to conclude an obvious quantifiable effect of the blocking protein on control activity over the 20 fold

protein range tested, other than the suggestion of a trend for gelatin where higher concentrations result in a

higher signal and for BSA where higher concentrations appear to diminish the signal after an initial increase.

Overall, the control activity signal was increased when using blocking proteins.

Microfluidic chips: 1A2-CEC Control Activity (CA)

[Protein] (µg/ml) Chip CA pk height SD

t6 chip 50 1.1 247

500 1.39 415

1000 1.52 348

t6 chip 50 1.2 104

500 1.26 127

1000 1.01 464

t6 chip - 0.38 491

t20 chip - 0 502

-3.5

6.5

16.5

26.5

36.5

46.5

56.5

66.5

76.5

50 µg/ml 500

µg/ml

1000

µg/ml

50 µg/ml 500

µg/ml

1000

µg/ml

t6 mins t6 mins t6 mins t20 mins

Gelatin BSA No

blocker

No

blocker

Pea

k A

rea

Control Activity (DMF) Gelatin t6 mins 50 µg/ml

Gelatin t6 mins 500 µg/ml

Gelatin t6 mins 1000 µg/ml

BSA t6 mins 50 µg/ml

BSA t6 mins 500 µg/ml

BSA t6 mins 1000 µg/ml

No blocker t6 mins

No blocker t20 mins

n=342 n=246

n=308 n=297

n=216

n=213

n=286

197

Figure 78 summarises the data obtained for the Serpentine droplet incubation chip. A

substantial increase in signal is obtained when using gelatin, and although the standard

deviation associated with each measurement is relatively high (caused by variation in the

peak height and areas obtained over each quantified droplet series), the data suggested an

overall recovery towards the level of control activity seen in the microtitre plate experiment.

When using BSA, the quantified signal appeared lower, although taking account of the error

associated with the recorded data, this may be comparable to that obtained with gelatin. BSA

is known to sequester compounds through non-specific binding237,238, which could account for

a lower signal due to competitive binding with the CEC substrate.

10.0.5 Impact of Blocking Proteins on Cytochrome 1A2-CEC pIC50

In this section, the cytochrome P450 enzyme inhibition reaction is considered from the

perspective of how the reported inhibitor IC50 may be affected by the inclusion of blocking

proteins to the reaction. All IC50 data has been log transformed to pIC50 for easier data

visualisation.

Addition of gelatin to the 1A2 reaction in the microtitre plate was observed to decrease the

apparent potency of fluvoxamine by approximately one order of magnitude, seen by a

decrease in pIC50 (Figure 79). This effect could be explained by interaction of the high

concentration of the blocking protein in the well with the substrate and/or reactive enzyme

pocket. Fluvoxamine pIC50 was also observed to decrease in the droplet tests by about the

same amount when using gelatin (Figure 80). In both microtitre plate and droplet tests, the

change in pIC50 is more variable when using BSA which may be caused by greater non-

specific interaction of the BSA with the inhibitor and/or substrate.

198

Figure 79. Microtitre plate pIC50 data for fluvoxamine. Overall, the pIC50 data suggests the presence of

blocking protein co-administered to the incubate reduces the apparent potency of the fluvoxamine inhibitor.

For gelatin, the pIC50 appears to increase as the concentration if gelatin increases. For BSA, the data is more

variable, which may be explained by interaction of BSA with the inhibitor and/or substrate at different

concentrations.

6.00

7.00

8.00

50 µg/ml 500 µg/ml 1000

µg/ml

50 µg/ml 500 µg/ml 1000

µg/ml

t6 mins t6 mins t6 mins

Gelatin BSA No blocker

pIC

50

pIC50 fluvoxamine (microtitre plate)

Gelatin t6 mins 50 µg/ml

Gelatin t6 mins 500 µg/ml

Gelatin t6 mins 1000 µg/ml

BSA t6 mins 50 µg/ml

BSA t6 mins 500 µg/ml

BSA t6 mins 1000 µg/ml

No blocker t6 mins

Microtitre plate: Fluvoxamine IC50

IC50

(µM) pIC50

Gelatin µg/ml t6 Plate 50 0.34 6.47

500 0.3 6.52

1000 0.08 7.10

BSA µg/ml t6 Plate 50 0.05 7.30

500 0.60 6.22

1000 0.14 6.85

No blocker t6 Plate - 0.03 7.49

199

Figure 80. Micro droplet IC50 data for fluvoxamine. Overall, the pIC50 data suggests the presence of blocking

protein co-administered to the incubate reduces the apparent potency of the fluvoxamine inhibitor. For gelatin,

the pIC50 appears largely unaffected by gelatin concentration, whereas for BSA, the data suggests there may be

a concentration dependency on the observed pIC50.

Microfluidic chip: Fluvoxamine IC50

IC50 (µM) pIC50

Gelatin µg/ml t6 chip 50 0.31 6.51

500 0.40 6.40

1000 0.14 6.86

BSA µg/ml t6 chip 50 0.25 6.61

500 0.19 6.72

1000 0.007 8.15

No blocker t6 chip - 0.028 7.56

6.00

7.00

8.00

50 µg/ml 500 µg/ml 1000

µg/ml

50 µg/ml 500 µg/ml 1000

µg/ml

t6 chip t6 chip t6 chip

Gelatin BSA No blocker

pIC

50 /

µM

pIC50 fluvoxamine (DMF) Gelatin t6 chip 50 µg/ml

Gelatin t6 chip 500 µg/ml

Gelatin t6 chip 1000 µg/ml

BSA t6 chip 50 µg/ml

BSA t6 chip 500 µg/ml

BSA t6 chip 1000 µg/ml

No blocker t6 chip

n=308 n=297

n=216

n=287n=342

n=250

n=213

200

10.1 Summary & Conclusions

10.1.1 Fluorescent Tagging of Proteins & Labelled Proteins at the Droplet

Interface

Although the tests are purely qualitative, with no quantitative reference to the amount of

fluorescence observed, and hence the concentration of protein resident at the droplet

interface, the results suggest a pronounced surface localisation of protein in the droplet. This

could certainly explain the lack of enzymatic reaction seen in the droplet P450 cytochrome

experiments described previously. Some caution is exercised here as the labelled protein,

whilst a large macromolecule similar in size to the P450 cytochrome protein construct, is

different and may behave differently at the interface. However, it can be assumed the P450

cytochrome assay was affected by localisation of the material to the droplet interface,

possibly affecting its activity and reaction profile.

Furthermore, the number of molecules in a droplet of 14 nL volume at the intended incubate

concentration of 10 nM is relatively low. A large percentage of these molecules could be

bound to the droplet interface, and if rendered inactive, the substrate would not be

metabolised to the fluorescent product, explaining the lack of a signal previously seen.

10.1.2 Droplet Formation in the Presence of Blocking Proteins

Of the three proteins used tested for use as blocking proteins, only gelatin and BSA were

found to be suitable for use up to 1 mg/mL. Casein when used above 50 µg/mL in the PB

solution resulted in droplets that deformed significantly and was thus not pursued in further

testing.

201

10.1.3 Impact of Blocking Proteins on the Cytochrome P450 Reaction

The addition of blocking proteins at concentrations well in excess of the cytochrome P450

enzyme concentration used in these studies (10 nM incubate) were shown to have a positive

impact on recovering the available CHC signal produced from the enzyme reaction.

Equivalent tests performed in the microtitre plate assay did not reveal any signal

improvements. This suggested that the inclusion of blocking proteins to the droplet-based

enzyme reaction was able to reduce binding or interaction of enzyme to the droplet interface;

an effect only seen in the high surface area-to-volume ratio environment of the droplet.

Experiments conducted in the droplet format using blocking proteins also yielded evidence

that although the signal can be improved, there is the potential for the pharmacology of the

stem to be affected. In this case, this was seen as a shift in the reported pIC50 for the

fluvoxamine inhibitor. Caution must therefore be applied if requiring the use of secondary

materials to control interfacial artefacts in droplet microfluidic systems.

202

11 SUMMARY, CONCLUSIONS & FURTHER WORK

11.1 Summary

The aim was to investigate the feasibility of designing a proof of concept droplet microfluidic

device for performing biochemical cytochrome P450 enzyme inhibition studies in a micro

droplet environment, and how this would enable up-scaling of these assays in a routine

industrial screening environment. This approach would enable significant reductions in the

volume and hence cost of all reagents used to perform this type of assay when compared to

existing 96-, 384- or 1536-well microtitre plate formats. As part of the investigation, the

biphasic nature of the microfluidic environment meant it was necessary to consider the

potential for partitioning of materials from the dispersed droplet aqueous phase to the carrier

organic phase and the impact this may have on biochemical reaction and quantification.

Whilst it was not possible to exhaustively test all compounds’ chemistry, a selection of

common drugs were used in the test to provide a representative assessment. These studies

were conducted both on a ‘test tube’ scale, and as microtitre plate experiments. Thus

assumptions were made about a planar liquid interface compared to the spherical interface in

the droplet environment, and within experimental polymer microfluidic devices to provide

data more representative of the droplet environment. A range of oils were initially tested

with respect to droplet formation and in the test tube partitioning studies.

The fluorous oil, FC-70, was shown to form droplets very reproducibly and to have the

lowest partitioning potential in the glass vial experiments, so was subsequently tested in the

microtitre plate and chip droplet experiments.

The extent of compound partitioning from aqueous phase to fluorous oil for representative

drug discovery AZ compounds was conducted in 384-well microtitre plates. A model was

203

built on valid data obtained from two rounds of experimentation comprising 1406 diverse

chemical structures. This study indicated the potential for a number of compound chemistries

to be prone to fluorous phase partitioning, however only 5 compounds from 475 confirmed

results showed substantial partitioning greater than 70% which was lower than first expected

based on the initial glass vial tests.

Using the AZ compound partitioning data, a Bayesian score molecular fingerprint-based

model was built to attempt prediction of fluorous solubility. A further set of compounds were

selected based on the initial model output and experimentally tested to indicate model

accuracy. Of 187 valid compound results, only 9 were correctly predicted to be compounds

expected to partition > 20 % with the remaining 178 compounds which were also predicted to

partition >20 % found in the –20 to 20 % group . Whilst the model is not highly predictive at

this stage, it is envisaged that perhaps with further enrichment using data from a much larger

experimental test, it would be possible to increase the predictive ability of the model. The

poor correlation of the predictions provided by the fluorous solubility model to experimental

data suggested the range of physicochemical properties specified alone do not have a

significant bearing on the chemistry of fluorous solubility. There is perhaps reason to suggest

that a better prediction of fluorous solubility might be obtained if electronegativity data is

included in the model analysis and a closer consideration of fluorine bond lengths and bond

enthalpies to take account of fluorine chemistry which significantly differs to conventional

organic chemistry.

A range of construction techniques were considered for the microfluidic devices required.

The chosen approach of a CNC-machined, polymer-based chip using an optically transparent

microtitre plate seal, was based on the availability of the equipment necessary to machine the

plastic and the relative ease by which a number of devices could be produced in quick

succession for iterative design-make-test processes.

204

Initial tests concerning P450 enzyme inhibition assays included studying the linearity and

apparent limit of detection of standard metabolite curves over the concentration range 0.1 µM

to 1 µM in the microtitre plate compared to those conducted in the microfluidic chip. Good

agreement was seen with variability well controlled at typically less than 10 %. Secondly,

enzyme-substrate experiments were conducted in the Spiral and Serpentine microfluidic chips

to assess how this reaction proceeded in the droplet environment compared to the microtitre

plate. In the case of the Spiral chip, on only one occasion was it possible to obtain data that

yielded a strong enough signal to quantify the IC50 of the fluvoxamine inhibitor, yet this did

produce a value consistent with literature data (~0.03 µM). The shorter incubation

Serpentine chip did yield a reproducible enzyme reaction data that also closely matched the

data obtained in the equivalent microtitre plate tests.

Based on the outcome of these experiments, further work was completed investigating the

apparent effect of the droplet environment upon the enzyme/protein used. A qualitative study

was employed to provide evidence of protein adsorption to the droplet-oil interface,

hypothesised as a significant cause of the lack of enzyme activity found in the droplet P450

cytochrome studies. In this work, epifluorescence and laser scanning confocal microscopy in

conjunction with a static droplet reservoir chip were used to study the presence of

fluorescently labelled proteins localised at the droplet interface. The results indicated a

substantial localisation of fluorescently labelled protein at the interface, supporting the

hypothesis that P450 enzyme may be binding at the droplet interface, adversely affecting the

reaction with the CEC substrate. Following this, a series of tests were completed to

quantitatively assess the impact three different blocking proteins co-administered to the

droplets had on the P450 enzyme activity and how changes of pharmacology in terms of

pIC50 shift were observed. Casein as a blocking protein was not pursed further following

droplet formation tests that revealed this material to render droplets ‘sticky’ and deformed

205

within the chip. BSA and gelatin were successful at largely restoring cytochrome P450

enzyme activity, although there was evidence the presence of the blocking proteins in the

reactant droplets affected pharmacology, seen as an increase in the reported IC50 by about 1

order of magnitude.

11.2 Conclusions

11.2.1 Partitioning of Compound from the Droplet

Testing of compounds with respect to partitioning in a biphasic environment is by no means

trivial when significant numbers of compounds are concerned. On a small scale, it is feasible

to characterise the partitioning of all compounds used within the study, however at the scale

of industrial compound libraries and HTS, this task would become prohibitively time

consuming, thus the use of predictive models to provide informative data ahead of

experimental testing would be of high value in this area of research. Rank ordering of

compounds in respect of partitioning conducted in silico offers a means to identify classes of

compounds that may be challenging to assay/analyse in droplets.

There are assumptions made from the data obtained in the tests presented in this work that

may not fully represent certain specific cases; for example, specialised chemistry such as

organometallic compounds or chelating properties may affect partitioning in ways not

predicted by the model presented. The purpose of the partitioning experiments detailed is not

intended to be a fully quantitative assessment, but rather an indicator that may be

extrapolated to a wider population of chemistry. Much of the recent research in leakage of

compounds from droplets, described in chapter 1, concerns small molecule fluorescent dyes

which are not wholly representative of the wide chemistry that will be explored in drug

206

discovery. This may in part be due to the relative scarcity of large compound collection sets

available outside of the pharma industry and the cost of obtaining such libraries. The attempt

in this project to characterise partitioning for a diverse pharmaceutical compound set is

important in further defining and understanding the relevance partitioning has for droplet

microfluidics applied to drug discovery screening.

As discussed in Chapter 1, research into the impact of surfactants on biochemical assay is

varied and certainly not complete. Fluorous phases and fluorosurfactants have become

somewhat commonplace in droplet microfluidics, yet there is still no complete set of studies

that fully characterise or describe their use. Non-ionic fluorosurfactants were considered as a

way to control non-specific adsorption of proteins at the droplet interfaces (Roach et al.176)

and yet in this project, despite attempting to replicate such fluorosurfactants, it was found that

the fluorosurfactant synthesised for this project did not prevent P450 cytochrome enzyme

from interacting with the droplet interface and was observed to adversely affect leakage from

the droplet. However, it was successful in providing stable droplet formation.

The possibility of extensive leakage from the aqueous to oil phase in droplet MF is a key

issue, that unless appropriately addressed by including internal controls for every compound

tested, or using blocking proteins to reduce partitioning to within acceptable limits, may

prove be a significant limiting factor that reduces the scope of application. In a worst-case

scenario, droplet microfluidic technology may not be best placed within a screening

environment. Instead, the results suggest that droplet microfluidics may be better utilised

within specific niche applications where high degrees of miniaturisation can benefit the

overall process, but, crucially, the materials studied are well characterised.

207

11.2.2 Enzyme Inhibition & Proteins at the Droplet Interface

In addition to the concerns of compound partitioning, further problems arise when using

proteins and enzymes in droplets where adsorption at the droplet interface can lead to

conformational change or binding site blockage resulting in an observed loss of enzyme

reactivity. Simply increasing the overall droplet enzyme / protein concentration may be

effective in negating the effect of interface adsorption; however this is then counter to a key

advantage of miniaturisation by microfluidics as the level of reagents needed for each test is

increased. As described before, blocking proteins can be used to reduce the effect of

interface interaction of the protein being studied, however this may then result in

pharmacology changes which have to be considered and appropriately assessed.

Miniaturisation of the cytochrome P450 inhibition assay, as detailed in chapter 2, was

partially successful after inclusion of blocking proteins to recover the reaction signal.

However, it is clear from the initial attempts using the Spiral chip that a simple scaling-down

translation of this assay from microtitre plate to droplet format was not successful. To deliver

a robust working assay, more work is required in addressing the underlying challenges of

partitioning and the science of proteins/enzymes at liquid interfaces. Significant re-

optimisation and validation of any assay transferred to a droplet format is most likely

required before useful data can be obtained, if indeed a suitable set of conditions can be

found that are acceptable and reproducible.

11.2.3 Droplet Technology as an Industrial Screening Tool

Aside from the observed problems associated with partitioning and loss of P450 cytochrome

activity in this work, an additional complication arises in the means of introducing reagents

208

into microfluidic devices. This was out of scope for this project being a sizeable issue in its

own right. Paradoxically, the proof of concept methods for droplet tests employed in this

work use relatively large quantities of reagents, which equals or exceeds that required for a

single microtitre plate assay, negating the point of miniaturisation. For droplet-based

technology to be successful as a large-scale screening tool, a more efficient means of

introducing reagents to microfluidic devices must be found.

11.3 Further work

A number of key areas have been identified that require addressing if droplet microfluidic

technology can prosper as an alternative or complementary screening tool within drug

discovery. In this section, critical aspects of current technological challenges, limitations and

future aspirations of droplet microfluidics are discussed, with an accompanying discussion on

potential approaches to address a number of identified issues. In addition, there is a

requirement to further investigate the extent of partitioning between phases in droplet

microfluidic systems and the impact this may have on the use of droplet microfluidics in

routine screening.

11.3.1 Predictive Model Development

The model described in this work to predict for compound partitioning was found to be

largely unsuccessful in defining an accurate correlation between fluorous phase solubility and

compound chemistry. As described previously, the data set available for training the model

was probably too small to allow a robust prediction to evolve, given the apparent complexity

of the number of physicochemical parameters that may ultimately describe the tendency for

209

fluorous phase solubility. Alternatively, the model is not correlating the correct parameters,

and as such, the current design may have little chance of ever being enriched with additional

data to achieve the desired outcomes.

To address this, considerable further experimentation should be undertaken to provide a

greater pool of data from which to build training and test sets to determine if higher

prediction accuracy can be achieved. If no model improvement is obtained by expanding the

data pool, other model definitions may be explored, where molecular structure and

physicochemical properties are considered in different ways to generate predictive

correlations. In addition, other experimental designs could be considered that offer a faster

way to test large numbers of compounds, such as chromatographic systems using fluorous

stationary phases where differing compound retention may correlate to greater fluorous oil

solubility.

11.3.2 Reagents Into Microfluidic Devices

A primary challenge to render droplet microfluidics useful from an industrial at-scale

screening tool perspective is the ability to inject reagents, and particularly, large numbers of

diverse compounds, into droplet systems in a manner that is both robust and easy for the

scientist to employ. This problem was solved for current microtitre plate based processes

through the co-emergence of robotic pipetting stations and liquid-handling robots capable of

dispensing multiple compounds many times to a multitude of plate types. To realise the full

potential of microfluidics, a similar solution is required, so that many compounds can be

studied in DMF systems either in parallel or sequentially. This will be essential to apply

microfluidic technology in early stage high throughput screening where compound library

collections may rise to an excess of 2 million compounds for large pharmaceutical collection

libraries, or where it is advantageous to perform screens for smaller compound sets or an

210

individual compound against many multiple biochemical or cell-based targets

simultaneously. Given the high level of financial investment in microtitre plate-based

technology across the drug discovery industry, it is understandable the industry sector may

not be ready to abandon entirely its current screening platforms, especially as an alternative is

not yet immediately ready for at-scale routine screening processes. Thus, for microfluidics to

be successful, microfluidics must easily interface with existing technologies.

In introducing compounds into microfluidic systems at larger throughput levels, it is desirable

to utilise existing liquid handling and plate-based equipment where possible. However, this

is not a trivial problem; microfluidic systems in research tend to be closed systems where

reagents and compounds are often supplied to the chips via syringe pumps, or other closed

positive displacement mechanism. Such an approach cannot be used for many different

compounds in succession, as constant disconnection and reconnection of vessels and changes

in system operating pressure will lead to air ingress and destabilisation. To avoid these issues

and yet achieve easy integration to the outside plate-based lab environment, it follows, for

example, that either a clever way of supplying compounds to a chip by dispensing directly

should be found, or by using a negative pressure driven chip to ‘pull’ compounds and

reagents from other vessels into the microfluidic device. Considering the above former

option, Raindance Technology utilised a way in which all the reagents required are added to

modified Eppendorf-type reservoirs which are then pushed into the microfluidic system using

a regulated gas pressure to displace fluid from the reservoirs. Importantly, this closed-loop

approach minimises air ingress, however requires complete replenishment between

experiments. By contrast, the droplet qPCR system developed by BioRad, utilises a system

whereby reagents are dispensed by any conventional means to the chip, followed by

application of a sealed pressure manifold to drive fluids through the device239,240. However,

this too is limited by the number of samples per experiment and does not enable the efficient

211

processing of the thousands or even just hundreds of compounds required per experiment in

early phase screening.

A further requirement may also arise from the opportunity of process streamlining and

efficiency gains that microfluidics can offer. As we have seen previously, typical HTS

campaign cascades for large compound libraries will progress from an initial single

concentration point primary screen, through confirmation screens, to concentration response

for confirmed actives. Currently, this places a large demand upon HTS centres in terms of

consumables, cost and the time it takes to progress compounds through the screening

cascade, which may run up to 6 months from start to end. Microfluidics could reduce this

burden by screening all compounds at concentration response in the primary assay, thereby

realising significant reagent and consumable cost reduction and also significantly shorter

campaign duration. To satisfy this, microfluidic devices will have to be designed in such a

way that compounds can be introduced to the system easily and robustly.

Niu et al.241 presented a novel means of generating dose response curves in a droplet

generator whereby incoming droplets of diluent collided with pre-loaded sample plugs within

the device. The chip channel design resulted in a daughter droplet of lower concentration to

be sheared off. The limitation of this approach lies in the requirement to preload samples to

the chip which for 100’s or 1000’s of compounds becomes a nearly impossible task to

achieve in a manner that is at least as cheap or easy as creating dose response curves in a

1536-well plate.

The ‘Mitos Dropix’ microfluidic dropletiser device developed in collaboration between Drop-

Tech (Cambridge, UK)45 and Dolomite Microfluidics (Royston, Cambridge, UK) offers a

way of sipping small quantities of several samples in a continuous manner to exclude ingress

of air to the downstream microfluidic chip device. Although currently limited to only 24

different samples at one time and thus not ideally suited to HTS at the moment, it shows

212

promise by being compatible with automated liquid handling equipment in having 384-well

microtitre plate spacing of the reagent wells. In this device, samples are floated on the higher

density fluorocarbon oil and a J-shaped sipping tube is used to sample from each reagent in

turn. The J-tube moves up and down on a carriage that can move from sample to sample. In

this manner, small samples inter-dispersed with fluorocarbon oil can be drawn into a

connected sample tube and from there, can be connected onto subsequent microfluidic chip

devices. Furthermore, blank sample droplets can be introduced which may be used in

conjunction with merging chips to generate controlled concentration response curves for each

compound. Intriguingly, it is also possible to incorporate droplet merging chips to the Dropix

system allowing the generation of concentration gradients that may be then fed to

downstream MF devices.

(i)

(ii)

Figure 81. (i) Droplets of compound ‘A’ formed as a serial concentration gradient, in which partitioning is

likely to be higher between all droplets. (ii) Droplets of compounds ‘A’, ‘B’ and ‘C’ grouped by concentration.

Partitioning is likely to be lower between droplets of the same concentration and only greater where the

concentration changes (points ‘X’).

In any future strategy allowing segmentation of sample in biphasic microfluidics, it is

imperative that appropriate consideration is given to determining the potential for drugs or

other reagents to leach from the droplets into the oil phase, or indeed, particularly for

A A A A A

A B C A B C A B C

X X

213

concentration response curves, to determine the extent of concentration gradient driven cross-

talk between droplets. Strategies to reduce these artefacts could include the use of pure

fluorocarbon oils using no surfactant, greater interspacing of droplets and, in the case of

concentration response curves, logical ordering of the droplets, grouping by each compound

and then concentration. In this arrangement, the gradient driven cross-talk would be more

likely to exist between a concentration of one compound and another concentration of

another compound, rather than between all droplets (Figure 81). Another option to reduce

cross-talk and partitioning is to avoid, where possible, using aqueous droplet environments

and use instead DMSO as a compound diluent, which being a better solvent for lipophilic

drug-like molecules, would be expected to increase solubility for many drug-like compounds.

11.3.3 Analytical Detection Methods

The majority of detection techniques employed within microfluidic technology research tend

to be spectrophotometric with some use of mass spectrometry (MS) and optical imaging (OI),

as previously described (pg. 50 onwards). Alternative techniques include: MS coupled to a

microfluidic system presents the possibility of conducting later-stage Lead Optimisation

assays based on hepatocytes or microsomes for intrinsic clearance studies where label-free

quantification of parent drug or its metabolites is required.

Optical imaging has found greater application in recent years in High Content Biology

screening (HCB) and is a powerful addition for phenotypic target identification. Throughput

is often lower than conventional HTS the requirement to conduct extensive imaging scans

(either laser scanning to generate a pseudo-image or genuine optical imaging) across multiple

fields of view at different wavelengths, such as would be required to interrogate cells stained

with different fluorophores, substantially increases the analysis time required. Droplet

214

microfluidic technology in particular may offer additional benefit to HCB by being capable

of both continuous flow and static modes, making it possible to design systems that can

perform HTS and HCB analysis in a one-flow process. To study cells in microfluidic

droplets, it may be necessary to render them stationary through the use of trap devices

allowing images to be acquired under zero flow conditions. The technical requirements for

droplet trapping and the ability to switch between flow and no-flow conditions are likely to

present additional challenges to the overall design of microfluidic systems. However, such

future developments in the area of droplet trapping and real-time imaging will add significant

value of microfluidics in phenotypic and high content biology screening.

11.3.4 Cell-Based Droplet Microfluidics

Despite a large volume of published work from academia and the biotech industry utilising

encapsulated cells, there are few reports of the widespread use of microfluidic cell

encapsulation within mainstream pharmaceutical drug discovery as a main screening

paradigm. A lack of understanding of how different cell types behave in such systems and

the previously outlined problems associated with compound introduction and leakage may all

contribute to an overall lack of confidence and certainty of how microfluidic technology may

succeed.

As discussed previously, agarose and alginate bead encapsulation have been reported, but

each report tends to be specific to a particular application and/or cell line. The emergence of

a more generic ‘best set’ of parameters that can be applied across a wide scope of cell-based

studies will foster a more consistent approach allowing a standard to develop that can be

commercialised more easily.

215

The potential of cell based encapsulation droplet microfluidics can be realised in two discrete

ways; i) reduction of cell numbers (100’s or 10’s of cells per test) to reduce assay costs, and

ii) single cell encapsulation to provide higher value data where cell-specific effects can be

evaluated.

Among the technological challenges of microfluidics, cell-based work is further complicated

by the necessity to ensure cell biology is not adversely affected by the nature of the system.

Cells may behave in unpredicted ways when compared to our current understanding of

microtitre plate cell assays. The limited volume of a bead or droplet will have a limited

quantity of the reagents required for cell survival and proliferation and the potential for

higher concentrations of toxins from cell metabolism may have a faster detrimental effect on

cell health. Thus, while MF may seem to be a desirable technology to reduce costs and

biology usage, this may be countered by limitations as to how long cell assays can operate for

in the MF system before cell apoptosis. Additionally, cell morphology may change as a

result of increased cell stress from the MF environment which in the worst case may result in

inaccurate data.

216

12 REFERENCES

1. Merten, C. A. Screening Europe 2010: an update about the latest technologies and

applications in high-throughput screening. Expert Rev Mol Diagn. 2010, 10, 559-563.

2. Litten, B. A.; Smith, R.; Banfield, E. An Automated 1536-Well Microplate Format

Cytochrome P450 Inhibition Assay Using a Tecan Freedom EVO Workstation with

Integrated Innovadyne Nanodrop II Dispenser. Journal of the Association for Laboratory

Automation 2010, 15, 58-64.

3. Berg, M.; Undisz, K.; Thiericke, R.; Moore, T.; Posten, C. Miniaturization of an Enzyme

Assay (β-Galactosidase) in the 384- and 1536-Well Plate Format. Journal of the

Association for Laboratory Automation 1999, 4, 64-67.

4. Ejendal, K. F. K.; Meyer, J. M.; Brust, T. F.; Avramova, L. V.; Hill, C. A.; Watts, V. J.

Discovery of antagonists of tick dopamine receptors via chemical library screening and

comparative pharmacological analyses. Insect Biochem. Mol. Biol. 2012, 42, 846-853.

5. Lamontagne, J.; Mills, C.; Mao, R.; Goddard, C.; Cai, D.; Guo, H.; Cuconati, A.; Block,

T.; Lu, X. Screening and identification of compounds with antiviral activity against

hepatitis B virus using a safe compound library and novel real-time immune-absorbance

PCR-based high throughput system. Antiviral Res. 2013, 98, 19-26.

6. Lim, K. T.; Zahari, Z.; Amanah, A.; Zainuddin, Z.; Adenan, M. I. Development of

resazurin-based assay in 384-well format for high throughput whole cell screening of

Trypanosoma brucei rhodesiense strain STIB 900 for the identification of potential anti-

trypanosomal agents. Exp. Parasitol. 2016, 162, 49-56.

7. Liu, B.; Li, S.; Hu, J. Technological advances in high-throughput screening. Am. J.

Pharmacogenomics 2004, 4, 263-276.

8. Morel, M.; Vanderstraete, M.; Cailliau, K.; Lescuyer, A.; Lancelot, J.; Dissous, C.

Compound library screening identified Akt/PKB kinase pathway inhibitors as potential

key molecules for the development of new chemotherapeutics against schistosomiasis.

International Journal for Parasitology: Drugs and Drug Resistance 2014, 4, 256-266.

9. Frederix, F.; Bonroy, K.; Reekmans, G.; Laureyn, W.; Campitelli, A.; Abramov, M. A.;

Dehaen, W.; Maes, G. Reduced nonspecific adsorption on covalently immobilized

protein surfaces using poly(ethylene oxide) containing blocking agents. Journal of

Biochemical and Biophysical Methods 2004, 58, 67.

10. Rebeski, D. E.; Winger, E. M.; Shin, Y.; Lelenta, M.; Robinson, M. M.; Varecka, R.;

Crowther, J. R. Identification of unacceptable background caused by non-specific protein

adsorption to the plastic surface of 96-well immunoassay plates using a standardized

enzyme-linked immunosorbent assay procedure. J. Immunol. Methods 1999, 226, 85-92.

11. Bosse, R.; Illy, C.; Elands, J.; Chelsky, D. Miniaturizing screening: how low can we go

today? Drug Discov. Today 2000, 5, 42-47.

12. Stevens, M. E.; Bouchard, P. J.; Kariv, I.; Chung, T. D. Y.; Oldenburg, K. R. Comparison

of Automation Equipment in High Throughput Screening. Journal of Biomolecular

Screening 1998, 3, 305-311.

217

13. DiMasi, J. A.; Grabowski, H. G.; Hansen, R. W. The Cost of Drug Development. N. Engl.

J. Med. 2015, 372, 1972-1972.

14. Harrison, C. Phenotypic screening: A more rapid route to target deconvolution. Nat Rev

Drug Discovery 2014, 13, 102-103.

15. Shi, W.; Qin, J.; Ye, N.; Lin, B. Droplet-based microfluidic system for individual

Caenorhabditis elegans assay. Lab Chip 2008, 8, 1432-1435.

16. Kogej, T.; Blomberg, N.; Greasley, P. J.; Mundt, S.; Vainio, M. J.; Schamberger, J.;

Schmidt, G.; Hüser, J. Big pharma screening collections: more of the same or unique

libraries? The AstraZeneca–Bayer Pharma AG case. Drug Discov. Today 2013, 18,

1014-1024.

17. AstraZeneca plc. Drug Discovery & Screening. Internal information 2000-2016

18. Chiang, P. C.; Hu, Y. Simultaneous determination of logD, logP, and pK(a) of drugs by

using a reverse phase HPLC coupled with a 96-well plate auto injector. Comb. Chem.

High Throughput Screen. 2009, 12, 250-257.

19. Yamashita, T.; Nishimura, I.; Nakamura, T.; Fukami, T. A System for logD Screening of

New Drug Candidates using a Water-Plug Injection Method and Automated Liquid

Handler. Journal of the Association for Laboratory Automation 2009, 14, 76-81.

20. Zdrazil, B.; Chichester, C.; Zander Balderud, L.; Engkvist, O.; Gaulton, A.; Overington,

J. P. Transporter assays and assay ontologies: useful tools for drug discovery. Drug

Discovery Today: Technologies 2014, 12, e47-e54.

21. Horst, R. L.; Reinhardt, T. A.; Beitz, D. C.; Littledike, E. T. A sensitive competitive

protein binding assay for vitamin D in plasma. Steroids 1981, 37, 581-591.

22. Shioya, H.; Shimojo, M.; Kawahara, Y. Determination in plasma of angiotensin-

converting enzyme inhibitor by inhibitor-binding assay. Journal of Chromatography B:

Biomedical Sciences and Applications 1991, 568, 309-314.

23. Honig, P. K.; Wortham, D. C.; Zamani, K.; Conner, D. P.; Mullin, J. C.; Cantilena, L. R.

Terfenadine-ketoconazole interaction: Pharmacokinetic and electrocardiographic

consequences. J. Am. Med. Assoc. 1993, 269, 1513-1518.

24. Brown, A. M. hERG Assay, QT Liability, and Sudden Cardiac Death. Cardiac Safety of

Noncardiac Drugs 2005, 67-81.

25. Atkinson, A.; Kenny, J. R.; Grime, K. Automated Assessment of Time-Dependent

Inhibition of Human Cytochrome P450 Enzymes Using Liquid Chromatography-

Tandem Mass Spectrometry Analysis. Drug Metabolism and Disposition 2005, 33, 1637-

1647.

26. Stresser, D. M.; Turner, S. D.; Blanchard, A. P.; Miller, V. P.; Crespi, C. L. Cytochrome

P450 Fluorometric Substrates: Identification of Isoform-Selective Probes for Rat

CYP2D2 and Human CYP3A4. Drug Metabolism and Disposition 2002, 30, 845-852.

27. Trubetskoy, O. V.; Gibson, J. R.; Marks, B. D. Highly Miniaturized Formats for In Vitro

Drug Metabolism Assays Using Vivid® Fluorescent Substrates and Recombinant

Human Cytochrome P450 Enzymes. Journal of Biomolecular Screening 2005, 10, 56-66.

218

28. Crespi, C. L.; Miller, V. P.; Stresser, D. M. Design and application of fluorometric assays

for human cytochrome P450 inhibition. In Methods in Enzymology Academic Press:

2002; Vol. Volume 357, pp 276-284.

29. Riley, R. J.; Grime, K. Metabolic screening in vitro: metabolic stability, CYP inhibition

and induction. Drug Discovery Today: Technologies 2004, 1, 365-372.

30. Hodgson, E.; Das, P. C.; Cho, T. M.; Rose, R. L. Phase 1 Metabolism of Toxicants and

Metabolic Interactions. In Molecular and Biochemical Toxicology John Wiley & Sons,

Inc.: 2008; pp 173-203.

31. Crespi, C. L.; Miller, V. P. The use of heterologously expressed drug metabolizing

enzymes - state of the art and prospects for the future. Pharmacology & Therapeutics

1999, 84, 121-131.

32. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and

computational approaches to estimate solubility and permeability in drug discovery and

development settings. Adv. Drug Deliv. Rev. 1997, 23, 3-25.

33. Lipinski, C. A. Drug-like properties and the causes of poor solubility and poor

permeability. J. Pharmacol. Toxicol. Methods 2000, 44, 235-249.

34. Yao, M.; Zhu, M.; Sinz, M. W.; Zhang, H.; Humphreys, W. G.; Rodrigues, A. D.; Dai, R.

Development and full validation of six inhibition assays for five major cytochrome P450

enzymes in human liver microsomes using an automated 96-well microplate incubation

format and LC–MS/MS analysis. J. Pharm. Biomed. Anal. 2007, 44, 211-223.

35. Litten, B. Automating metabolic inhibition assays - on a nanoscale. ISSUU 2006, 3, 21.

36. Grimm, S. W.; Einolf, H. J.; Hall, S. D.; He, K.; Lim, H.; Ling, K. J.; Lu, C.; Nomeir, A.

A.; Seibert, E.; Skordos, K. W.; Tonn, G. R.; Van Horn, R.; Wang, R. W.; Wong, Y. N.;

Yang, T. J.; Obach, R. S. The Conduct of in Vitro Studies to Address Time-Dependent

Inhibition of Drug-Metabolizing Enzymes: A Perspective of the Pharmaceutical

Research and Manufacturers of America. Drug Metabolism and Disposition 2009, 37,

1355-1370.

37. Lin, J. H.; Lu, A. Y. H. Role of Pharmacokinetics and Metabolism in Drug Discovery and

Development. Pharmacological Reviews 1997, 49, 403-449.

38. McDonnell, A. M.; H. Dang, C. Basic Review of the Cytochrome P450 System. J. Adv.

Pract. Oncol. 2013, 4, 263.

39. Easterbrook, J.; Lu, C.; Sakai, Y.; Li, A. P. Effects of Organic Solvents on the Activities

of Cytochrome P450 Isoforms, UDP-Dependent Glucuronyl Transferase, and Phenol

Sulfotransferase in Human Hepatocytes. Drug Metabolism and Disposition 2001, 29,

141-144.

40. Narimatsu, S.; Takatsu, N.; Yamano, S.; Inoue, Y.; Hanioka, N.; Kiryu, K.; Naito, S.;

Gonzalez, F. J.; Yamamoto, S. The effect of dimethyl sulfoxide on the function of

cytochrome P450 2D6 in HepG2 cells upon the co-expression with NADPH-cytochrome

P450 reductase. Chem. Biol. Interact. 2006, 159, 47-57.

41. Agresti, J. J.; Antipov, E.; Abate, A. R.; Ahn, K.; Rowat, A. C.; Baret, J.; Marquez, M.;

Klibanov, A. M.; Griffiths, A. D.; Weitz, D. A. Ultrahigh-throughput screening in drop-

219

based microfluidics for directed evolution. Proceedings of the National Academy of

Sciences 2010, 107, 4004-4009.

42. Zinchenko, A.; Devenish, S. R. A.; Kintses, B.; Colin, P.; Fischlechner, M.; Hollfelder, F.

One in a Million: Flow Cytometric Sorting of Single Cell-Lysate Assays in

Monodisperse Picolitre Double Emulsion Droplets for Directed Evolution. Anal. Chem.

2014.

43. Markey, A. L.; Mohr, S.; Day, P. J. R. High-throughput droplet PCR. Methods 2010, 50,

277-281.

44. Gu, S.; Lu, Y.; Ding, Y.; Li, L.; Zhang, F.; Wu, Q. Droplet-based microfluidics for dose–

response assay of enzyme inhibitors by electrochemical method. Anal. Chim. Acta 2013,

796, 68-74.

45. Gielen, F.; van Vliet, L.; Koprowski, B. T.; Devenish, S. R. A.; Fischlechner, M.; Edel, J.

B.; Niu, X.; deMello, A. J.; Hollfelder, F. A Fully Unsupervised Compartment-on-

Demand Platform for Precise Nanoliter Assays of Time-Dependent Steady-State Enzyme

Kinetics and Inhibition. Anal. Chem. 2013, 85, 4761-4769.

46. Clausell-Tormos, J.; Lieber, D.; Baret, J.; El-Harrak, A.; Miller, O. J.; Frenz, L.;

Blouwolff, J.; Humphry, K. J.; Köster, S.; Duan, H.; Holtze, C.; Weitz, D. A.; Griffiths,

A. D.; Merten, C. A. Droplet-Based Microfluidic Platforms for the Encapsulation and

Screening of Mammalian Cells and Multicellular Organisms. Chem. Biol. 2008, 15, 427-

437.

47. Brouzes, E.; Medkova, M.; Savenelli, N.; Marran, D.; Twardowski, M.; Hutchison, J. B.;

Rothberg, J. M.; Link, D. R.; Perrimon, N.; Samuels, M. L. Droplet microfluidic

technology for single-cell high-throughput screening. Proceedings of the National

Academy of Sciences 2009, 106, 14195-14200.

48. Yu, L.; Chen, M. C. W.; Cheung, K. C. Droplet-based microfluidic system for

multicellular tumor spheroid formation and anticancer drug testing. Lab Chip 2010, 10,

2424-2432.

49. Boedicker, J. Q.; Li, L.; Kline, T. R.; Ismagilov, R. F. Detecting bacteria and determining

their susceptibility to antibiotics by stochastic confinement in nanoliter droplets using

plug-based microfluidics. Lab Chip 2008, 8, 1265-1272.

50. Jakiela, S.; Kaminski, T. S.; Cybulski, O.; Weibel, D. B.; Garstecki, P. Bacterial Growth

and Adaptation in Microdroplet Chemostats. Angew. Chem. Int. Ed. 2013, 52, 8908-

8911.

51. Trivedi, V.; Doshi, A.; Kurup, G. K.; Ereifej, E.; Vandevord, P. J.; Basu, A. S. A modular

approach for the generation, storage, mixing, and detection of droplet libraries for high

throughput screening. Lab Chip 2010, 10, 2433-2442.

52. Cao, J.; Kursten, D.; Schneider, S.; Knauer, A.; Gunther, P. M.; Kohler, J. M. Uncovering

toxicological complexity by multi-dimensional screenings in microsegmented flow:

modulation of antibiotic interference by nanoparticles. Lab Chip 2012, 12, 474-484.

220

53. Granieri, L.; Baret, J.; Griffiths, A. D.; Merten, C. A. High-Throughput Screening of

Enzymes by Retroviral Display Using Droplet-Based Microfluidics. Chem. Biol. 2010,

17, 229-235.

54. Baret, J.; Beck, Y.; Billas-Massobrio, I.; Moras, D.; Griffiths, A. D. Quantitative Cell-

Based Reporter Gene Assays Using Droplet-Based Microfluidics. Chem. Biol. 2010, 17,

528-536.

55. Skhiri, Y.; Beneyton, T.; Mazutis, L.; Baret, J.; El Harrak, A.; Mayot, E.; Griffiths, A.;

Taly, V. Integrated microfluidic platform for directed evolution of biocatalysts for

biofuel cell applications; 14th International Conference on Miniaturized Systems for

Chemistry and Life Sciences; µTAS: 2010; , pp 980.

56. Miller, O. J.; El Harrak, A.; Mangeat, T.; Baret, J. C.; Frenz, L.; El Debs, B.; Mayot, E.;

Samuels, M. L.; Rooney, E. K.; Dieu, P.; Galvan, M.; Link, D. R.; Griffiths, A. D. High-

resolution dose-response screening using droplet-based microfluidics. Proc. Natl. Acad.

Sci. U. S. A. 2012, 109, 378-383.

57. Colin, P.; Kintses, B.; Gielen, F.; Miton, C. M.; Fischer, G.; Mohamed, M. F.; Hyvonen,

M.; Morgavi, D. P.; Janssen, D. B.; Hollfelder, F. Ultrahigh-throughput discovery of

promiscuous enzymes by picodroplet functional metagenomics. Nat Commun 2015, 6.

58. Fradet, E.; Bayer, C.; Hollfelder, F.; Baroud, C. N. Measuring Fast and Slow Enzyme

Kinetics in Stationary Droplets. Anal. Chem. 2015, 87, 11915-11922.

59. Chen, Y.; - Wijaya Gani, A.; - Tang, S. K. Y. - Characterization of sensitivity and

specificity in leaky droplet-based assays. Lab Chip 2012, - 5093.

60. Courtois, F.; Olguin, L. F.; Whyte, G.; Theberge, A. B.; Huck, W. T. S.; Hollfelder, F.;

Abell, C. Controlling the Retention of Small Molecules in Emulsion Microdroplets for

Use in Cell-Based Assays. Anal. Chem. 2009, 81, 3008-3016.

61. Baret, J.; Kleinschmidt, F.; El Harrak, A.; Griffiths, A. D. Kinetic Aspects of Emulsion

Stabilization by Surfactants: A Microfluidic Analysis. Langmuir 2009, 25, 6088-6093.

62. Baret, J. Surfactants in droplet-based microfluidics. Lab Chip 2012, 12, 422-433.

63. Debon, A. P.; Wootton, R. C. R.; Elvira, K. S. Droplet confinement and leakage: Causes,

underlying effects, and amelioration strategies. Biomicrofluidics 2015, 9, 024119.

64. Gupta, R.; Rousseau, D. Surface-active solid lipid nanoparticles as Pickering stabilizers

for oil-in-water emulsions. Food Funct. 2012, 3, 302-311.

65. Pan, M.; Rosenfeld, L.; Kim, M.; Xu, M.; Lin, E.; Derda, R.; Tang, S. K. Y. Fluorinated

Pickering Emulsions Impede Interfacial Transport and Form Rigid Interface for the

Growth of Anchorage-Dependent Cells. ACS Appl. Mater. Interfaces 2014, 6, 21446-

21453.

66. Becker, H. Microfluidics: a technology coming of age. Med. Device Technol. 2008, 19,

21-24.

67. Becker, H. - Collective wisdom. - Lab Chip 2010, - 1351.

221

68. Gartner, I. Gartner Hype Cycle.

http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp (accessed

March, 07, 2016).

69. Kaminski, T. S.; Churski, K.; Garstecki, P. Microdroplet Technology: Principles and

Emerging Applications in Biology and Chemistry. 2012, 117-136.

70. Churski, K.; Kaminski, T. S.; Jakiela, S.; Kamysz, W.; Baranska-Rybak, W.; Weibel, D.

B.; Garstecki, P. Rapid screening of antibiotic toxicity in an automated microdroplet

system. Lab Chip 2012, 12, 1629-1637.

71. Dressler, O. J.; Maceiczyk, R. M.; Chang, S.; deMello, A. J. Droplet-Based

Microfluidics: Enabling Impact on Drug Discovery. Journal of Biomolecular Screening

2013.

72. Du, G.; Fang, Q.; den Toonder, J. M. J. Microfluidics for cell-based high throughput

screening platforms—A review. Anal. Chim. Acta 2016, 903, 36-50.

73. Wong, S. S. W.; Samaranayake, L. P.; Seneviratne, C. J. In pursuit of the ideal antifungal

agent for Candida infections: high-throughput screening of small molecules. Drug

Discov. Today 2014, 19, 1721-1730.

74. Joachim, J.; Jiang, M.; McKnight, N. C.; Howell, M.; Tooze, S. A. High-throughput

screening approaches to identify regulators of mammalian autophagy. Methods 2015, 75,

96-104.

75. Acker, M. G.; Auld, D. S. Considerations for the design and reporting of enzyme assays

in high-throughput screening applications. Perspectives in Science 2014, 1, 56-73.

76. An, P.; Winters, D.; Walker, K. W. Automated high-throughput dense matrix protein

folding screen using a liquid handling robot combined with microfluidic capillary

electrophoresis. Protein Expr. Purif. 2016, 120, 138-147.

77. Hao, S.; Song, M. Technology-driven strategy and firm performance: Are strategic

capabilities missing links? Journal of Business Research 2016, 69, 751-759.

78. Hung, C.; Lee, W. A proactive technology selection model for new technology: The case

of 3D IC TSV. Technological Forecasting and Social Change 2016, 103, 191-202.

79. Colombo, M. G.; D’Adda, D.; Pirelli, L. H. The participation of new technology-based

firms in EU-funded R&D partnerships: The role of venture capital. Research Policy

2016, 45, 361-375.

80. Chen, C.; Zhang, J.; Guo, R. The D-Day, V-Day, and bleak days of a disruptive

technology: A new model for ex-ante evaluation of the timing of technology disruption.

Eur. J. Oper. Res. 2016, 251, 562-574.

81. Whitesides, G. M. The origins and the future of microfluidics. Nature 2006, 442, 368-

373.

82. Hocquellet, A.; Odaert, B.; Cabanne, C.; Noubhani, A.; Dieryck, W.; Joucla, G.; Le

Senechal, C.; Milenkov, M.; Chaignepain, S.; Schmitter, J.; Claverol, S.; Santarelli, X.;

Dufourc, E. J.; Bonneu, M.; Garbay, B.; Costaglioli, P. Structure–activity relationship of

human liver-expressed antimicrobial peptide 2. Peptides 2010, 31, 58-66.

222

83. Casey, J. T.; O'Cleirigh, C.; Walsh, P. K.; O'Shea, D. G. Development of a robust

microtiter plate-based assay method for assessment of bioactivity. J. Microbiol. Methods

2004, 58, 327-334.

84. Matteucci, M.; Christiansen, T. L.; Tanzi, S.; Østergaard, P. F.; Larsen, S. T.; Taboryski,

R. Fabrication and characterization of injection molded multi level nano and microfluidic

systems. Microelectronic Engineering 2013, 111, 294-298.

85. Hopmann, C.; Fischer, T. New plasticising process for increased precision and reduced

residence times in injection moulding of micro parts. CIRP Journal of Manufacturing

Science and Technology 2015, 9, 51-56.

86. Sarker, S. D.; Nahar, L.; Kumarasamy, Y. Microtitre plate-based antibacterial assay

incorporating resazurin as an indicator of cell growth, and its application in the in vitro

antibacterial screening of phytochemicals. Methods 2007, 42, 321-324.

87. Bevan, P.; Ryder, H.; Shaw, I. Identifying small-molecule lead compounds: The

screening approach to drug discovery. Trends Biotechnol. 1995, 13, 115-121.

88. Zeng, Y.; Shin, M.; Wang, T. - Programmable active droplet generation enabled by

integrated pneumatic micropumps. Lab Chip 2013, - 267.

89. Grover, W. H.; Skelley, A. M.; Liu, C. N.; Lagally, E. T.; Mathies, R. A. Monolithic

membrane valves and diaphragm pumps for practical large-scale integration into glass

microfluidic devices. Sensors Actuators B: Chem. 2003, 89, 315-323.

90. Unger, M. A.; Chou, H.; Thorsen, T.; Scherer, A.; Quake, S. R. Monolithic

Microfabricated Valves and Pumps by Multi-layer Soft Lithography. Science 2000, 113.

91. Lin, Y. H.; Chen, Y. J.; Lai, C. S.; Chen, Y. T.; Chen, C. L.; Yu, J. S.; Chang, Y. S. A

negative-pressure-driven microfluidic chip for the rapid detection of a bladder cancer

biomarker in urine using bead-based enzyme-linked immunosorbent assay.

Biomicrofluidics 2013, 7, 24103.

92. Gielen, F.; Buryska, T.; Vliet, L. V.; Butz, M.; Damborsky, J.; Prokop, Z.; Hollfelder, F.

Interfacing Microwells with Nanoliter Compartments: A Sampler Generating High-

Resolution Concentration Gradients for Quantitative Biochemical Analyses in Droplets.

Anal. Chem. 2015, 87, 624-632.

93. Abate, A. R.; Weitz, D. A. Syringe-vacuum microfluidics: A portable technique to create

monodisperse emulsions. Biomicrofluidics 2011, 5, 014107.

94. Olechno, J.; Shieh, J.; Ellson, R. Improving IC50 Results with Acoustic Droplet Ejection.

Journal of the Association for Laboratory Automation 2006, 11, 240-246.

95. Abate, A. R.; Hung, T.; Mary, P.; Agresti, J. J.; Weitz, D. A. High-throughput injection

with microfluidics using picoinjectors. Proceedings of the National Academy of Sciences

2010, 107, 19163-19166.

96. Song, H.; Chen, D. L.; Ismagilov, R. F. Reactions in Droplets in Microfluidic Channels.

Angewandte Chemie International Edition 2006, 45, 7336-7356.

223

97. Kim, C.; Lee, K. S.; Kim, Y. E.; Lee, K.; Lee, S. H.; Kim, T. S.; Kang, J. Y. Rapid

exchange of oil-phase in microencapsulation chip to enhance cell viability. Lab Chip

2009, 9, 1294-1297.

98. Jiang, Q.; Zhang, S. Z.; Peng, J. P.; Wang, X. L. Preparation and in vitro studies of

microencapsulated cells releasing human tissue inhibitor of metalloproteinase-2. J.

Zhejiang Univ. Sci. B. 2005, 6, 859-864.

99. Lama, R.; Zhang, L.; Naim, J. M.; Williams, J.; Zhou, A.; Su, B. Development, validation

and pilot screening of an in vitro multi-cellular three-dimensional cancer spheroid assay

for anti-cancer drug testing. Bioorg. Med. Chem. 2013, 21, 922-931.

100. Friedrich, J.; Seidel, C.; Ebner, R.; Kunz-Schughart, L. Spheroid-based drug screen:

considerations and practical approach. Nat. Protocols 2009, 4, 309-324.

101. Ho, W. Y.; Yeap, S. K.; Ho, C. L.; Rahim, R. A.; Alitheen, N. B. Development of

Multicellular Tumor Spheroid (MCTS) Culture from Breast Cancer Cell and a High

Throughput Screening Method Using the MTT Assay. PLoS ONE 2012, 7, e44640.

102. Lagus, T. P.; Edd, J. F. High Throughput Single-cell and Multiple-cell Micro-

encapsulation. J Vis Exp 2012, e4096.

103. Todd P Lagus and Jon,F.Edd A review of the theory, methods and recent applications of

high-throughput single-cell droplet microfluidics. J. Phys. D 2013, 46, 114005.

104. Wilson, J. L.; Najia, M. A.; Saeed, R.; McDevitt, T. C. Alginate encapsulation

parameters influence the differentiation of microencapsulated embryonic stem cell

aggregates. Biotechnol. Bioeng. 2014, 111, 618-631.

105. Edd, J. F.; Di Carlo, D.; Humphry, K. J.; Koster, S.; Irimia, D.; Weitz, D. A.; - Toner,

M. Controlled encapsulation of single-cells into monodisperse picolitre drops. Lab Chip

2008, 1262.

106. Alvetex Alvetex. http://reinnervate.com/.

107. Nanofiber Solutions Nanofiber. http://www.nanofibersolutions.com/products.html.

108. Mimetix Mimetix. http://www.electrospinning.co.uk/product/mimetix-96-well-plate-3d-

cell-based-assays/.

109. Insphero Insphero. http://www.insphero.com/.

110. Curiox Curiox. http://www.curiox.com/.

111. Workman, V. L.; Dunnett, S. B.; Kille, P.; Palmer, D. D. Microfluidic chip-based

synthesis of alginate microspheres for encapsulation of immortalized human cells.

Biomicrofluidics 2007, 1.

112. Workman, V. L.; Dunnett, S. B.; Kille, P.; Palmer, D. D. On-Chip Alginate

Microencapsulation of Functional Cells. Macromolecular Rapid Communications 2008,

29, 165-170.

224

113. Crowther, D. C.; van Vliet, L.; Kuhaudomlarp, S.; Gielen, F.; Yan, J.; Azhar, M.;

Hinault, M.; Hollfelder, F. An Assay For Seeded Protein Aggregration Detects Abeta

Seeds In Serum. Alzheimer's & Dementia 2014, 10, P271.

114. Tresset, G.; Takeuchi, S. A Microfluidic Device for Electrofusion of Biological

Vesicles. Biomed. Microdevices 2004, 6, 213-218.

115. Terry, S. C.; Jerman, J. H.; Angell, J. B. A gas chromatographic air analyzer fabricated

on a silicon wafer. IEEE Trans. Electron Devices 1979, 26, 1880-1886.

116. Becker, H.; Gärtner, C. Polymer microfabrication methods for microfluidic analytical

applications. Electrophoresis 2000, 21, 12-26.

117. Fielden, P. R.; Baldock, S. J.; Goddard, N. J.; Morrison, L.; Prest, J. E.; Brown, B. J. T.;

Zgraggen, M. In In Micromolded polymer electrokinetic separation systems with

variable volume sampling and integrated optical and conductivity detection; Bornhop, D.

J., Dunn, D. A., Mariella, R. P., Jr., Murphy, C. J., Nicolau, D. V., Nie, S., Palmer, M.

and Raghavachari, R., Eds.; SPIE: 2002; Vol. 4626, pp 429-440.

118. Baldock, S. J.; Fielden, P. R.; Goddard, N. J.; Prest, J. E.; Treves Brown, B. J. Integrated

moulded polymer electrodes for performing conductivity detection on isotachophoresis

microdevices. Journal of Chromatography A 2003, 990, 11-22.

119. Prest, J. E.; Baldock, S. J.; Day, P. J. R.; Fielden, P. R.; Goddard, N. J.; Treves Brown,

B. J. Miniaturised isotachophoresis of DNA. Journal of Chromatography A 2007, 1156,

154-159.

120. Baldock, S. J.; Fielden, P. R.; Goddard, N. J.; Kretschmer, H. R.; Prest, J. E.; Brown, B.

J. T. A versatile sample injection system for miniaturised isotachophoresis devices.

Microelectronic Engineering 2008, 85, 1440-1442.

121. Charles S. Henry; Min Zhong; Susan M. Lunte; Moon Kim; Haim Bau; Jorge J.

Santiago Ceramic microchips for capillary electrophoresis, electrochemistry. Anal.

Commun. 1999, 36, 305-307.

122. Chiem, N.; Harrison, D. J. Microchip-Based Capillary Electrophoresis for

Immunoassays: Analysis of Monoclonal Antibodies and Theophylline. Anal. Chem.

1997, 69, 373-378.

123. Jacobson, S. C.; Ramsey, J. M. Electrokinetic Focusing in Microfabricated Channel

Structures. Anal. Chem. 1997, 69, 3212-3217.

124. Xia, Y.; Whitesides, G. M. Soft Lithography. Annual Review of Materials Science 1998,

28, 153-184.

125. Hung, L.; Lin, R.; Lee, A. P. Rapid microfabrication of solvent-resistant biocompatible

microfluidic devices. Lab on a Chip 2008, 8, 983-987.

126. McCormick, R. M.; Nelson, R. J.; Alonso-Amigo, M.; Benvegnu, D. J.; Hooper, H. H.

Microchannel Electrophoretic Separations of DNA in Injection-Moulded Plastic

Substrates. Anal. Chem. 1997, 69, 2626-2630.

225

127. Roberts, M. A.; Rossier, J. S.; Bercier, P.; Girault, H. UV Laser Machined Polymer

Substrates for the Development of Microdiagnostic Systems. Anal. Chem. 1997, 69,

2035-2042.

128. Zourob, M.; Mohr, S.; Mayes, A. G.; Macaskill, A.; Perez-Moral, N.; Fielden, P. R.;

Goddard, N. J. A micro-reactor for preparing uniform molecularly imprinted polymer

beads. Lab Chip 2006, 6, 296-301.

129. Vinothkumar, T. S.; Deivanayagam, K.; Ganesh, A.; Kumar, D. Influence of different

organic solvents on degree of swelling of poly (dimethyl siloxane)-based sealer. Journal

of Conservative Dentistry : JCD 2010, 14, 156-159.

130. Romero, P. A.; Tran, T. M.; Abate, A. R. Dissecting enzyme function with microfluidic-

based deep mutational scanning. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 7159-7164.

131. Fujii, T. PDMS-based microfluidic devices for biomedical applications. Microelectronic

Engineering 2002, 61–62, 907-914.

132. Friend, J.; Yeo, L. Fabrication of microfluidic devices using polydimethylsiloxane.

Biomicrofluidics 2009, 4, 026502.

133. Chen, L.; Luo, G.; Liu, K.; Ma, J.; Yao, B.; Yan, Y.; Wang, Y. Bonding of glass-based

microfluidic chips at low- or room-temperature in routine laboratory. Sensors Actuators

B: Chem. 2006, 119, 335-344.

134. Wang, H. Y.; Foote, R. S.; Jacobson, S. C.; Schneibel, J. H.; Ramsey, J. M. Low

temperature bonding for microfabrication of chemical analysis devices. Sensors

Actuators B: Chem. 1997, 45, 199-207.

135. Baldock, S. J.; Fielden, P. R.; Goddard, N. J.; Kretschmer, H. R.; Prest, J. E.; Treves

Brown, B. J. Novel variable volume injector for performing sample introduction in a

miniaturised isotachophoresis device. Journal of Chromatography A 2004, 1042, 181-

188.

136. Fuchs, A.; Kanoufi, F.; Combellas, C.; Shanahan, M. E. R. Wetting and surface

properties of (modified) fluoro-silanised glass. Colloids Surf. Physicochem. Eng. Aspects

2007, 307, 7-15.

137. Caldwell, J. R.; Jackson, W. J. Surface treatment of polycarbonate films with amines.

Journal of Polymer Science Part C: Polymer Symposia 1968, 24, 15-23.

138. Fidalgo, L. M.; Abell, C.; Huck, W. T. S. Surface-induced droplet fusion in microfluidic

devices. Lab Chip 2007, 984.

139. Shea, M.; Ommert, S.; Gleich, L.; Towle, T.; Haspel, H.; Schmid, N.; Jindal, H.;

Kellogg, G.; Carvalho, B. ADMET Assays on Tecan's LabCD-ADMET System. Journal

of the Association for Laboratory Automation 2003, 8, 74-77.

140. Baroud, C. N.; Gallaire, F.; Dangla, R. Dynamics of microfluidic droplets. Lab Chip

2010, 10, 2032-2045.

141. Gu, H.; Duits, M. H. G.; Mugele, F. Droplets Formation and Merging in Two-Phase

Flow Microfluidics. International Journal of Molecular Sciences 2011, 12, 2572-2597.

226

142. Fu, T.; Wu, Y.; Ma, Y.; Li, H. Z. Droplet formation and breakup dynamics in

microfluidic flow-focusing devices: From dripping to jetting. Chemical Engineering

Science 2012, 84, 207-217.

143. Madou, M. J. Fundamentals of Microfabrication: The Science of Miniaturisation,

Second Edition; CRC Press: 2002; , pp 752.

144. Bird, R. B. Transport Phenomena; Wiley: New York, 2002; .

145. Gyros Gyros. http://www.gyros.com/ (accessed 03/30, 2015).

146. NASA Tech Briefs FPGAs Yield Virtual Laser Valves for Microfluidics.

http://www.techbriefs.com/component/content/article/27-ntb/features/application-

briefs/11615 (accessed 03/30, 2015).

147. Zhang, L.; Yin, X. Parallel separation of multiple samples with negative pressure sample

injection on a 3-D microfluidic array chip. Electrophoresis 2007, 28, 1281-1288.

148. Glawdel, T.; Ren, C. L. Electro-osmotic flow control for living cell analysis in

microfluidic PDMS chips. Mech. Res. Commun. 2009, 36, 75-81.

149. Zhao, C.; Yang, C. Electro-osmotic mobility of non-Newtonian fluids. Biomicrofluidics

2011, 5, 014110.

150. Cho, H.; Kim, H.; Kang, J. Y.; Kim, T. S. How the capillary burst microvalve works. J.

Colloid Interface Sci. 2007, 306, 379-385.

151. Yang, S.; Undar, A.; Zahn, J. D. A microfluidic device for continuous, real time blood

plasma separation. Lab Chip 2006, 6, 871-880.

152. Lee, M. G.; Shin, J. H.; Bae, C. Y.; Choi, S.; Park, J. Label-Free Cancer Cell Separation

from Human Whole Blood Using Inertial Microfluidics at Low Shear Stress. Anal.

Chem. 2013, 85, 6213-6218.

153. Lea, W. A.; Simeonov, A. Fluorescence Polarization Assays in Small Molecule

Screening. Expert opinion on drug discovery 2011, 6, 17-32.

154. Song, Y.; Liao, J. An In Vitro Forster Resonance Energy Transfer-Based High-

Throughput Screening Assay for Inhibitors of Protein–Protein Interactions in

SUMOylation Pathway. Assay and Drug Development Technologies 2012, 10, 336-343.

155. Degorce, F.; Card, A.; Soh, S.; Trinquet, E.; Knapik, G.; Xie, B. HTRF: A Technology

Tailored for Drug Discovery –A Review of Theoretical Aspects and Recent

Applications. Curr Chem Genomics 2009, 22.

156. Frackowiak, D. The Jablonski diagram. Journal of Photochemistry and Photobiology B:

Biology 1988, 2, 399.

157. Mohr, S.; Fisher, K.; Scrutton, N. S.; Goddard, N. J.; Fielden, P. R. Continuous two-

phase flow miniaturised bioreactor for monitoring anaerobic biocatalysis by

pentaerythritol tetranitrate reductase. Lab Chip 2010, 10, 1929-1936.

158. Adcock, J. L.; Barnett, N. W.; Francis, P. S. Luminescence - Overview. In Reference

Module in Chemistry, Molecular Sciences and Chemical Engineering Elsevier: 2014; .

227

159. Mirasoli, M.; Guardigli, M.; Michelini, E.; Roda, A. Recent advancements in chemical

luminescence-based lab-on-chip and microfluidic platforms for bioanalysis. J. Pharm.

Biomed. Anal. 2014, 87, 36-52.

160. Kertesz, V.; Van Berkel, G. J. Fully automated liquid extraction-based surface sampling

and ionization using a chip-based robotic nanoelectrospray platform. Journal of Mass

Spectrometry 2010, 45, 252-260.

161. Lazar, I. M.; Grym, J.; Foret, F. Microfabricated devices: A new sample introduction

approach to mass spectrometry. Mass Spectrom. Rev. 2006, 25, 573-594.

162. Fidalgo, L. M.; Whyte, G.; Ruotolo, B. T.; Benesch, J. L. P.; Stengel, F.; Abell, C.;

Robinson, C. V.; Huck, W. T. S. Coupling Microdroplet Microreactors with Mass

Spectrometry: Reading the Contents of Single Droplets Online13. Angewandte Chemie

International Edition 2009, 48, 3665-3668.

163. Ji, J.; Nie, L.; Qiao, L.; Li, Y.; Guo, L.; Liu, B.; Yang, P.; Girault, H. H. Proteolysis in

microfluidic droplets: an approach to interface protein separation and peptide mass

spectrometry. Lab Chip 2012, 12, 2625-2629.

164. Gasilova, N.; Yu, Q.; Qiao, L.; Girault, H. H. On-Chip Spyhole Mass Spectrometry for

Droplet-Based Microfluidics. Angewandte Chemie International Edition 2014.

165. Angel, T. E.; Aryal, U. K.; Hengel, S. M.; Baker, E. S.; Kelly, R. T.; Robinson, E. W.;

Smith, R. D. Mass spectrometry-based proteomics: existing capabilities and future

directions. Chem. Soc. Rev. 2012, 41, 3912-3928.

166. Tanyanyiwa, J.; Leuthardt, S.; Hauser, P. C. Conductimetric and potentiometric

detection in conventional and microchip capillary electrophoresis. Electrophoresis 2002,

23, 3659-3666.

167. Wani, T. A.; Khalil, N. Y.; Abdel-Rahman, H.; Darwish, I. A. Novel microwell-based

spectrophotometric assay for determination of atorvastatin calcium in its pharmaceutical

formulations. Chemistry Central Journal 2011, 5, 1-8.

168. Jones, A. J. Y.; Hirst, J. A spectrophotometric coupled enzyme assay to measure the

activity of succinate dehydrogenase. Anal. Biochem. 2013, 442, 19-23.

169. Haggag, R. S.; Gawad, D. A.; Belal, S. F.; Elbardisy, H. M. Spectrophotometric and

spectrofluorimetric determination of mesna, acetylcysteine and timonacic acid through

the reaction with acetoxymercuri fluorescein. Anal. Methods 2016, 8, 2479-2493.

170. Gajraj, A.; Ofoli, R. Y. Effect of Extrinsic Fluorescent Labels on Diffusion and

Adsorption Kinetics of Proteins at the Liquid–Liquid Interface . Langmuir 2000, 16,

8085-8094.

171. Sugiura, S.; Nakajima, M.; Iwamoto, S.; Seki, M. Interfacial Tension Driven

Monodispersed Droplet Formation from Microfabricated Channel Array. Langmuir

2001, 17, 5562-5566.

172. White, H. E. Modern College Physics; van Nostrand: 1948 .

228

173. Messager, A.; Miracle-Sole, S.; Ruiz, J.; Shlosman, S. Interfaces in the Potts model II:

Antonov's rule and rigidity of the order disorder interface. Comms in Mathematical Phy.

1991, 140, 275-290.

174. Ozkan, M.; Wang, M.; Ozkan, C.; Flynn, R.; Esener, S. Optical manipulation of objects

and biological cells in microfluidic devices. Biomed. Microdevices 2003, 5, 61.

175. Holt, D. J.; Payne, R. J.; Chow, W. Y.; Abell, C. Fluorosurfactants for microdroplets:

Interfacial tension analysis. J. Colloid Interface Sci. 2010, 350, 205-211.

176. Roach, L. S.; Song, H.; Ismagilov, R. F. Controlling Nonspecific Protein Adsorption in a

Plug-Based Microfluidic System by Controlling Interfacial Chemistry Using Fluorous-

Phase Surfactants. Anal. Chem. 2005, 77, 785-796.

177. Holtze, C.; Rowat, A. C.; Agresti, J. J.; Hutchison, J. B.; Angile, F. E.; Schmitz, C. H. J.;

Koster, S.; - Duan, H.; - Humphry, K. J.; Scanga, R. A.; Johnson, J. S.; Pisignano, D.;

Weitz, D. A. Biocompatible surfactants for water-in-fluorocarbon emulsions. Lab Chip

2008, 1632.

178. Goldburg, W. I. Dynamic light scattering. American Journal of Physics 1999, 67, 1152-

1160.

179. Song, H.; Tice, J. D.; Ismagilov, R. F. A Microfluidic System for Controlling Reaction

Networks in Time. Angewandte Chemie International Edition 2003, 42, 768-772.

180. Zheng, B.; Roach, L. S.; Ismagilov, R. F. Screening of Protein Crystallization

Conditions on a Microfluidic Chip Using Nanoliter-Size Droplets. J. Am. Chem. Soc.

2003, 125, 11170-11171.

181. Garstecki, P.; Fuerstman, M. J.; Stone, H. A.; Whitesides, G. M. Formation of droplets

and bubbles in a microfluidic T-junction-scaling and mechanism of break-up. Lab on a

Chip 2006, 6, 437-446.

182. De Menech, M.; Garstecki, P.; Jousse, F.; Stone, H. A. Transition from squeezing to

dripping in a microfluidic T-shaped junction. J. Fluid Mech. 2008, 595, 141.

183. Ralf Seemann and Martin Brinkmann and Thomas Pfohl and,Stephan Herminghaus

Droplet based microfluidics. Reports on Progress in Physics 2012, 75, 016601.

184. Tice, J. D.; Song, H.; Lyon, A. D.; Ismagilov, R. F. Formation of Droplets and Mixing

in Multiphase Microfluidics at Low Values of the Reynolds and the Capillary Numbers.

Langmuir 2003, 19, 9127-9133.

185. Tice, J. D.; Lyon, A. D.; Ismagilov, R. F. Effects of viscosity on droplet formation and

mixing in microfluidic channels. Anal. Chim. Acta 2004, 507, 73-77.

186. Rane, T. D.; Zec, H.; Puleo, C.; Lee, A. P.; Wang, T. Droplet microfluidics for

amplification-free genetic detection of single cells. Lab on a chip 2012, 12, 3341-3347.

187. Zhang, S.; Guivier-Curien, C.; Veesler, S.; Candoni, N. Prediction of sizes and

frequencies of nanoliter-sized droplets in cylindrical T-junction microfluidics. Chemical

Engineering Science 2015, 138, 128-139.

229

188. Pompano, R. R.; Li, H.; Ismagilov, R. F. Rate of Mixing Controls Rate and Outcome of

Autocatalytic Processes: Theory and Microfluidic Experiments with Chemical Reactions

and Blood Coagulation. Biophys. J. 2008, 95, 1531-1543.

189. Suea-Ngam, A.; Rattanarat, P.; Chailapakul, O.; Srisa-Art, M. Electrochemical droplet-

based microfluidics using chip-based carbon paste electrodes for high-throughput

analysis in pharmaceutical applications. Anal. Chim. Acta 2015, 883, 45-54.

190. Su, Y. -.; Kim, H.; Kovenklioglu, S.; Lee, W. Y. Continuous nanoparticle production by

microfluidic-based emulsion, mixing and crystallization. Journal of Solid State

Chemistry 2007, 180, 2625-2629.

191. Mazutis, L.; Gilbert, J.; Ung, W. L.; Weitz, D. A.; Griffiths, A. D.; Heyman, J. A.

Single-cell analysis and sorting using droplet-based microfluidics. Nat. Protocols 2013,

8, 870-891.

192. Teh, S.; Lin, R.; Hung, L.; Lee, A. P. Droplet microfluidics. Lab on a Chip 2008, 8, 198-

220.

193. Prest, J. E.; Baldock, S. J.; Fielden, P. R.; Goddard, N. J.; Treves Brown, B. J. Analysis

of amino acids by miniaturised isotachophoresis. Journal of Chromatography A 2004,

1051, 221-226.

194. Biral, A.; Zanella, A. Introducing purely hydrodynamic networking functionalities into

microfluidic systems. Nano Communication Networks 2013, 4, 205-215.

195. Ahn, K.; Kerbage, C.; Hunt, T.; Westervelt, R.; Link, D.; Weitz, D. Dielectrophoretic

manipulation of drops for high-speed microfluidic sorting devices. Appl. Phys. Lett.

2006, 88, 024104.

196. Srivastava, S. K.; Baylon-Cardiel, J. L.; Lapizco-Encinas, B. H.; Minerick, A. R. A

continuous DC-insulator dielectrophoretic sorter of microparticles. Journal of

Chromatography A 2011, 1218, 1780-1789.

197. Chen, L.; Zheng, X.; Hu, N.; Yang, J.; Luo, H.; Jiang, F.; Liao, Y. Research Progress on

Microfluidic Chip of Cell Separation Based on Dielectrophoresis. Chinese Journal of

Analytical Chemistry 2015, 43, 300-309.

198. Zhang, K.; Liang, Q.; Ma, S.; Mu, X.; Hu, P.; Wang, Y.; Luo, G. On-chip manipulation

of continuous picoliter-volume superparamagnetic droplets using a magnetic force. Lab

Chip 2009, 9, 2992.

199. Lehmann, U.; Vandevyver, C.; Parashar, V. K.; Gijs, M. A. M. Droplet-Based DNA

Purification in a Magnetic Lab-on-a-Chip. Angewandte Chemie International Edition

2006, 45, 3062-3067.

200. Al‐Hetlani, E.; Hatt, O. J.; Vojtíšek, M.; Tarn, M. D.; Iles, A.; Pamme, N. Sorting and

Manipulation of Magnetic Droplets in Continuous Flow. AIP Conference Proceedings

2010, 1311, 167-175.

201. Lombardi, D.; Dittrich, P. S. Droplet microfluidics with magnetic beads: a new tool to

investigate drug--protein interactions. Analytical and Bioanalytical Chemistry 2010, 399,

347-352.

230

202. Berge, B.; Peseux, J. Variable Focal Lens controlled by an External Voltage: An

Application of Electrowetting. Eur. Phys. J. 2000, 3, 159.

203. Aijian, A. P.; Garrell, R. L. Digital Microfluidics for Automated Hanging Drop Cell

Spheroid Culture. Journal of Laboratory Automation 2014.

204. Lin, T.; Yao, D. Applications of EWOD Systems for DNA Reaction and Analysis. J.

Adhes. Sci. Technol. 2012, 26, 1789-1804.

205. Tan, Y.; Ho, Y.; Lee, A. P. Droplet coalescence by geometrically mediated flow in

microfluidic channels. Microfluid Nanofluid 2007, 3, 495-499.

206. Chen, Y.; Chang, W.; Fang, W.; Ting, S.; Yao, D.; Yang, J. Fission and fusion of

droplets in a 3-D crossing microstructure. Microfluid Nanofluid 2012, 13, 239.

207. Sencia, M.; Takeda, J.; Abe, S.; Nakamura, T. Induction of cell fusion of plant

protoplasts by electrical stimulation. Plant Cell Physiol. 1979, 20, 1441-1443.

208. Tan, W.; Takeuchi, S. Timing controllable electrofusion device for aqueous droplet-

based microreactors. Lab on a Chip 2006, 6, 757-763.

209. Chiu, D. T. A microfluidics platform for cell fusion. Curr. Opin. Chem. Biol. 2001, 5,

609-612.

210. Tan, Y.; Fisher, J. S.; Lee, A. I.; Cristini, V.; Lee, A. P. Design of microfluidic channel

geometries for the control of droplet volume, chemical concentration, and sorting. Lab

on a Chip 2004, 4, 292-298.

211. Jousse, F.; Farr, R.; Link, D. R.; Fuerstman, M. J.; Garstecki, P. Bifurcation of droplet

flows within capillaries. Phys. Rev. E. Stat. Nonlin Soft Matter Phys. 2006, 74, 036311.

212. Andrés, A.; Rosés, M.; Ràfols, C.; Bosch, E.; Espinosa, S.; Segarra, V.; Huerta, J. M.

Setup and validation of shake-flask procedures for the determination of partition

coefficients (log D) from low drug amounts. European Journal of Pharmaceutical

Sciences 2015, 76, 181-191.

213. Baldursdottir, S. G.; Fullerton, M. S.; Nielsen, S. H.; Jorgensen, L. Adsorption of

proteins at the oil/water interface—Observation of protein adsorption by interfacial shear

stress measurements. Colloids and Surfaces B: Biointerfaces 2010, 79, 41-46.

214. Osakai, T.; Yuguchi, Y.; Gohara, E.; Katano, H. Direct Label-free Electrochemical

Detection of Proteins Using the Polarized Oil/Water Interface. Langmuir 2010, 26,

11530-11537.

215. Holden, M. A.; Cremer, P. S. Microfluidic tools for studying the specific binding,

adsorption, and displacement of proteins at interfaces. Annu. Rev. Phys. Chem. 2005, 56,

369-387.

216. Abdulla Yusuf, H.; Baldock, S. J.; Barber, R. W.; Fielden, P. R.; Goddard, N. J.; Treves

Brown, B. J. Novel microsystems for concentration gradient generation through

computer optimization with validation using optical instrumentation. Microelectronic

Engineering 2008, 85, 1265-1268.

231

217. Ali, U.; Karim, K. J. B. A.; Buang, N. A. A Review of the Properties and Applications

of Poly (Methyl Methacrylate) (PMMA). Polymer Reviews 2015, 55, 678-705.

218. Nicholas J.Goddard; Singh, K.; Bounaira, F.; Richard J.Holmes; Sara J.Baldock; Lynsay

W.Pickering; Peter R.Fielden; Richard D.Snook Anti-Resonant Reflecting Optical

Waveguides (ARROWS) as Optimal Optical Detectors for µTAS Applications.

Department of Instrumentation and Analytical Science, UMIST 2007.

219. Puntarulo, S.; Cederbaum, A. I. Temperature dependence of the microsomal oxidation of

ethanol by cytochrome P450 and hydroxyl radical-dependent reactions. Arch. Biochem.

Biophys. 1989, 269, 569-575.

220. Hansen, L. D.; Transtrum, M. K.; Quinn, C.; Demarse, N. Enzyme-catalyzed and

binding reaction kinetics determined by titration calorimetry. Biochimica et Biophysica

Acta (BBA) - General Subjects 2016, 1860, 957-966.

221. Frenz, L.; Blank, K.; Brouzes, E.; Griffiths, A. D. Reliable microfluidic on-chip

incubation of droplets in delay-lines. Lab on a Chip 2009, 9, 1344-1348.

222. Brøsen, K.; Skjelbo, E.; Rasmussen, B. B.; Poulsen, H. E.; Loft, S. Fluvoxamine is a

potent inhibitor of cytochrome P4501A2. Biochem. Pharmacol. 1993, 45, 1211-1214.

223. Pastrakuljic, A.; Tang, B. K.; Roberts, E. A.; Kalow, W. Distinction of CYP1A1 and

CYP1A2 activity by selective inhibition using fluvoxamine and isosafrole. Biochem.

Pharmacol. 1997, 53, 531-538.

224. Vlahov, G.; Angelo, C. S. The Structure of Triglycerides of Monovarietal Olive Oils: A

13C-NMR Comparative Study. Lipid / Fett 1996, 98, 203-205.

225. de Boer AR; T, L.; van Elswijk DA; H, L.; WM, N.; H, I. On-line Coupling of High-

Performance Liquid Chromatography to a Continous-Flow Enzyme Assay Based on

Electrospray Ionisation Mass Spectrometry. Anal. Chem. 2004, 76, 3155-3161.

226. Zamfir, A. D. Recent advances in sheathless interfacing of capillary electrophoresis and

electrospray ionization mass spectrometry. Journal of Chromatography A 2007, 1159, 2-

13.

227. Sinclair, I.; Gallagher, R. Charged aerosol detection: factors for consideration in its use

as a generic quantitative detector. Chromatography Today 2008, 1, 5.

228. Sinclair, I.; Charles, I. Applications of the Charged Aerosol Detector in Compound

Management. Journal of Biomolecular Screening 2009, 14, 531-537.

229. Charles, I.; Sinclair, I.; Addison, D. H. Capture and Exploration of Sample Quality Data

to Inform and Improve the Management of a Screening Collection. Journal of

Laboratory Automation 2013.

230. (Anonymous) Kohonen network. Scholarpedia , 1568.

231. Spiegel, D. S.; Turner, E. L. Bayesian analysis of the astrobiological implications of

life’s early emergence on Earth. Proceedings of the National Academy of Sciences 2012,

109, 395-400.

232

232. Mahoney, D. W.; Therneau, T. M.; Heppelmann, C. J.; Higgins, L.; Benson, L. M.;

Zenka, R. M.; Jagtap, P.; Nelsestuen, G. L.; Bergen, H. R.; Oberg, A. L. Relative

Quantification: Characterization of bias, variability and fold changes in mass

spectrometry data from iTRAQ labeled peptides. Journal of proteome research 2011, 10,

4325-4333.

233. Haworth, D. Digital Astrophotography: The State of the Art. 2005, 15-29.

234. Palanuwech, J.; Coupland, J. N. Effect of surfactant type on the stability of oil-in-water

emulsions to dispersed phase crystallization. Colloids Surf. Physicochem. Eng. Aspects

2003, 223, 251-262.

235. Yu, X.; Yue, K.; Hsieh, I.; Li, Y.; Dong, X.; Liu, C.; Xin, Y.; Wang, H.; Shi, A.;

Newkome, G. R.; Ho, R.; Chen, E.; Zhang, W.; Cheng, S. Z. D. Giant surfactants

provide a versatile platform for sub-10-nm nanostructure engineering. Proceedings of the

National Academy of Sciences 2013, 110, 10078-10083.

236. Gladysz, J. A.; Curran, D. P.; Horvaith, I. T. Handbook of Fluorous Chemistry; Wiley-

VCH: Weinheim, Germany, 2004; .

237. Sahyun, M. R. V. Binding of Aromatic Compounds to Bovine Serum Albumin. Nature

1966, 209, 613-614.

238. Ozeki, S.; Tejima, K. Drug Interactions. V. Binding of Basic Compounds to Bovine

Serum Albumin by Fluorescent Probe Technique. Chem. Pharm. Bull. 1979, 27, 638-

646.

239. Sykes, P. J.; Fau, N. S.; Fau, B. M.; Hughes, E. F.; Condon J Fau - Morley,,A.A.;

Morley, A. A. Quantitation of targets for PCR by use of limiting dilution. BioTechniques

JID - 8306785 1119.

240. Vogelstein, B.; Kinzler, K. W. Digital PCR. Proceedings of the National Academy of

Sciences 1999, 96, 9236-9241.

241. Niu, X.; Gielen, F.; Edel, J. B.; deMello, A. J. A microdroplet dilutor for high-

throughput screening. Nat Chem 2011, 3, 437-442.

242. Chaudhuri, R. G.; Paria, S. Dynamic contact angles on PTFE surface by aqueous

surfactant solution in the absence and presence of electrolytes. J. Colloid Interface Sci.

2009, 337, 555-562.

243. Henry, A. C.; Tutt, T. J.; Galloway, M.; Davidson, Y. Y.; McWhorter, C. S.; Soper, S.

A.; McCarley, R. L. Surface Modification of Poly(methyl methacrylate) Used in the

Fabrication of Microanalytical Devices. Anal. Chem. 2000, 72, 5331-5337.

244. Chabert, M.; Viovy, J. Microfluidic high-throughput encapsulation and hydrodynamic

self-sorting of single cells. Proceedings of the National Academy of Sciences 2008, 105,

3191-3196.

245. Goldner, L. S.; Jofre, A. M.; Tang, J. Droplet Confinement and Fluorescence

Measurement of Single Molecules. Methods In Enzymology 2010, 472, 61.

246. Lea, W. A.; Simeonov, A. Fluorescence Polarization Assays in Small Molecule

Screening. Expert opinion on drug discovery 2011, 6, 17-32.

233

247. Rujimethabhas, M.; Wilairat, P. Determination of critical micelle concentration using

acridine orange dye probe. An undergraduate experiment. J. Chem. Educ. 1978, 55, 342.

248. Furton, K. G.; Norelus, A. Determining the critical micelle concentration of aqueous

surfactant solutions: Using a novel colorimetric method. J. Chem. Educ. 1993, 70, 254.

249. Bielawska, M.; Chodzińska, A.; Jańczuk, B.; Zdziennicka, A. Determination of CTAB

CMC in mixed water + short-chain alcohol solvent by surface tension, conductivity,

density and viscosity measurements. Colloids Surf. Physicochem. Eng. Aspects 2013,

424, 81-88.

250. Alimuddin, M.; Grant, D.; Bulloch, D.; Lee, N.; Peacock, M.; Dahl, R. Determination of

logD via Automated Microfluidic Liquid-Liquid Extraction. J. Med. Chem. 2008, 51,

5140-5142.