1

Options for Automated Solubility Measurement In Options for Automated Solubility Measurement In Discovery and Developments:Discovery and Developments:

Science Issues and Practical SolutionsScience Issues and Practical Solutions

Arnon ChaitANALIZA, Inc.

2

OUTLINEOUTLINE

Solubility: the basics Solubility assays Particles in solution: why should we care? Focus: elemental solubility assay

– How does it work?– Sample data for pH, excipient screening

DMSO effect: The Good, the Bad, and the Ugly

How do we deal with impure samples?

3

Assay Effects: Summary (1)Assay Effects: Summary (1)

DRYDRY

DMSODMSO

TurbidityTurbidity

AbsorbanceAbsorbance

ElementalElemental

Small

Large

INCREASE

IndirectIndirect

DirectDirect

Variable/unknown

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Assay Effects: Summary (2)Assay Effects: Summary (2)

Solid particles are an integral part of the solubility assay– Particles are always present– They must be present for turbidity to work– They are artifacts in absorbance/elemental

assays

The effects of particles on the data must be considered when examining the data– Subtle to substantial influence on quality of

results

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Assay Effects: Summary (3)Assay Effects: Summary (3)

DMSO effect can be very large, and is unpredictable

Starting material has significant influence on the data

DMSO effect can not be neglected, even at small amounts

Late discovery and development should avoid DMSO if at all possible

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A Simple ConceptA Simple Concept

Solubility = Concentration of a dissolved compound in equilibrium with its solid

But:– Which solid?

Equilibrium (most stable form) vs. apparent (other forms)

– Which solvent? Buffers (intrinsic?) and co-solvents (kinetic)

– Which equilibrium? Time (kinetic) and temperature

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But It’s All in the DetailsBut It’s All in the Details

Conceptually a very easy experiment to conduct

But:– We don’t always have the right form (discovery)– We don’t always have time to wait for

equilibrium– Everything matters:

Probably one of the most sensitive thermodynamic experiments to conduct

– Different users refers to same term with different meanings

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Who Are The Users?Who Are The Users?

Discovery:– Obtain a measure of solubility of relevance to HTS and early

ADMET Can we determine if the compound will crash out of

solution?

– Obtain ADMET information of relevance for selecting amongst hits

Development:– Early lead characterization– Lead optimization– Formulation excipient selection

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Who Are The Users? (2)Who Are The Users? (2)

Will the compound crash out? solubility

Everyone else needs good data:

– For promoting a compound: binning may be sufficient

– For lead optimization: accurate data

– For formulation development: accurate data

– For anything regulatory: accurate data

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How Does a Compound Come Out of How Does a Compound Come Out of Solution?Solution?

Homogeneous vs. heterogeneous nucleation

Growth by dislocations (crystalline) vs. amorphous

G

rr*

http://monet.physik.unibas.ch/~lang/gallery/gallery.htm

Courtesy: Rick Rogers, NASA GRC

Hard spheresInsulin

YBCuO

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Solid FormsSolid Forms

Amorphous

Crystalline

Small molecules: Multiple crystalforms Amorphous is rare

Biomolecules: Single crystalform Amorphous is the norm

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Key Differentiating FactorKey Differentiating Factor

Elemental Concentration:CarbonNitrogen

...

Static Light Scattering

Absorbance

Evaporative LightScattering

No standards / calibration

No standards / calibration

Compound specific

Are you measuring the actual concentration?

Elemental:Yes, directly

Absorbance:Yes, indirectly

Turbidity:No, solubility is inferred from dilution factor off a standard

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What Controls the Size Distribution in Time?What Controls the Size Distribution in Time?

Ostwald coarsening: Particles nucleate at a minimum radius (critical radius)

Growth is an interplay between:oversaturation level in the liquidgain/loss from dissolving/growing neighbors

particles

The particle size distribution is a solution this interplay

Lifshitz-Slezov and Marqusee and Ross theories predict a continuous growth of larger particles at the expense of smaller ones: 1/3( , )f r t t

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But Why Should We Care?But Why Should We Care?We Can Always Filter the ParticlesWe Can Always Filter the Particles

You can NOT filter all the particles– You must have solid at solubility

Filtering simply places a dynamic cutoff limit to the size distribution function – but it will again change in time!

Filtering is necessary to reduce the potentially deleterious effects of particles

Log(Size) Log(Size)

Time

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Take Home Message #1Take Home Message #1

Particles are a fact of life:without them - we have no solubility

Particle size is changing dynamically

You can only interfere – for a while - with the natural size distribution by filtering

Each assay technique is sensitive to particles to a different degree

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Optical Properties of Particles in SolutionOptical Properties of Particles in Solution

Van de Hulst, 1957

Gold particles

Scattering is very small in comparison with absorbance when the solid particles are small

MeasuredSignal

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Can We Predict Optical Effects of Particles Can We Predict Optical Effects of Particles In Practice?In Practice?

Light scattering depends on:– Angle, refractive indices, sizes, cross section

efficiencies, concentration, wave length, structure factor, time… - in a nonlinear and non-intuitive way!

Plus:– Structure of the compound in solution changes with

concentration (monomers dimers trimers …)– Static light scattering is VERY sensitive to

everything: Free surface shape, scratches, dust, fingerprints, …

Too complex to predict in practice!

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A Little Experiment in Light Scattering (1)A Little Experiment in Light Scattering (1)

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Saturated solutionsPhosphate buffer, pH 111 week incubation

Left to Right:

ChlorpromazineHCl Bendroflumethiazide Clofazimine Bifonazole ThioridazineHCl TriflupromazineHCl Nifedipine Perphenazine PromazineHCl

Can You Tell The Saturated Solution?Can You Tell The Saturated Solution?

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So Let’s Filter the StuffSo Let’s Filter the Stuff

Dynamic Light Scattering:

Bendroflumethiazide, saturated, filtered

Filter,m

Particle size,nm

Polydispersity

0.2 481 – 4,000 0.226

0.45 270 0.096

1 543 - 2,000 0.327

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Particle Effects in Optical Assays (1)Particle Effects in Optical Assays (1)

Turbidity:– Relies on particles to produce scattering– No filtering is desired– Particle evolution is unpredictable

Absorbance:– Scattering is an undesired artifact

– Filtering is desired– Scattering contribution to data is unpredictable

0 scattered transmittedI I I

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Particle Effects in Optical Assays (2)Particle Effects in Optical Assays (2)

I0It

No particles:

No scattering

Beer’s law works

Is

Particles in solution:

Scattering

Beer’s law does not work

~A C Determine from dissolved samples (undersaturated)

Use in saturated solutions

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Caffeine concentration, mg/ml

0 20 40 60 80 100

Ana

lytic

al S

igna

l

0

10

20

30

40

50

60

70

The Ideal ExperimentThe Ideal Experiment

All of these data have particle effects!

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Saturation is a Difficult Place To AssaySaturation is a Difficult Place To Assay

nm solids artifacts:

- Assay specific- Significant for optical techniques

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Take Home Message #2 Take Home Message #2

Particles will affect solubility data:

Turbidity:Increase, perhaps

significantly

Absorbance:Increase, unpredictably

Elemental:Increase, more predictably

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What Do We Want to Study?

Time

Temperature

DMSO Crystalform

Salt

Co-Solvent/Excipient

pH

Solubility

Time

Temperature

DMSO Crystalform

Salt

Co-Solvent/Excipient

pH

Solubility

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How We Chose to Do the Job:How We Chose to Do the Job:Automated Solubility WorkstationAutomated Solubility Workstation

Philosophy of Design:– Accuracy is everything– Assay flexibility is a must– Medium throughput is acceptable (up to 250/day)– System must be compatible with dry and DMSO-

dissolved– Minimize number of assumptions– Native assay relies on elemental assays: no

standards required– Eliminate the need for experienced chemist by using

intelligent software– Simplify and modernize everything significant

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Solubility: Where Do You Spend Solubility: Where Do You Spend YourYour Time? Time?

Weighing

Preparing standards (or believing nominal library concentrations)

Deciphering data – trapping errors

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Streamlined Solubility AssayStreamlined Solubility Assay

Weighing:– Optional. Useful only for detecting

insufficient source material (undersaturated solution)

Preparing standards (or believing nominal library concentrations)– Eliminated. You only need MW and #of

nitrogens Deciphering data – trapping errors

– Intelligent data analysis, smart error detection, automated reporting

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Two Options on One PlatformTwo Options on One Platform

Native Assay: Equimolar nitrogen detection

Dry or dissolved sample

No calibration required

Optional Assay:

Millipore MultiScreen®

solubility plates

DMSO dissolved samples

UV assay

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The ASolWNative Assay

Millipore MultiScreen® Solubility Protocol

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ADW - Automated Discovery WorkstationADW - Automated Discovery Workstation

ANALIZA’sADWIn-house DevelopmentSystem

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ASolW – Automated Solubility WorkstationASolW – Automated Solubility Workstation

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Total Nitrogen DetectionTotal Nitrogen Detection

Exceptions:– N2 – nothing is detected!

– Nitrosamine (for GC)– Azides (1/3 response)– Double bonds (partial

response)

)900600(2*23

1050

2

nmhONOONO

productsNOONR

Separate

calibration

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Effect of Nitrogen StructureEffect of Nitrogen Structure

Compound # N atoms Adjacent N? mg/ ml Yield % caffeine 4 None 1.35 94.7 HEPES 2 None 6.29 90.9 propranolol HCl 1 None 2.52 96.5 sulfanilamide 2 None 2.07 98.3 tris base 1 None 4.97 100.4 benzocaine 1 None 0.60 100.2 verapamil HCl 2 None 0.47 86.0 nicotinamide 2 None 2.65 99.7 alprenolol HCl 1 None 1.67 102.7 trimethoprim 4 None 0.12 86.7 phenacetin 1 None 0.17 110.1

Average

yield: 96.6%

Compound # N atoms Adjacent N? mg/ ml Yield % antipyrine 2 2 1.98 82.1 allopurinol 4 2 0.08 76.4

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Assay Using Nitrogen ContentAssay Using Nitrogen Content

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Dynamic RangeDynamic Range

ppm N of calibrator

1 10 100 1000 10000

Ana

lytic

al s

igna

l +/-

1 s

d (

n=3

)

0.01

0.1

1

10

100

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AccuracyAccuracy

Sexp = 0.036(±0.018) + 1.005(±0.008)*Slit

N = 12; r2 = 0.9993; s = 0.0596,

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Data DispersionData Dispersion

ppm N of calibrator

1 10 100 1000 10000

rela

tive

sta

ndar

d d

evia

tion,

CV

, %

(n

=3)

0.01

0.1

1

10

100

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Repeated MeasurementsRepeated Measurements

Run #

0 5 10 15 20

Sol

ubili

ty,

mg/

ml

0.0

0.1

0.2

0.3

0.4

0.5

Average = 0.264Upper Limit = 0.283

Lower Limit = 0.245

Allopurinol in 0.15M NaCl in 0.01M Universal buffer, pH 6.6

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Effect of Filter Type on ConcentrationEffect of Filter Type on Concentration

ANALIZA recommends PTFE 0.45

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Solubility-pH ProfilesSolubility-pH Profiles

0.01M universal buffer

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Solubility with PEG600

0

2

4

6

8

10

12

14

16

18

0 20 40 60 80 100 120

% PEG 600

mg

/ml

clofazimine bifonazole nifedipine

Excipients/Co-Solvent Effects (1)Excipients/Co-Solvent Effects (1)

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Excipients/Co-Solvent Effects (2)Excipients/Co-Solvent Effects (2)

Solubility with Tween80

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 2 4 6 8 10 12 14 16 18

% Tween

So

lub

ility

mg

/ml

clofazimine bifonazole nifedipine

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What is the Effect of DMSO?What is the Effect of DMSO?

DMSO is a fact of life during screening

DMSO solubility is a current issue in HTSe.g. K. Balakin, “DMSO Solubility and Bioscreening”, Current Drug Discovery, (August 03)

Key Issue: How did you start?– DMSO-dissolved sample: NO solid

Precipitating solid at saturation crystalline form

Difficult experiment requiring high concentrations

– DMSO added to solid form

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DMSO – Time ExperimentsDMSO – Time Experiments

30 mM DMSO stock solutions, 20-fold dilution Powder with buffer or with 5% DMSO 50 mM phosphate buffer at pH 11

Source % DMSOIncubationTime, hrs.

Powder 0 20

DMSO stock 5 20

DMSO stock 5 1

Powder 0 1

Powder 5 20

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Test Compounds – Solubility (mg/ml)Test Compounds – Solubility (mg/ml)

1 bendroflumethiazide 0.3507

2 nifedipine 0.0013

3 nitrofurazone 0.1615

4 perphenazine 0.0006

5 trimethoprim 0.3440

6 nafoxidineHCl 0.0040

7 nadolol 0.0038

8 imipramineHCl 0.0007

9 verapamilHCl 0.0064

10 clofazimine 0.0006

11 bifonazole 0.0009

12 clomipramineHCl 0.0004

13 labetalolHCl 0.5946

14 chlorpromazineHCl 0.0023

15 triflupromazineHCl 0.0013

16 thioridazineHCl 0.0007

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Group I: Fully DissolvedGroup I: Fully Dissolved

#1 #5 #13So

lub

ility

Re

lativ

e t

o T

ota

l Co

mp

ou

nd

Con

cen

tra

tion

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Powder in Buffer alone, 20 hr incubation30 mM DMSO stock, diluted 20-fold in bufferPowder in buffer with 5% DMSOPlot 1 Upper Control Line

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Group II: Similar ResultsGroup II: Similar Results

#3 #7 #11 #14 #15

So

lub

ility

Rel

ativ

e t

o P

ow

der

in B

uff

er

Alo

ne

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Powder in Buffer alone, 20 hr incubation30 mM DMSO stock, diluted 20-fold in bufferPowder in buffer with 5% DMSOPlot 1 Upper Control Line

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Group III: Significant DMSO Stock Effect Group III: Significant DMSO Stock Effect

#4 #2 #6 #10 #16

Sol

ubili

ty R

elat

ive

to P

owde

r in

Buf

fer

Alo

ne

0

10

20

30

40

Powder in Buffer alone, 20 hr incubation30 mM DMSO stock, diluted 20-fold in bufferPowder in buffer with 5% DMSOPlot 1 Lower Control Line

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Group IV: Significant DMSO Effect Group IV: Significant DMSO Effect

#4 #8 #9 #12

So

lubi

lity

Rel

ativ

e t

o P

owde

r in

Buf

fer

Alo

ne

0

5

10

15

20

25

Powder in Buffer alone, 20 hr incubation30 mM DMSO stock, diluted 20-fold in bufferPowder in buffer with 5% DMSOPlot 1 Upper Control Line

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Powder Plus DMSO: Reasonable at Low %Powder Plus DMSO: Reasonable at Low %

Allopurinol

Bendroflumethiazide

Butamben

Clofazimine

Nitrofurazone

Theophylline

Nifedipine

Perphenazine

Phenacetin

Sulfanilamide

Trimethoprim

pH 7.4

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Powder Plus DMSO: Reasonable at Low %Powder Plus DMSO: Reasonable at Low %

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DMSO Stock Effect is Still Significant at 1%DMSO Stock Effect is Still Significant at 1%

Equilibrium Solubility in PBS/ 1% DMSO

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Kin

etic

Sol

ubili

ty fr

om D

MS

O s

tock

, mg/

ml

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1

73

9 8 2

5

4

6

1 bendroflumethiazide2 benzocaine3 benzthiazide4 butamben5 nifedipine6 nitrofurazone7 perphenazine8 phenacetin9 trimethoprim

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Take Home Message #3Take Home Message #3

DMSO effect is unpredictable

DMSO effect is most pronounced when starting material is DMSO-dissolved

DMSO effect is still significant and unpredictable in practice, even at low DMSO %, when starting from DMSO-dissolved samples

DMSO-dissolved samples are fine for early discovery questions, but can not be relied on for later discovery and development

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How Do We Deal With Impure Samples?How Do We Deal With Impure Samples?

Impurities are a fact of life:– Combi libraries are 90-95% pure– Lead synthesis is similarly pure

Turbidity:– No need, usually, but you don’t know what

came out of solution

Absorbance:– Use spectral techniques – not trivially

Elemental:– Clean sample: inline column + fast gradient

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Final ThoughtsFinal Thoughts

If you need good numbers, you have to watch your steps:– Particle effects in optical methods– Filter effects– DMSO effects when starting from dissolved

samples Elemental method provides for automated,

accurate determinations with full accessibility to later discovery/development questions: pH, temperature, salts, etc…

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Coming Up: Integrating ADMET (1)Coming Up: Integrating ADMET (1)

Hardware is expensive Software is very expensive Gold-standard assays are always that Only big pharma can own “one of each” In silico predictive analysis works much

better on same series, if properly trained Chemists will use your data if it is GOOD,

USEFUL, COMPREHENSIVE, and EASILY AVAILABLE

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Coming Up: Integrating ADMET (2)Coming Up: Integrating ADMET (2)

Automated Discovery Workstation: Multi-assay on the same hardware platform:

– Solubility (dry/DMSO, pH, salts,…)– LogD (pH)– BBB (near term GIT) permeability– Coming: Tox (cardiac), protein binding

Nothing proprietary, no magic, only gold-standard assays

Never have to weigh or prepare standards Never have to believe your library concentration Optional on-line sample cleanup Future integration with predictive software

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Thank YouThank You

Acknowledgements

Padetha Tin

Pam LechnerAlexander BelgovskiyBoris Zaslavsky