for a safer world Science

Comparability: Manufacturing,

Characterisation and Controls Workshop –

Approaches to robust characterisation data from

heterogeneous populations

J Braybrook

Trinity Hall, Cambridge 14/15 Sept 2015

The characterisation conundrum

Chemically-synthesised drug entity

• Defined process-structure-function

• Complete knowledge and measurement of all relevant inputs and outputs of process

• One defined active ingredient linked unambiguously via its identity to its safety and efficacy profile

Biologic entity

• Correlated process-structure-function

• Partial knowledge and measurement of all relevant inputs and outputs of process

• Heterogeneous, partially defined active ingredient correlated to its safety and efficacy profile – contingent on process consistency

Cell-based product

• Structure determination of small parts of a cell (cellular active component) – ‘windows’ of characterisation

• Cells are a heterogeneous population – ‘patterns with uncertainty’

Comparability = CQAs remain within allowable range of variation ( « highly similar » product)

Quality evaluation

should include:

•Batch data

•In-process controls

•Characterisation

•Process re-validation

•Stability testing

CRITICAL QUALITY ATTRIBUTES

CRITICAL PROCESS PARAMETERS

Analytical sensitivity

Extended characterisation

Stability testing

Non-clinical &

clinical evaluation

Allowable variation

Raw materials

Formulation & Process

development

Process &

equipment

validation

How much analysis and when?

• Starting Materials

• In-Process

• Product

• Stability

• Comparability

– Product and Sample

retention

Characterisation

(‘well specified’)

All possible tests

All applicable tests

kn

ow

led

ge

Product

Release

Comparability

Safety

Quality

Efficacy

Private communication from C.Bravery

Likely important analytical methods for

comparability

• Those that support well-specified product characterisation

• Those that measure cell functions

– related to (assumed) MoA

– one or more of which will be considered ‘potency’ assays

• Those that measure (re-confirm) stability – extended characterisation

BUT ALL analytical methods have limitations and uncertainty

UNLESS robust analytical methods/tools are available that can look at critical quality parameters, it will not be possible to show comparability with quality data alone

SO.....NEED to understand measurement method uncertainty to minimise ALL controllable sources of variability

Ishikawa (Cause-and-effect; Fishbone or

Herringbone) diagram: RNA quantification

9

Case study 1 – single cell analysis

• Individual brain cells may express as few as 65% of the same genes

as their neighbours

• In the immune system, cells placed in the same category on the

basis of surface markers can express significantly different sets of

genes and have different responses to vaccines

• As tumour cells evolve, their genomes change in unusual ways

creating extensive heterogeneity

The biological properties and responses of cells are not uniform

Cells are individual - measuring the average response of a cell

population (the traditional approach) gives an incomplete picture

The individual cell makes fundamental decisions, such as whether to

migrate or differentiate into a new cell type etc

Single cell gene expression analysis

Initial sample >1x106 cells Lyse cells to

release RNA Extract RNA PCR – gene expression

Simplified overview of standard gene expression analysis

This process is not readily transferable for single cell analysis

What are the analytical challenges?

• How to handle single cells (µm in diameter)?

• How to extract and handle material from a single cell?

– A single cell typically contains <1pl of cytoplasm and 6pg of RNA

• How to amplify the material for analysis?

– A standard PCR for a single gene uses 10ng of RNA (equivalent to

approximately 1600 cells)

• How to perform analysis to allow multiple genes to be

measured from a single cell?

Cell selection

Single step cell isolation and lysis

Standard PCR reactions are compatible with

single cell analysis following pre-amplification

BUT

New micro-fluidic digital technologies allow nl

scale absolute quantitation detection

1 cell 10 cells 100 cells

Single step PCR

Challenges of dPCR

• How does sample preparation affect

the measurements?

• Does the amplification step bias the

measurements?

• Is the technology comparable to

standard PCR?

• Is chip to chip variability an issue?

Factors investigated

• Importance of validating whole process efficiency not only qPCR:

Reverse transcription Pre-amplification qPCR

• Comparison of workflow with/without digestion of gDNA (DNase)

• Discrimination of technical vs. biological (single cell to single cell)

variation

• Validation of limit of detection

• Comparison of single cell data analysis / normalisation methods to

control for sources of between-experiment random variation

AS Devonshire, M-O Baradez, G Morley, D Marshall and CA Foy. Validation of high-throughput single cell analysis

methodology. Anal Biochem 452 (2014) 103-113

Lysis buffer (15 L)

qPCR

RT-Pre-amp (52 L)

Diluted RT-Pre-amp

(260 L)

Dilute 1:5

LCM

DNase (21 L)

Proportion single cell

analysed: 0.9%

2.25 L

qPCR

RT-Pre-amp (15 L)

Diluted RT-Pre-amp (75

L)

Dilute 1:5

LCM

2.25 L

Workflow A

(Separate lysis step)

Workflow B

(Direct RT-PA)

Proportion single cell

analysed: 3.0%

Workflow comparison

0

1

2

3

4

5

6

7

0.0 0.4 0.8 1.2 1.6 Sin

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ea

su

rem

en

t va

ria

tio

n (S

D C

q)

Technical variation (SD Cq)

A B

Total variation

= biological +

technical variation

Workflow

Discrimination in variation

• Workflow A associated with greater technical variation than Workflow B

Comparison of single cell data analysis/

normalisation methods lo

g2(E

x)

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-6

-4

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4

6

8*** ***

log

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*** **

Gene expressed differentially between

2 cell lines

Gene expressed at similar level in

2 cell lines

Less variation associated with

reference gene and global

normalisation

Magnitude of fold difference in

expression between cell lines

varies with normalisation method

Analysis of hundreds of cells for multiple genes leads to:

• large amounts of data, far more than conventional PCR

• heterogeneity and issues with missing data

And

Particularly for data interpretation, biological processes are often a

long chain of events

Challenges of data analysis

Because of the large dataset the gene

expression data is difficult to interpret

Apply mathematical

modelling approaches

such as principal

component analysis

(PCA)

Data interpretation

Missing data

• Is this due to limit of detection or

genuine low/absent expression?

Missing data severely decreases the

number of cells which can be

included in side-by-side comparison

17 genes, 0 cells

11 genes, 17 cells

8 genes, 85 cells

Analysing a large number of

genes and cells, missing data

becomes an issue:

From a sample of 200 single cells

Bootstrapping missing data

Cell line 1 Cell line 2

Chip 1 Chip 3 Chip 5 Chip 2 Chip 4 Chip 6

no

rmalised

TB

P C

q

single cells

PCA with normalised data

Cell line 1

Cell line 2

PCA

Cell line 2 Cell line 1

11 genes

8 genes

Cell line 1

Cell line 1 Cell line 2

Cell line 2

PCA with bootstrapped data

Using the bootstapping approach the data analysis can be increased

• Analysis for all 17 genes

Cell line 1

Cell line 2

Replacing missing data by bootstrapped numbers:

Allows more cells to be analysed simultaneously

Does not affect overall discriminatory power of the analysis

Summary for case study 1

• Variability in gene expression measurements arises at all stages of

single cell analysis

• Cell isolation procedures can affect measurements more than

stochastic (biological) variability

• RT-qPCR kits introduce biases in RNA amplification

• Optimisation of procedures allow successful transfer of RT-qPCR

assays to high-throughput dPCR platform

• Missing data is a strongly limiting factor for analysis but use of data-

fitting and modelling techniques allow differences in expression

patterns to be distinguished

Gene expression at single cell level

Whole population analysis Single cell analysis

Cell line 1 Cell line 2 Cell line 1 Cell line 2

gene A gene B gene A gene B gene A gene B gene A gene B

NS

NS

S

S

Case study 2 - immunophenotyping

• Early detection of deviation from phenotype improves safety

and cost

• Immunophenotyping by FACS therefore offers great potential, if

suitable specific biomarkers can be identified

BUT

• A framework for a generic FACS analysis to fingerprint cell

preparations and quality issues offers more potential

HOWEVER

• Need to overcome issues of subjectivity – gating, fluorescent

intensity measurement etc

28

Sources of measurement uncertainty

Importance of normalisation and data

analysis procedures

Baradez MO, Lekishvili T and Marshall D, Phenotypic fingerprinting of cell products by robust normalized

measurement of ubiquitous surface markers, Cytometry A, 06/2015; DOI:10.1002/cyto.a.22637, 2015

Quality and phenotypic stability assessment

of cell therapies by FACS

T Lekishvili, MO Baradez and D. Marshall. Quality and phenotypic stability assessment of cell therapies during

manufacture by generic FACS immunophenotyping. ESCCA meeting 2013, Luxembourg

Comparability of scalable MSC using

automated and manual processing

Archibald P, Chandra A, Thomas D, Morley G, Lekishvili T, Devonshire A, Williams DJ. Comparability of Scalable,

Automated hMSC culture using manual and automated process steps. Biochemical Engineering Journal, Article in

Biochemical Engineering Journal · July 2015

Case study 3 – bioreactor measurements

2

2.5

3

3.5

4

4.5

Glu

cose

(g/

L)

Media glucose

0

5000

10000

15000

20000

25000

30000

35000

Ce

lls p

er

50

0 b

ead

s

Cell number

Cells in a bioreactor can be measured using simple assays, such as:

Cell growth – Standard proliferation assays

Media component depletion – BioProfiler

BUT

These measurements do not give a compete picture of cell behaviour

Confocal imaging

Quantitative analysis

The confluency of the

cells on the beads

correlates with cell

number

Can be used to track

the growth of cells

during manufacture

M-O. Baradez and D. Marshall, The use of multidimensional image-based analysis to accurately monitor cell

growth in 3D bioreactor culture, PLoS One, 6(10), 26104, 2011

Quality assessment of pancreatic islets

• Current assay: dithizone staining

– Live insulin producing cells

– 2D qualitative assessment

• Proposed assay: live/dead/total

cell staining

– 3D quantitative assessment

Automated viability profiling

2D histogram computed

from 3D data

3D imaging Islet viability profile

dead live dying C

ou

nt

(pix

el)

green

red

• Live/dead staining (calcein-AM + ethidium homodimer-1)

• Laser scanning confocal microscopy

• Automated 3D Z-stack analysis

Z-stack

Quantifying shape of human islets

Eccentricity

(elongation)

Solidity

(roughness)

Size (area)

Major axis length

Minor axis length

Summary of case study 3

• Automated quantitative classification (clustering ) of islets could offer

better implantation tissue selection

Case study 4 – cell media analysis

Instrument: Agilent QToF 6530

Column: ACE C4 150x2.2mm 3µm particle size

LC: Agilent 1200RR

40

GFs1

GFs2

GFs0

De-convoluted average mass

(to be compared with theoretical average mass)

• Extracted ion chromatogram of the most abundant ions - indicative of the

relative amount of GF/aliquot

41

Analysis of growth factor

GFs1

GFs2

GFs0

Summary of case study 4

• Clear mass spectral differences between the different sources of GF

• All the different masses observed are different from the theoretical

masses

– This can be due to:

- Adducts in the different preparation

- Post translational modifications (note: all GF prepared from E.Coli)

- Different sequence

• Different relative amounts of GF found between the different sources

– Note: impurity content may affect ionisation and cause ion suppression

• Work on-going

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