A Holistic systems approach to controlling Bioassays ... · PDF fileA Holistic systems approach to controlling Bioassays: Lessons Learned CaSSS-Bioassay 2014 Bhavin Parekh, Ph.D. Group

A Holistic systems approach to controlling Bioassays: Lessons Learned CaSSS-Bioassay 2014 Bhavin Parekh, Ph.D. Group Leader-Bioassay Development Eli Lilly and Company Indianapolis, IN 46221

Outline

Background

Lesson Learned over the last decade

3 Case studies

Summary

Parekh, CaSSS conf., 2009 Copyright © 2009 Eli Lilly and Company

Why do need bioassays?

Biological therapeutics (proteins, vaccines, Ab, etc.) are complex and heterogeneous in composition. They can exist in multiple physical and chemical conformations.

Even though physiochemical analysis (HPLC, MS, AAA, etc.) can be extensive, it cannot measure activity (or impact of changes in activity) of bioproducts.

Bioassays need to be sensitive to structural changes and/or to product degradation that may impact biological activity, efficacy, or safety

By measuring potency using a bioassay, we can infer the structural integrity of a complex biological, thus bioassays are a measure of the quality of the therapeutic

4/16/2014

Case Study 1

Product X is a protein product

Potency assay is a cell-based assay. Ulitizes UMR-106 (osteosarcoma) cell line which expresses the receptor endogenously. Assay readout is a competitive cAMP ELISA.

Method was validated to support clinical development and post-approval


16-Apr-14 File name/location

Method Description: Cell Induction

Intracellular cAMP released

Product X

binding

UMR-106 cell covered

with Product X receptors

= Product X

= intracellular cAMP produced in

response to Product X

Cell Lysis

cAMP detection


Method Description: cAMP Detection

Assay Plate coated with

goat anti-rabbit antibody

Rabbit anti-cAMP antibody

binds cAMP and cAMP-AP

AP AP

AP

= cAMP

AP AP AP

Cellular cAMP and Alkaline

Phosphatase-conjugated cAMP

added to Assay Plate

= Rabbit anti-cAMP = Goat anti-rabbit

AP AP AP

CSPD/Subs

= Light

Wash

Figure 1. rhPTH(1-34) Dose Response Curve

Product X (nM)

0.001 0.01 0.1 1 10

RLU

's

0

5

10

15

20

25

rhPTH(1-34) Reference Standard

rhPTH(1-34) Unknown Sample


Increase in assay failures Valid Run Ratio: 8.3 Slope: 1.6 L-term 0.0506

Invalid Run Ratio: 3.2 Slope: 1.4 L-term: 0.3257

0

25000

50000

75000

100000

Y

.01 .006 .1 .07 .04 .02 1 .7 .5 .3 .2 2 3 4 6

Concentration

0

5000

10000

15000

20000

25000

Y

.01 .006 .1 .07 .04 .02 1 .7 .5 .3 .2 2 3 4 5 7

Concentration

Y Median Response S

Median Response UPASS FAIL Failed assay acceptance criteria L-term. L-term is the log width of the 95% confidence interval for

individual reported potencies


Historical Performance: L-term Analysis


Assay performance deteriorated

Decline in assay run critical parameters 5-fold decrease in assay response (100K to 20K)

2-fold reduction in S/N (asymptote ratio) from 6.0 to 3.0

Poor quality dose-response curves

High L-term values

Assay valid rate dropped from >90% to <25%

Created significant assay backlog and meet market supply chain demands


Root Cause Investigation

Focused on both the analytical method and the cAMP detection (Vendor) kit

Comprehensive technical assessment 8 months in duration

Cross-functional effort (QCL, Bioassay Development, Vendor, TPO Lab)

Every aspect of the assay was assessed

Cells losing response?

Cell health/viability

Pipetting technique, sample preparation, wash steps, blocking, substrate incubation, etc, etc

Root Cause was finally Determined!!! Conjugate dilution buffer identified as primary cause


Conjugate Buffer Diluent Improvement

0

50000

100000

150000

200000

Y

.01 .006 .1 .07 .04 .02 1 .8 .6 .4 .3 .2 2 3 4 5 7

Concentration

L-term: 0.1035 Slope: 1.7 Asymptote Ratio: 6.1 Potency: 69.80%

.

Vendor conjugate dilution buffer

Lilly conjugate dilution buffer (Mg2+ and Zn2+)

0

5000

10000

15000

20000

25000

Y

.01 .006 .1 .07 .04 .02 1 .7 .5 .3 .2 2 3 4 5 7

Concentration

Invalid Run Ratio: 3.2 Slope: 1.4 L-term: 0.3257


Lessons Learned: Short and long term

improvements

Short-term

Implement new conjugate dilution buffer

Bioassay Development group maintain on-going communication with vendor kit vendor

Long-term

Proactively monitor assay performance and react to trends

Develop new assay based on a different technology


Lesson Learns from Case Study 1

Reliance on Vendor-supplied kit Single sourced

Complex biological reagents

Non-GMP supplier

Look at the business model. Are kits designed for discovery/screening efforts

Does “QC tested” meet your standards

Reliability concerns

Lot to lot differences

Timely delivery of kits

Assay complexity Multiple liquid transfer and pipetting steps

Labor intensive

Prone to errors

Sensitive to analyst technique


Long term strategy: Outcomes

Developed reporter gene assay in same parental cell line

Demonstrated comparability between cell-based ELISA vs. reporter gene assay

Validated and transferred method to testing lab

Reporter gene assay Significant improvement in assay run parameters

4-fold increase in response level

Enhanced S/N ratio

L-term values consistently below limit (NMT 0.2000)

Acceptable dose-response curves

Assay valid run rate >90%

Completed transfer of assay to TPO

ATP

cAMP G-Protein

X-Receptor Adenylate Cyclase

PKA

Activated PKA

CREB Phospho-CREB

(activated)

CRE-Sequence

(promoter)

CRE-Luciferase Construct

Luciferase mRNA

Luciferase (Protein)

Product X

Case Study 2: Drug Y Bioassay

Reporter gene assay with promoter of validated gene linked to luciferase

Gene is stimulated by a constant concentration of agonist.

Drug Y inhibits the stimulation by the gene and this can be measured in a dose-dependent manner.

Method qualified in QC lab to support early phase CT lot release and stability Accuracy <20%, Precision <10%. Linearity (R2) = >0.95

Stimulating

molecule

Drug Y

Luciferase

Testing of DS and DP lots revealed a bias in potency values

%P

ote

ncy

100

120

140

160

Bioassay Results

Avg Potency= 116%± 13%

High Bias and variability not observed with sys. Suit sample that is also testing no each plate

High Bias and variability observed with DS and DP testing (Lot and stability)

Avg Potency= 102%± 8%

DS/DP lot testing Sys. Suit. testing

Bioassay Curve Characteristics for Product Y

0

100000

200000

300000

400000

500000

600000

0.001 0.01 0.1 1 10 100 1000 10000 100000

Rela

tiv

e L

igh

t U

nit

s

Conc.

Ref Std

Drug X

5% RLU change = 14%

Potency difference

Potency = 152%

Shallow slope (<1)

Summary of Assay Improvements and learnings

Difficult to change the dynamics of the biological response , i.e. slope since it may be an inherent biological property

Instead a series of improvements to provide robustness, and reduce variability and bias.

Improvements

• Implement gravimetric preparation of samples to reduce pipetting error associated

with viscosity

• Add more robustness to the curve by adding dilution points in the linear portion

• Implement twice the cell number per well to increase overall signal strength

• Remove Phenol Red from medium to reduce quenching of luminescent signals

Reduced Bias and Variability

Avg = 116%±13% Avg = 105%±8%

Company Confidential Copyright © 2013 Eli Lilly and Company

Kamikura 2013

Case 3: Reporter gene assay- Stability of recombinant cell lines

Instability likely mediated by epigenetic gene silencing

Method qualified for early phase release and stability testing Cell passage stability assessed (>20 passages) assess during early method development

However, several years later….


Kamikura 2013

Developing stable reporter genes assays

•vector design (eg. bicistronic vectors to link selection to promoter activity, lentivirus) – increasing

transgene stability

• appropriate gene promoter (preferred to transcription factor binding sites) – reduced variability

• Requires baseline expression for selection –open chromatin and maintenance of response

Luciferase Neo/Kan

1.85kb reporter of choice

Ori

promoter of choice

Luciferase Neo/Kan

Vector

1 mRNA

Neo/Kan Luciferase 2 proteins


Kamikura 2013

Example: Bi-cistronic Vector Stability

x axis

1 10 100

0

50000

100000

150000

System Suitability graph

4-P Fit: y = (A - D)/( 1 + (x/C)^B ) + D: A B C D Rel. Pot.

Plot#1 (Reference Standard: Concentration vs Medi... 1.72e+05 6.29 14.2 2.22e+04 1

Plot#2 (System Suitability: Concentration vs Median... 1.72e+05 6.29 13.8 2.22e+04 1.03__________

Weighting: Fixed

PLA (Std. Curve: Plot#1) Degrees of Freedom: parallel = 11 free = 8 non-parallel = 3

R^2 = 0.994 F-stat = 0.296 F-prob = 0.827

Low Passage (p4)

x axis

1 10 100

10000

20000

30000

40000

50000

60000

70000

80000

System Suitability graph

4-P Fit: y = (A - D)/( 1 + (x/C)^B ) + D: A B C D Rel. Pot.

Plot#1 (Reference Standard: Concentration vs Medi... 8.46e+04 6.13 13.4 1.17e+04 1

Plot#2 (System Suitability: Concentration vs Median... 8.46e+04 6.13 13.3 1.17e+04 1__________

Weighting: Fixed

PLA (Std. Curve: Plot#1) Degrees of Freedom: parallel = 11 free = 8 non-parallel = 3

R^2 = 0.993 F-stat = 0.495 F-prob = 0.696

High Passage (p48)

Passaged 1-2x week for ~11 months Acceptable even in the absence of selection = stable genome integration

* no selection

Future technology exploration….


Several genomic editing technologies allow generation of reporter gene at gene-specific sites

TALENS Zn Fingers Crispr-Cas9

Learnings for Case Studies 2 and 3


Reporter gene assays Good Platform approach for many targets

Helps build expertise Leverage cross project learnings Minimal manipulation by analysts Shared instrumentation: likely to catch problems

However, take a long term view to assess control of bioassay method

Qualifications and validations are ‘snap-shots’ in time Assess method performance and capability based on long-term API/DP testing

experience. In most cases, long-term passage related stability can be controlled based on

Understanding stability of initial clones Two tiered cell banking strategy (master and working cell banks)

Tracking and trending of parameters such as curve properties, passage numbers, analysts, split schedules, FBS lots, critical reagents, etc are an important tool to assess control


Acknowledgement Bioassay development group

Darren Kamikura

Sharon Sibley

Jeanne Helmreich

Piyush Vyas

Denise Lyons

Liming Shi

Jane Sterner

Global Quality Labs

Robert Beckmann

Liying Lin

Katie Singer

Linda Wolters

Documents

A Holistic systems approach to controlling Bioassays ... · PDF fileA Holistic systems approach to controlling Bioassays: Lessons Learned CaSSS-Bioassay 2014 Bhavin Parekh, Ph.D. Group