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March 24 - 25, 2014
Sheraton Silver Spring Hotel
Silver Spring, Maryland USA
Organized by:
Welcome to Bioassays 2014: Scientific Approaches & Regulatory Strategies
On behalf of the Scientific Organizing Committee and CASSS, we are excited to welcome you to
Bioassays 2014: Scientific Approaches & Regulatory Strategies and look forward to your participation
and input March 24 - 25, 2014 at the Sheraton Hotel in Silver Spring, Maryland.
"Bioassays" has established itself as a premier conference and unique opportunity for participants and
opinion leaders to discuss and debate current regulatory and industry topics regarding bioassays.
Bioassays are a critical component of the analytical control strategies for biologics and other complex
molecules. The ability of an assay to characterize and demonstrate biological activity is essential and
developing such bioassays is becoming more difficult as biologic drugs are engineered to be more
complex and/or have multiple modes of action. Companies are continuously challenged with developing
assays that are biologically relevant for the analysis of these different mechanisms. Bioassays are also
used for lot release, stability, comparability and characterization studies, which requires that the assays
be robust and, in most cases, suitable for a QC lab.
Bioassays 2014 is structured to encourage attendee interaction. Each session includes case study
presentations followed by a panel discussion allowing for lively dialogue between attendees from
academia, industry and regulatory agencies. As in previous years, we expect this format to result in
additional focus on the technical and regulatory details of the topic. Regulatory participation from the
US FDA, Health Canada and various European agencies has grown each year. In addition, an exhibitor
showcase and poster reception at the end of Day One will give attendees the opportunity to present
additional topics and continue the day's discussion in an informal setting.
We are sure you will find Bioassays 2014 to be informative and productive, and that it will provide you
with current perspectives on bioassays.
Program Co-Chairs: Chana Fuchs, CDER, FDA, USA
Denise Gavin, CBER, FDA, USA
Helena Madden, Biogen Idec, USA
Bruce Meiklejohn, Eli Lilly and Company, USA
Scientific Organizing Committee: Evangelos Bakopanos, Health Canada, Canada
Katrin Buss, BfArM, Federal Institute for Drugs and Medical Devices, Germany
Hélène Gazzano-Santoro, Genentech, a Member of the Roche Group, USA
Xu-Rong Jiang, MedImmune, USA
Thomas Anders Millward, Novartis Pharma AG, Switzerland
Noel Rieder, Amgen Inc., USA
Sally Seaver, Seaver Associates LLC, USA
The Scientific Organizing Committee gratefully acknowledges the program partners for
their generous support of Bioassays 2014.
SUSTAINING PLATINUM PROGRAM PARTNER
Biogen Idec
PROGRAM PARTNERS
Eli Lilly and Company
Genentech, a Member of the Roche
Group
MedImmune
EXHIBITOR PROGRAM PARTNERS
Eurofins Lancaster Laboratories
Promega Corporation
Stegmann Systems
MEDIA PROGRAM PARTNERS
BioPharm International
BioProcess International
BioProcessing Journal
BioTech International
Genetic Engineering & Biotechnology
News
IPQ Publications
LCGC North America
Technology Networks Limited
The Analytical Scientist
Wiley/Journal of Separation Science
Bioassays 2014: Scientific Approaches & Regulatory Strategies
Scientific Program Summary
Monday, March 24, 2014
07:30 – 17:00 Registration in the Cypress Foyer
07:30 – 08:30 Continental Breakfast in the Magnolia Ballroom
08:30 – 08:45 CASSS Welcome and Introductory Comments in the Cypress Ballroom
Bruce Meiklejohn, Eli Lilly and Company, Indianapolis, IN USA
Bioassays 2014 Welcome and Introductory Comments in the Cypress
Ballroom
Bruce Meiklejohn, Eli Lilly and Company, Indianapolis, IN USA
Bioassays Lessons Learned: Part One
Workshop Session One in the Cypress Ballroom
Session Chairs: Bruce Meiklejohn, Eli Lilly and Company and Noel Rieder, Amgen Inc.
08:45 – 08:50 Introduction
08:50 – 09:15 Issues to Consider When Developing Potency Assays for Biologic Products
Baolin Zhang, CDER, FDA, Bethesda, MD USA
09:15 – 09:40 Managing Acceptance Criteria Throughout the Development Lifecycle
Shea Watrin, Amgen Inc., Wellsville, UT USA
09:40 – 10:05 A Holistic Systems Approach to Controlling Bioassay: Lessons Learned
Bhavin Parekh, Eli Lilly and Company, Indianapolis, IN USA
10:05 – 10:30 Lessons Learned: Choice of Potency Assay and Differential Sensitivity to
Degradation Pathways Kirby Steger, Bristol-Myers Squibb Company, Princeton, NJ USA
10:30 – 11:00 AM Break – Visit the Exhibits and Posters in the Magnolia Ballroom
11:00 – 12:15 PANEL DISCUSSION – Questions and Answers
Katrin Buss, BfArM, Germany
Denise Gavin, CBER, FDA, USA
Bhavin Parekh, Eli Lilly and Company, USA
Kirby Steger, Bristol-Myers Squibb Company, USA
Shea Watrin, Amgen Inc., USA
Baolin Zhang, CDER, FDA, USA
Monday, March 24 continued…
12:15 – 13:30 Hosted Lunch in the Magnolia Ballroom
Bioassays Lessons Learned: Part Two Workshop Session Two in the Cypress Ballroom
Session Chairs: Hélène Gazzano-Santoro, Genentech, a Member of the Roche Group and Thomas
Anders Millward, Novartis Pharma AG
13:30 – 13:35 Introduction
13:35 – 14:00 Two-in-One: A Novel Approach of Bioassay Selection for Dual Specificity
Antibodies
Guoying Jiang, Genentech, a Member of the Roche Group, South San Francisco,
CA USA
14:00 – 14:25 Challenges and Strategies in Selecting MOAs-reflective Bioassays for
Bispecific Antibody Xianzhi Zhou, MedImmune, Gaithersburg, MD USA
14:25 - 14:50 Challenges in the Development of Potency Assays for ADCs and their Utility
to Detect Conjugate Variants Sonia Connaughton, ImmunoGen, Inc., Waltham, MA USA
14:50 - 15:15 Development of an Alternative, in-vitro Potency Assay for Rabies Virus
Vaccines Robin Levis, CBER, FDA, Rockville, MD USA
15:15 - 15:45 PM Break – Visit the Exhibits and Posters in the Magnolia Ballroom
15:45 - 17:00 PANEL DISCUSSION – Questions and Answers
Evangelos Bakopanos, Health Canada, Canada
Sonia Connaughton, ImmunoGen, Inc., USA
Chana Fuchs, CDER, FDA, USA
Guoying Jiang, Genentech, a Member of the Roche Group, USA
Robin Levis, CBER, FDA, USA
Xianzhi Zhou, MedImmune, USA
Exhibitor Partner Showcase in the Cypress Ballroom
Session Chairs: Xu-Rong Jiang, MedImmune and Sally Seaver, Seaver Associates LLC
17:15 – 17:30 Introduction
17:30 – 17:45 Effective cGMP Bioassay Outsourcing
Weihong Wang, Eurofins Lancaster Laboratories, Lancaster, PA USA
Alexander Knorre, BSL BIOSERVICE Scientific Laboratories GmbH,
Planegg/Munich, Germany
Monday, March 24 continued…
17:45 – 18:00 New Capabilities of PLA
Ralf Stegmann, Stegmann Systems, Rodgau, Hesse, Germany
18:00 – 18:15 Bioluminescent Technologies for Biological Functional Analysis and Protein-
Protein Interactions
Mei Cong, Promega Corporation, Madison, WI USA
18:15 – 19:45 Exhibitor and Poster Reception in the Magnolia Ballroom
19:45 Adjourn Day One
Tuesday, March 25, 2014
07:30 – 17:00 Registration in the Cypress Foyer
07:45 – 08:45 Continental Breakfast in the Magnolia Ballroom
Bioassays to Support Biopharmaceutical Development
Workshop Session Three in the Cypress Ballroom
Session Chairs: Katrin Buss, BfArM, Federal Institute for Drugs and Medical Devices and Helena
Madden, Biogen Idec
08:45 – 08:50 Introduction
08:50 – 09:15 Implementation of the Next Generation Effector Function Assays for
Comparability Assessments Poonam Aggarwal, Pfizer, Inc., Chesterfield, MO USA
09:15 – 09:40 The Dual Benefit of Structure Function Studies: Better Understanding of
Molecules and Help with MOA-relevant Bioassays
Carl Co, Biogen Idec, Cambridge, MA USA
09:40 – 10:05 Standards and Beyond: Challenges of Application of Old Methods to Next
Generation Products
Elena Semenova, Protein Sciences Corporation, Meriden, CT USA
10:05 – 10:30 Global Implementation of Bioassays – Things to Consider
Bruce Meiklejohn, Eli Lilly and Company, Indianapolis, IN USA
10:30 - 11:00 AM Break – Visit the Exhibits and Posters in the Magnolia Ballroom
11:00 – 12:15 PANEL DISCUSSION – Questions and Answers
Poonam Aggarwaal, Pfizer, Inc., USA
Carl Co, Biogen Idec, USA
Chana Fuchs, CDER, FDA, USA
Bruce Meiklejohn, Eli Lilly and Company, USA
Elena Semenova, Protein Sciences Corporation, USA
12:15 – 13:30 Hosted Lunch in the Magnolia Ballroom
Tuesday, March 25 continued…
Bioassay Controls & Control Strategies
Workshop Session Four in the Cypress Ballroom
Session Chairs: Evangelos Bakopanos, Health Canada and Sally Seaver, Seaver Associates LLC
13:30 – 13:35 Introduction
13:35 – 14:00 Assay Acceptance Criteria for Multiwell-Plate-Based Biological Potency
Assays
C. Jane Robinson, National Institute for Biological Standards and Control
(NIBSC), Hertfordshire, United Kingdom
14:00 – 14:25 “Edging Out” Edge Effects in a Cell-based Assay
Shelley Elvington, Genentech, a Member of the Roche Group, South San
Francisco, CA USA
14:25 – 14:50 Near-universal Similarity Bounds for Bioassays
David Lansky, Precision Bioassay, Inc., Burlington, VT USA
14:50 – 15:15 Health Canada Experiences with Bioassay Controls & Control Strategies
Omar Tounekti, BGTD, Health Canada, Ottawa, ON Canada
15:15 – 15:45 PM Break – Visit the Exhibits and Posters in the Magnolia Ballroom
15:45 – 17:00 PANEL DISCUSSION – Questions and Answers
Shelley Elvington, Genentech, a Member of the Roche Group, USA
David Lansky, Precision Bioassays, Inc., USA
Tsai-Lien Lin, CBER, FDA, USA
Thomas Anders Millward, Novartis Pharma AG, Switzerland
C. Jane Robinson, NIBSC, United Kingdom
Omar Tounekti, BGTD, Health Canada, Canada
17:00 – 17:15 Bioassays Workshop Recap
Closing Remarks and Invitation to Bioassays 2015 Helena Madden, Biogen Idec, Cambridge, MA USA
17:15 Adjournment
Bioassays Lessons Learned: Part One Session Abstract
Session Chairs:
Bruce Meiklejohn, Eli Lilly and Company and Noel Rieder, Amgen Inc.
Bioassays represent an essential part of the control strategy for assessing safety and potency of
biopharmaceuticals. There are many potential challenges in bioassay development, implementation, and
maintenance. Avoiding or dealing with these is often an integral part of bioassay development as well as
implementation into routine product testing. This session, which will be divided into two parts, will
present a series of case studies on challenges and successes experienced with bioassays. Each talk will
discuss the specifics of the case study and the key learning’s.
NOTES:
Presenter’s Abstracts
Issues to Consider When Developing Potency Assays for Biologic Products
Baolin Zhang
CDER, FDA, Bethesda, MD USA
A suitable measure of potency is critical to ensure the quality of biologics and other complex products.
To achieve this goal, sponsors are required to develop potency assays that can be used for product
characterization, lot release, in-process and stability testing. Because potency is a product-specific
measurement, potency assays are evaluated on a case-by-case basis. The assay adequacy is assessed by
taking account of multiple factors including, but not limited to, product type, history, mechanism(s) of
action (MoA), associated risk, phases of development, and quality data from physicochemical and
biochemical testing. This presentation provides an overview of regulatory expectations
regarding potency assays and discusses several case studies that highlight some of the relevant issues
commonly seen in the regulatory submissions. Emphasis will be placed on MoA and comparability
studies which represent the most challenging aspects of potency assay development.
NOTES:
3/13/2014
1
Baolin Zhang, Ph.D. Senior Investigator/Product Quality Reviewer
Division of Therapeutic Proteins
Office of Biotechnology Products
Center for Drug Evaluation and Research
CASSS Bioassays 2014: Scientific Approaches & Regulatory Strategies Washington D.C., March 24-25, 2014
Issues to Consider When Developing Potency Assays for Biologic Products
Disclaimers
The views and opinions expressed in this presentation should not be used in place of regulations, published FDA guidances or discussions with the Agency.
Outline
• Regulatory expectations
• Applications
• Issues to consider • Relevance to the mechanism(s) of action (MoA)
• Acceptance limits
• Comparability when making changes
• Case study
3/13/2014
2
Potency (21 CFR 600.3(s))
“the specific ability or capacity of the product, as indicated by appropriate laboratory tests or by adequately controlled clinical data obtained through the administration of the product in the manner intended, to effect a given result.”
Potency Tests (21 CFR 610.10)
“tests for potency shall consist of either in vitro or in vivo tests, or both, which have been specifically designed for each product so as to indicate its potency in a manner adequate to satisfy the interpretation of potency given by the definition in § 600.3(s) of this chapter.”
ICH Q6B
“for complex molecules, the physicochemical information may be extensive but unable to confirm the higher-order structure, which, however, can be inferred from the biological activity”
Examples of complex molecules: •Biological products (e.g. therapeutic proteins, mAbs)
•Mixture products wherein the proportion of “active” ingredients could not be determined by typical physicochemical/biochemical testing
Why a Potency Test?
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Complexity of Therapeutic Proteins
• Heterogeneity • post-translational modifications (e.g. glycosylation) • aggregates/degraded products • charged variants • misfolded species • oxidized species • host cell residuals (Host cell proteins, DNA) • leachables (heavy metals, resin)
• Large molecular sizes: 6 – 300 kDa • Higher-order structures (1, 2, 3) • Less defined structure-function relationships
(Compared to small molecule drugs) • Complex manufacturing processes
Regulatory Requirements for Potency of Biologics License
• 21 CFR 601.2 & FDC Act
All biological products regulated under section 351 of the PHS Act must meet prescribed requirements of safety, purity and potency for Biologic License Application (BLA) approval.
• 21 CFR 610.1
“No lot of any licensed product shall be released by the manufacturer prior to the completion of tests for conformity with standards applicable to such product,” which include tests for potency, sterility, purity, and identity (21 CFR Part 610, Subpart B).
•For all phases of IND clinical studies, data are required to assure product
•Identity, quality, purity and strength (21 CFR 312.23(a)(7)
•Stability (21 CFR 312.23(a)(7)(ii)
• ICH Q6B Specifications: Test procedures and acceptance criteria for biotechnological/biological products
Regulatory Expectations for Potency Testing - Investigational Protein Products
3/13/2014
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Potency Tests: Applications
• Required for characterization, lot release, in-process and stability testing.
• Demonstrate product activity, quality and consistency throughout product development
• Provide a basis for assessing product comparability before and after manufacturing changes
• Evaluate product stability (expiry dating)
• Control clinical dosing consistency
Relationship Between Potency and Clinical Efficacy
21 CFR 314.126(d)
•Potency is a measure of the bioactivity of a drug product that produces a defined clinical effect.
•Potency tests are used to establish that a consistently manufactured product is administered during all phases of a clinical investigation.
•Clinical efficacy is demonstrated by “substantial evidence” from adequate and well-controlled investigations with a consistently manufactured product. Other determining factors include: – PK/PD profile
– Patient population
– Clinical end-point (e.g. overall survival in cancer treatment)
• Cell-based assays • cellular responses - proliferation, growth arrest, cell death, cytokine release • signal transduction - phosphorylation of signaling components • gene transcription (reporter genes) • ligand-receptor binding
• Animal-based assays • eg Lethal Dose 50 (LD50)
• Biochemical assays • eg enzyme activity
• Multiple assays (array matrix) • For products that have complex and/or not fully characterized mechanism of action
Typical Potency Assays
3/13/2014
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Potency Assays for Products Targeting Cell Death or Cell Growth Pathways
A large number of protein therapeutics function through modulation of cell death or cell growth pathways in the target cells.
• Induce cell death or growth arrest – Cancer – Pain syndromes – Viral infection
• Promote cell growth or cell survival – Wound healing – Organ transplantation – Chronic heart failure – Neurodegenerative diseases
• Readouts: • Cell viability/cell proliferation • Apoptosis • Signal molecules, such as:
- Caspase activation - MAPK phosphorylation - Receptor binding
• Correlation between the readouts and the intended biological activity –Cancer drugs: Cell death vs. growth inhibition
Joslyn Brunelle and Baolin Zhang (2010) Drug Resistance Updates, 13:172-179.
Potency Assays for Products Targeting Cell Death or Cell Growth Pathways (cont’d)
Issues to Consider
• Relevance to MoA • Acceptance limits • Validation • Changes to bioassays
– Comparability
3/13/2014
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Mechanism of Action (MoA)
21 CFR 201.57©(13)(i) • Clinical Pharmacology section of the labeling, which
states the following: • “This section must contain information relating to
the human clinical pharmacology and actions of the drug in humans.”
• This section must include the following subsections: • Mechanism of action… • Pharmacodynamics… • Pharmacokinetics…
• The MoA should be discussed at various levels, including the cellular, receptor (selectivity), target organ, and the whole body level, depending on what is known.
• Only reasonably well-characterized mechanisms should be described (21 CFR 201.56(a)(2)).
• Speculation on the mechanism of drug action must be avoided (21 CFR 201.56(a)(2)).
• “How Therapeutic and Adverse Effects Occur” – Guidance for Industry (2009): Clinical Pharmacology Section of Labeling for Human
Prescription Drug and Biological Products— Content and Format
MoA: What is it?
MoA in Human Body: A Learning Process in the Product Lifecycle
Relevant disease models
Cell-based assays
Enzyme assays
• A bioassay may not capture all the functional attributes of a product:
– e.g. glycosylation, pegylation, ADCC, CDC, etc.
• Bioassays are performed in combination with physicochemical/biochemical tests to support product quality.
e.g. LD50
1) Phenotypic changes
2) Signal transduction
In vitro enzymatic reactions
3/13/2014
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What Should Be Assessed for Potency?
• Relevance to the MoA(s).
– Desired MoA vs. well-characterized MoA
– Assay Matrix - Complex or unknown MoA
• Correlation between the surrogate assay(s) and the biological activity related to potency
- Signal transduction assays
• Ability to discriminate between an active and inactive product or degraded form of the product.
Issues to Consider
• Relevance • Acceptance limits • Validation • Changes to bioassays
• Comparability
Acceptance Criteria for Potency – Biologics License
• A validated potency assay or assay matrix with defined acceptance criteria must be described and justified in the BLA (21 CFR 601.2(a) and 211.165(e))
• “… should reflect the capacity of manufacturing process, and the potency limits established for product lots used in the pivotal clinical studies demonstrating clinical effectiveness” (FDC Act, Section 505(d), 21 U.S.C. 351).
3/13/2014
8
Acceptance Criteria for Potency – Early Phases
• In early development phases, potency acceptance criteria can be difficult to set:
- Limited manufacturing experience - Limited lots of drug substance (DS) and drug product (DP) - Assays not fully validated
• Broad acceptance criteria
– Evaluated with physicochemical and biochemical testing data
• As development proceeds, the acceptance criteria should be tightened to reflect the actual manufacturing capacity, clinical experience and assay performance.
Issues to Consider
• Relevance • Acceptance limits • Validation • Changes to bioassays
• Comparability
Assay Parameters that are Usually Validated
• Robustness to assess sources of variability – reference standards (ICH Q6B) – instruments – reagents (e.g. stable cell lines)
• System suitability • Accuracy • Linearity & Range • Precision (Repeatability, Reproducibility) • Intermediate Precision (analysts, days, laboratories if more
than one will be used)
• Specificity
3/13/2014
9
Stable Cell Lines Used in Potency Assays
• Selection of cell lines – a lineage close to the cell/tissue type targeted by the drug
– a surrogate cell line if an appropriate cell surface receptor is expressed (either endogenously or via stable transfection).
– growth characteristics (e.g. homogeneity, robust growth, functional stability)
• Two-tiered cell banking system (MCB & WCB) – plasmid copy number
– cell passage number
• Standard operating procedures (SOPs) for handling the cell line, including the cell culture conditions, passage number, and procedures for detecting microbial contaminants (e.g. mycoplasma).
Dose Titration Curve
• The dose titration curve should be optimized so that the dilutions are appropriately distributed throughout the entire dose response curve with sufficient coverage in the linear portion of the curve.
• Appropriate statistical analysis (e.g. parallel line analysis) needs to be applied when generating final relative potency results.
• Once a bioassay is validated, it is important to monitor its performance over time. • trending chart for suitable parameters of the ref standard
(RS) response curve and potency of analyzed QC samples
Issues to Consider
• Relevance • Acceptance limits • Validation • Changes to bioassays
• Comparability
3/13/2014
10
Comparability Exercise
• Demonstrate that changes to the potency assay do not interfere with the suitability of the analytical procedures for their intended purposes in terms of
– Accuracy, precision, specificity, detection limit, quantitation limit, linearity, range, robustness
– Pre-defined acceptance criteria
• Same test samples should be assayed using both the original bioassay and the proposed bioassay
– DS, DP, RS, stress and accelerated stability samples
• Appropriate statistical analysis
• In an IND Phase 1 study, a cell-based signal transduction assay (MAPK phosphorylation) was used as a potency assay for a protein product intended to treat Type 2 diabetics.
• Reviewer comment: A potency assay should reflect as much as possible the intended mechanism of action of the drug product. In this case, this would be a measure of improved glucose tolerance or increased glucose uptake in adipocytes. You should provide data demonstrating the correlation between MAP kinase activation and glucose uptake in response to the drug. Alternatively, you may develop an assay that directly measures the uptake of glucose in adipocytes.
Case study # 1
• A recombinant growth factor is tested for treating chronic heart failure. The sponsor developed a potency assay measuring phosphorylation of erbB2 receptor in a cancer cell line.
• Reviewer comment: The potency assay should be optimized to provide a more reliable quantitation of the product’s bioactivity in order to assure consistent dosing. Data should be provided to demonstrate the correlation between phosphorylation of erbB2 and the intended bioactivity of the product i.e. inhibition of cell death of cardiomyocytes.
Case study # 2
3/13/2014
11
• In a Phase 1 IND, a cell viability assay was used as a potency test with a proposed acceptance criteria: IC50 (0.1- 60 nM)
• Reviewer comment: The release specification for potency is not acceptable. The acceptance criterion is too broad to ensure lot-to-lot dosing consistency. From the data provided, it is difficult to assess whether the proposed potency range is wide due to inherent assay variability or whether there are considerable differences in lot-to-lot activity. The potency assay should either be controlled by using a suitable internal reference standard with an allowable range or the manufacturing process must be better controlled.
Case study # 3
• A potency assay was changed from a cell proliferation format to a caspase activation format
– Specification (relative to RS) was not changed
– Better assay precision
– Less plates to meet system suitability
• Validated new assay
– Accuracy (spike recovery), precision (repeatability), intermediate precision, linearity, range and robustness
• Comparability demonstrated by assessing
– Lot release, drug substance, drug product, stability and stressed samples (e.g. heat, light)
Case study # 4
• One applicant developed a cell-based assay to replace animal-based assays for lot release of a licensed protein toxin.
• Issues with the mLD50 assay - highly variable - high rate of assay failure - limited detection range - nonspecific to the product - suffering and death of animals
Case study # 5
3/13/2014
12
mLD50 Bioassay Cell Based Assay Specificity for toxin No Yes, depend on cell lines Sensitivity LD50 1 U ≦ 0.5 U (LD50 equivalent) Range 0.5 – 2.0 U ≦ 0.5 - >100U Precision +/-20-30% <10% Validity 70 – 80 % > 90%
• Comparability demonstrated using a variety of samples (drug substance, drug product, reference standards, stressed samples).
• Improved performance
Case study # 5 (cont’d)
• The potency assays for a licensed recombinant growth factor product have evolved from animal-based assays to cell-based proliferation assay to cell-based gene expression assay.
Case study # 6
Pros Cons
Animal-based assays
• Direct manifestation of MoA • Manifestation of the active
glycosylated forms
• Requires many animals • Highly variable
Culture cell-based assays
• Less laborious • More sensitive • More robust • Fast
• Respond to non-glycosylated product
• Not suitable for measuring drug levels in plasma
Case study #7: Bioassays for complex small molecule drugs
• A bioassay was included as part of the release specifications for a mixture cancer drug, because typical physicochemical/biochemical assays could not determine the proportion of “active” ingredients in the product.
• The approved potency assay uses xenograft tumor mouse models implanted with a murine tumor cell line.
• The assay was used for > 15 years as a release test until recently when tumors failed to grow appropriately upon implantation.
• A cell viability assay using a human cancer cell line is under development:
• Is the selection of such a cell line acceptable?
• Can this new assay be used to replace the animal-based assay for lot release?
3/13/2014
13
Progressive implementation of potency assays
•Limited data on relevant biological attributes
•Broader acceptance criteria
•Release and stability testing
Early phase: pre-clinical
phase 1 phase 2
Later phase: phase 3 pivotal
Biologics License
• A validated potency assay or assay matrix
• Defined acceptance criteria
• Multiple bioassays • Validation • Narrower limits to
ensure lot-to-lot consistency
• Stability testing of validation lots to establish expiry dating prior to licensure
Final thoughts on potency tests
• Potency tests are product-specific, and the adequacy of these assays is evaluated on a case-by-case basis.
• Potency tests may evolve and change significantly in the course of product development and in the product lifecycle.
• It is recommended that sponsors seek timely advice from the FDA on designing, evaluating and validating potency assays.
Acknowledgements
• Serge Beaucage
• Susan Kirshner
• Amy Rosenberg
• Ennan Guan
• Dov Pluznik
Managing Acceptance Criteria Throughout the Development Lifecycle
Shea Watrin
Amgen Inc., Wellsville, UT USA
One of the most significant challenges, when developing a bioassay and shepherding it through to
commercialization, is setting appropriate assay acceptance criteria. Early in development assay
acceptance criteria may be set based on limited experience with the assay, these criteria may be found to
be not ideal as the assay begins to be used by a broader variety of labs, reagent lots, and equipment.
Cases will be presented where criteria need to be adjusted as the assay matures and discuss appropriate
practices.
NOTES:
3/13/2014
1
Managing Acceptance Criteria Throughout the Development Lifecycle
Shea Watrin, Cecilia Chin, Julie TerWee
Amgen Quality
Outline
• Acceptance criteria primer
• Criteria on day 1
• What necessitates change?
• Is it ok to change?
• Case Study
4 Parameter Logistic (4PL) Curve
50
1
left asymptote
slope
right asymptote
b
a dy d
dose
c
a
b
c ED
d
4-Parameter
Logistic
3/13/2014
2
Fundamental Assumption Checked Before Relative Potency Calculation
• Similarity of the test and standard material
• Desire is to show that the only difference between the samples is concentration
• This assumption is checked by evaluating the parallelism or similarity of the dose response curves
Acceptance Criteria
• ‘Parallelism’ of curves. • Do the curves have the same a, b, & d.
• F Test of full vs. reduced models
• Equivalence test for comparisons of a, b, & d.
• Sufficient Assay Response
• Max-to-Min
• Fold Stimulation
• Etc.
• Goodness-of-Fit
• R2
• Residual Sum of Squares
Initial Acceptance Criteria
• Difficult to set without experience
• Need something generic • F-test for parallelism
• Wide limits for Pair Wise Comparisons
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3
Update Criteria
• After a reasonable set of data has been collected • Reasonable: Broad and sufficient experience (Labs, reagents,
equipment, etc.,)
• Limits are setting off inappropriate signals • Good assays fail
• Bad assays pass
Information Missing Early in Development
• Assay development timelines may be so short as to not allow for sufficient experience with all important factors:
• Numerous critical reagent lots
• Variety in analysts
• Variety in labs
• Variety in equipment (plate readers, pipettes, etc.,)
Appropriate to Set New Limits?
• If assays start failing criteria, determine which situation you are in:
• 1. Bad assays are appropriately failing
• Investigate causes of failures and improve
• 2. Good assays are inappropriately failing
• Adjust criteria to allow good assays in the future to pass
• Definition of good is important
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What is a good assay?
• Accurate
• Precise
• Ensure that curves are parallel
Set New Limits
• Data in hand • Look at historic data and set limits at edges of experience
• Quantify variability
• Setup studies to capture appropriate variation in factors of concern (Analysts, labs, reagents, etc…)
• Design Space
• Attempt to push criteria to extremes and determine if the quality of results is maintained
Set New Limits
• Calculate limits based on any one of the 3 procedures on previous slide
• Make sure assays are still accurate and precise at or near the proposed criteria
3/13/2014
5
Case Study 1 (AlphaScreen® Binding Assay)
• Max-to-Min limit was set in development lab (Max to Min ≥ 40)
• Assay transferred to new lab
• Shortly after transfer, Max-to-Min failures began to occur
Example Curve
• Curves and Potencies Looked
Good Despite Max-to-Min
Failures
• Max-to-Min = 17.8
• Relative Potency = 0.995
Investigation Findings
• The receiving lab for the method transfer was using a different lot of AlphaScreen® beads
• The beads come from a single source and only one lot was used during the method development
• Determined that results with new lot of beads were still accurate and precise
• A study was set up to determine how low the Max-to-Min could go and still have accurate and precise results
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Accuracy and Precision vs. Max-to-Min
• Determined that assay was
accurate and precise with
much lower Max-to-Min values
• Lowered the acceptance
criteria and monitored the
assay
Case Study 2 (Gene Expression Bioassay)
• Assay began to have a few failures and several values right at the limit for Max-to-Min
• Investigation was initiated and hypotheses were tested
Max-to-Min Pre-Investigation
3/13/2014
7
Investigation Findings
• Cell density identified as the most likely cause of low Max-to-Min
• Cell Density was then increased and the data were monitored
Max-to-Min
Relative Potency
Increased Cell Density
Implemented
Higher Ppk = lower probability of Out-of-Specification result
3/13/2014
8
Conclusion
• Acceptance criteria that are created and met during development may not be appropriate through the method lifecycle.
• Key factors influencing acceptance criteria variability including laboratory, analyst, instrument, reagent lot, reagent concentration, etc. should be monitored and evaluated for accuracy and precision.
• Fitness for use of the method must be ensured as changes are made to criteria
A Holistic Systems Approach to Controlling Bioassay: Lessons Learned
Bhavin Parekh
Eli Lilly and Company, Indianapolis, IN USA
Bioassays are one of the most complicated assays implemented as part of the analytical control strategy
for the development of bioproducts. Unfortunately, they can unexpectedly falter when you least expect. I
will present case examples highlighting several areas (eg., assay platform, curve characteristics, assay
design, criticial reagents) that need to be taken into account to control the assay from a holistic
perspective.
NOTES:
1
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
Strategic considerations in developing bioassays to support a biotech portfolio
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
3/13/2014
2
Case Study 1
Product X is a protein product
Assay ulitizes UMR-106 (osteosarcoma) cell line which expresses the receptor endogenously
Method was validated to support clinical development and post-approval
Parekh, CaSSS conf., 2009 Copyright © 2009 Eli Lilly and Company
13-Mar-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
13-Mar-14 File name/location
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
3
13-Mar-14 File name/location
Historical Performance: Assay Response
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
13-Mar-14 File name/location
Historical Performance: L-term Analysis
13-Mar-14 File name/location
Historical Performance: Outcomes
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
4
13-Mar-14 File name/location
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 Determined Conjugate dilution buffer identified as primary cause
13-Mar-14 File name/location
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
13-Mar-14 File name/location
Lesson Learns
Risk 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
Risk 2: Assay complexity Multiple liquid transfer and pipetting steps
Labor intensive
Prone to errors
Sensitive to analyst technique
5
13-Mar-14 File name/location
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
13-Mar-14 File name/location
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 CRL
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
6
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
Re
lati
ve
Lig
ht
Un
its
Conc.
Ref Std
Drug X
5% RLU change = 14%
Potency difference
Potency = 152%
Shallow slope (<1)
Two Linear points
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
7
Experiment Conducted to Determine Effects of
Volumetric vs Gravimetric in Drug Y Bioassay
40
60
80
100
120
140
160
180
200
20 uL Volumetric 100 uL Volumetric Gravimetric
%R
ela
tiv
e P
ote
ncy
Individual Run Bioassay Results by Dilution Method
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 gene silencing
8
Company Confidential Copyright © 2013 Eli Lilly and Company
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
Signal remains stable over several passages
Parekh, CaSSS conf., 2009 Copyright © 2009 Eli Lilly and Company
Passaged 2x week for 16 weeks. Acceptable even in the absence of selection = stable genome integration
Company Confidential Copyright © 2013 Eli Lilly and Company
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
9
Future technology exploration….
Parekh, CaSSS conf., 2009 Copyright © 2009 Eli Lilly and Company
Several genomic editing technologies allow generation of reporter gene at gene-specific sites
TALENS Zn Fingers Crispr-Cas9
Learnings
Parekh, CaSSS conf., 2009 Copyright © 2009 Eli Lilly and Company
Platform assays Helps build expertise Leverage cross project learnings Shared instrumentation: likely to catch problems
How much control do you want of critical reagents (cells, antigens/ligands, consumable, substrates) Qualify vendors for critical reagents? Make i Additional contractual agreements regarding quality of reagents and timely
delivery?
Take a long term view to assess control of bioassay method Qualifications and validations are ‘snap-shots’ in time 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
Parekh, CaSSS conf., 2009 Copyright © 2009 Eli Lilly and Company
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
Lessons Learned: Choice of Potency Assay and Differential Sensitivity to Degradation Pathways
Kirby Steger
Bristol-Myers Squibb Company, Princeton, NJ USA
Potency assays represent key components in the design of a well-defined control strategy for protein
therapeutics. During drug development, various methods iterations may be in use to determine the
potency of the therapeutic. Care must be taken to fully understand the mechanistic relevance of the
chosen systems for the definition of Critical Quality Attributes (CQAs). A case study will be presented
to illustrate how the choice of potency assays for release and characterization can influence the
designation of CQAs.
Slides were not available at the time of printing.
NOTES:
Bioassays Lessons Learned: Part One Workshop
PANEL DISCUSSION – Questions and Answers
Katrin Buss, BfArM, Germany
Denise Gavin, CBER, FDA, USA
Bhavin Parekh, Eli Lilly and Company, USA
Kirby Steger, Bristol-Myers Squibb Company, USA
Shea Watrin, Amgen Inc., USA
Baolin Zhang, CDER, FDA, USA
Questions to be discussed:
In your experience, which have been the most important lessons learned with respect to bioassay
development?
What are the most important points to consider during bioassay development and/or
implementation for product testing?
What are proven tips and tricks for getting a bioassay to run reproducibly?
How do you mitigate bioassay performance issues if they can’t be avoided?
Did you ever encounter bioassay problems that could not be solved or mitigated?
From a health authority perspective: what are the most frequent bioassay-related challenges
faced by sponsors?
From a sponsor perspective: what are the most frequent bioassay-related questions received from
health authorities?
NOTES:
NOTES:
Bioassays Lessons Learned: Part Two Session Abstract
Session Chairs:
Hélène Gazzano-Santoro, Genentech, a Member of the Roche Group and Thomas Anders Millward,
Novartis Pharma AG
Bioassays represent an essential part of the control strategy for assessing safety and potency of
biopharmaceuticals. There are many potential challenges in bioassay development, implementation, and
maintenance. Avoiding or dealing with these is often an integral part of bioassay development as well as
implementation into routine product testing. This session, which will be divided into two parts, will
present a series of case studies on challenges and successes experienced with bioassays. Each talk will
discuss the specifics of the case study and the key learning’s.
NOTES:
Presenter’s Abstracts
Two-in-One: A Novel Approach of Bioassay Selection for Dual Specificity Antibodies
Guoying Jiang
Genentech, a Member of the Roche Group, South San Francisco, CA USA
Dual-specificity antibodies that simultaneously recognize two different antigens are being considered as
potential therapeutics for human diseases in recent years. How to design and develop a bioassay that is
reflective of the mechanism of action (MoA) and can measure the dual activities poses unique and
exciting challenges. For example, how many bioassays will be needed, one or two? Here we presented a
novel approach of having only one bioassay for a dual-specificity antibody by using a cell line that
expresses both receptors. It was found that the assay was able to measure the antibody effects on both
targets and was stability-indicating. Furthermore, the assay was demonstrated to be able to detect
potency changes in either target with assessment of antibody variants. This “single bioassay” approach
is reflective of the MoA of the intended therapeutic indications and has the potential to be used for other
dual specificity antibodies.
Slides were not available at the time of printing.
NOTES:
Challenges and Strategies in Selecting MOAs-reflective Bioassays for Bispecific Antibody
Xianzhi Zhou
MedImmune, Gaithersburg, MD USA
Abstract and slides were not available at the time of printing.
NOTES:
Challenges in the Development of Potency Assays for ADCs and their Utility to Detect Conjugate
Variants
Sonia Connaughton
ImmunoGen, Inc., Waltham, MA USA
Antibody-drug conjugates (ADCs) are anticancer agents that comprise a tumor-targeting antibody with a
cytotoxic agent attached. ADCs are designed to deliver the cytotoxic agent selectively to the tumor
tissue by targeting an antigen expressed on the surface of a cancer cell. The talk will discuss the
challenges of developing bioassays for ADCs containing a maytansinoid cytotoxic agent. This talk will
also focus on the complementary role of bioassays and analytical assays to study the in vitro activity of
conjugate variants
Slides were not available at the time of printing.
NOTES:
Development of an Alternative, in-vitro Potency Assay for Rabies Virus Vaccines
Robin Levis
CBER, FDA, Rockville, MD USA
The potency assay for currently licensed rabies virus vaccines, The NIH Test, is an animal challenge
assay in mice. Following two immunizations with the test vaccine, mice are challenged via IC
inoculation with a virulent strain of rabies virus. A potency value is assigned based on protection against
rabies virus disease and is quantitated relative to a reference vaccine of known potency. This potency
assay requires the use of many animals, takes a long time, and is highly variable with results ranging
from 25 – 400 %. In addition, the validity criteria for the assay are very stringent leading to many
invalid tests requiring multiple re-tests. The development of an alternative potency assay for rabies virus
vaccines has been ongoing for over three decades. Recently, a new collaborative study has been
initiated to define and validate an alternate potency assay for rabies virus vaccines. Critical scientific and
regulatory considerations for the adoption of an alternative assay will be presented.
NOTES:
1
Development of an alternative,
in vitro potency assay for rabies
virus vaccines.
Robin Levis
Division of Viral Products
Office of Vaccines Research and Review
Food and Drug Administration
March 24, 2014
Disclaimer
“My comments are an informal
communication and represent my own best
judgment. These comments do not bind or
obligate FDA.”
Regulations Related to Potency
for Human Vaccines
No specific tests are defined in the CFR for potency of vaccines. CFR 610.10 states: “Tests for potency shall consist
of either in vitro or in vivo tests, or both, which have been specifically designed for each product so as to indicate its potency in a manner adequate to satisfy the interpretation of potency given by the definition in 600.3(s) of this chapter.
Assigned potency must be shown to correlate with clinical efficacy.
2
Introduction
Rabies vaccines
Potency assignment
NIH potency test
History of alternate test development
WHO collaborative study using SRID – 1980s
FDA/NIBSC working group – 2000s
EDQM/EMA collaborative working group - 2012
Current work on alternate test development
ELISA assay development
Regulatory pathway for licensure
Licensed Rabies Vaccines – US
• sanofi pasteur - Human Diploid Cell Vaccine
• IMOVA X rabies - licensed in 1999
• Pitman-Moore Strain
• grown in MRC-5 cells
• Novartis Vaccines and Diagnostics - Purified
Chick Embryo Cell Vaccine
• RabAvert – licensed in 1989
• Fixed Rabies Virus Strain - Flury LEP
• grown in primary chicken fibroblasts
Rabies virus vaccine potency
Rabies virus vaccine efficacy was originally defined as
protection from death by rabies disease
based on a field study
Iranian wolf study (published 1976)
Potency assignment based on survival – 1 IU/dose
WHO recommends vaccines have a potency of > 2.5 IU/mL
Current rabies vaccines are licensed with a potency
specification of > 2.5 IU/mL (as determined using the
NIH potency test)
Efficacy of currently licensed vaccines has been
demonstrated in controlled clinical trials
No vaccine failures* using current post-exposure
treatment regimen
3
Successful protection of humans exposed to
rabies infection. Postexposure treatment with the
new human diploid cell rabies vaccine and
antirabies serum.
JAMA. 1976 Dec 13;236(24):2751-4.
Bahmanyar M, Fayaz A, Nour-Salehi S, Mohammadi M, Koprowski H.
Abstract
Forty-five persons severely bitten by rabid dogs and wolves in Iran
were treated after exposure with a new rabies vaccine produced in
cultures of human diploid cells. All except one also received one
injection of rabies immune serum. This treatment, in contrast to past
experience with other vaccines, resulted in protection of all individuals
against rabies. Thus, almost a century after the postexposure
treatment of humans was initiated, an effective tool for protecting
man against rabies has finally been developed.
NIH Potency Test (Monogr Ser World Health Organ. 1966;23:145-51.)
Current potency test:
Originally developed for neural tissue based vaccines
Animal-based immune challenge assay: Immunize mice at day 0 and 7 (5 groups/5-fold
dilutions)
I.C. Challenge on day 14
Observe for rabies disease*
Potency is calculated based on survival relative to a reference vaccine.
Assay is currently used as release test and as a stability indicating test
Why do we need a
replacement test? NIH potency test has never been considered a great
assay.
Test uses ~600 mice.
High degree of variability: 25 – 400%
Takes up to 6 weeks to complete.
Pass on potency is the geometric mean of two valid
tests in the US and Canada.
Single test used everywhere else.
Ongoing discussions on test replacement for several
decades
Collaborative studies to establish an alternative test started in
the early 1980s
4
A collaborative study on the use of single radial
immunodiffusion for the assay of rabies virus
glycoprotein.
J Biol Stand. 1984 Jul;12(3):283-94.
Ferguson M, Seagroatt V, Schild GC.
Abstract
The single radial immunodiffusion (SRD) technique has been applied to the
assay of the glycoprotein content of rabies vaccines produced in cell cultures.
Fourteen laboratories in seven countries participated in a collaborative study to
evaluate the reproducibility of the SRD technique; some laboratories also
examined vaccines in the mouse protection (NIH) test and by enzyme
immunoassay. Good agreement was found between potency estimates using
the SRD technique: the geometric coefficients of variation for combined
potency estimates of all laboratories were about 10%. SRD assays appear to
have a role for the in vitro assay of antigen content of vaccine and could
complement results obtained in in vivo assays which are subject to wide
variability.
Use of the single radial immunodiffusion test as a
replacement for the NIH mouse potency test for
rabies vaccine.
Dev Biol Stand. 1986;64:73-9.
Fitzgerald EA, Needy CF.
Abstract
The method currently recommended by the World Health Organization (WHO) for the
potency assay of rabies vaccine is the NIH mouse potency test, a highly variable test
requiring large numbers of animals. The Single Radial Immunodiffusion (SRID) test, an in
vitro test, has been used successfully for the quantitation of hemagglutinin in inactivated
influenza vaccine and is being evaluated for its utility as an assay for the rabies virus
glycoprotein, considered to be the major protective antigen, of rabies vaccine. Potency
values calculated using the SRID test were compared with those calculated using the
NIH test for rabies vaccines produced in cell culture. The within-test variability was
significantly lower with the SRID test but the potency values were generally higher than
those from the NIH test. Vaccines which assay below the minimum acceptable
potency value (2.5 International Units/ml) in the NIH test generally gave values
above that level in the SRID test. The implications of these results on rabies
vaccine control testing are discussed.
Alternatives to the NIH Rabies
Vaccine Potency Test
Working group re-convened
FDA sponsored workshop - September 2000
Representatives from industry (Chiron
Behring and Aventis Pasteur), CDC, Thomas
Jefferson University, Kansas State University,
NIBSC, AFSSAPS, PEI, EDQM
Collaborative study between CBER, NIBSC,
and two industry sponsors
Goal of study was to develop an ELISA assay that
would potentially serve as an alternative potency
assay
5
Overview of Study (2000)
Development of an in vitro ELISA to test for
antigen content in rabies vaccines
ELISA to replace NIH test as a release test
for potency
No requirement for correlation with NIH test
Consistency of manufacturing
ELISA to replace NIH test to test shelf life
and stability
Show that ELISA will identify sub-potent lots
Initial ideas for replacement
potency test (2000)
Development of uniform protocol
Development of uniform reagents
Establishment of standard values as
compared to a reference standard
Mathematical determination of potency
from ELISA results
Mass measurement vs. protection in animals
Lots to test:
normal production lots
sub-potent lots
Lessons learned from
collaboration (2000)
Developed ELISA assay with available reagents
Common reagents were difficult to obtain
Even published ones
Vaccine strain differences matter
Potency relative to a reference standard were
different based on vaccine strain and reagents
used
Able to identify sub-potent lots
Data correlated with NIH test results
Industry sponsors continued with ELISA testing
for information purposes.
6
Regulatory Catch 22
What is the approval pathway for an alternate
test?
Sponsors want to know NRAs will approve
alternate test before expending resources to
develop and validate test.
NRAs (in this case – FDA) would like to see
data prior to confirming adequacy of test as a
replacement.
Is it possible to institute a replacement
test for the rabies potency assay?
We have successfully approved the replacement of
several animal-based immunogenicity assays with
ELISA-based assays for the measurement of viral
vaccine potency
Neutralizing epitopes were well defined – or –
Antibody used in the assay bound to critical conformational
epitopes
Clear correlations could be shown between amount of
antigen required to induce immune response in animals vs
amount of antigen measured using alternative in vitro
assays vs immune response in human vaccinees
Can we do this with rabies vaccines?
Replacement of NIH Test
Currently potency is defined by protection
against challenge in animals –
By virtue of survival after challenge, the NIH potency
assay measures a protective response (animals are
doing the work for us)
Potency/dose should correlate with clinical efficacy
Correlation between protection against disease in animals
and potency in humans was established in clinical trials -
If neutralizing epitopes are well defined then it should
be possible to correlate the amount of antigen with
immune response measured in animal. Then,
It should be possible to define potency based on the
amount of antigen in a dose.
7
Replacement of NIH Test
Attributes of alternate assay: Neutralizing epitopes are well defined for rabies
Reagents are defined that recognize these epitopes and distinguish appropriate conformation of virus
Protective immune response has been defined – well accepted for human vaccines
Clear correlations HAVE NOT BEEN shown between amount of antigen required to induce protective immune response in animals vs amount of antigen measured using alternative in vitro assays (how does this translate to vaccine efficacy)
How do we show correlation between potency defined by protection in animals vs potency defined by alternate methods?
Is there a necessity for clinical studies?
EPAA meeting
(The European Partnership for Alternative
Approaches to Animal Testing)
Archachon, France - October 2012
Re-initiate collaborative discussion on
alternate test development
Establish timeline for reagent and assay
development
Two phase development approach
Phase 1- reagent selection - labs are testing
reagents with individual, in house assays.
Phase 2 – collaborative study to define assay
Development of an
alternative potency test
Development of study protocol
Development/availability of reagents
Difficult step*
Development of reagents at CBER
Standard values based on International reference(s)
Definition of potency from test results
Establishment of test specifications
Requirement for clinical data
Lots to test:
Normal production lots
Lots on stability
8
Considerations for Approval
Should there be a requirement to show comparability/equivalence to current mouse potency test?
If tests are not comparable, then the new test must be well qualified. Clear definition of potency: antigen units vs.
international units per dose.
Antibodies used for detection must correlate with protection.
Antibody binding affinity and vaccine strain differences must be well defined (common reference) Due to strain differences, it may be necessary to utilize
different reagents for each vaccine
Depending on the reagents used, the level of free G protein vs. virus associated G protein must be determined.
Test must be able to distinguish potent vs. non-potent lots.
Considerations for Approval
Is there a necessity for clinical data to support the new potency assay? For human vaccines – immunogenicity trial to show
antibody response to vaccines with potency measured using alternative assay
Should this be required for the currently licensed vaccines?
History of manufacturing consistency
History of clinical efficacy
Summary
Renewed global effort by both human and veterinary vaccine manufacturers and control authorities to establish alternative rabies vaccine potency tests.
FDA has licensed non-animal based replacement tests for several vaccine products
FDA is working with sponsors and other regulatory authorities in this endeavor regarding the replacement of the NIH potency test for rabies virus vaccines.
Bioassays Lessons Learned: Part Two Workshop
PANEL DISCUSSION – Questions and Answers
Evangelos Bakopanos, Health Canada, Canada
Sonia Connaughton, ImmunoGen, Inc., USA
Chana Fuchs, CDER, FDA, USA
Guoying Jiang, Genentech, a Member of the Roche Group, USA
Robin Levis, CBER, FDA, USA
Xianzhi Zhou, MedImmune, USA
Questions to be discussed:
In your experience, which have been the most important lessons learned with respect to bioassay
development?
What are the most important points to consider during bioassay development and/or
implementation for product testing?
What are proven tips and tricks for getting a bioassay to run reproducibly?
How do you mitigate bioassay performance issues if they can’t be avoided?
Did you ever encounter bioassay problems that could not be solved or mitigated?
From a health authority perspective: what are the most frequent bioassay-related challenges
faced by sponsors?
From a sponsor perspective: what are the most frequent bioassay-related questions received from
health authorities?
NOTES:
NOTES:
Exhibitor Partner Showcase
Eurofins Lancaster Laboratories, Inc.
2425 New Holland Pike
Lancaster, PA 17601 USA
Phone: 717-656-2300
Website: www.EurofinsLancasterLabs.com
Company Description
Eurofins Lancaster Laboratories, a global leader in comprehensive cGMP-compliant laboratory services,
enables bio/pharmaceutical companies to advance candidates from development through
commercialization, ensuring regulatory compliance, cost effectiveness and achievement of timelines.
See why 800+ leading pharmaceutical and biotech customers continue to trust us with their product
tesing needs at www.EurofinsLancasterLabs.com.
NOTES:
Effective cGMP Bioassay Outsourcing
Alexander Knorre2; Weihong Wang
1
1Eurofins Lancaster Laboratories, Lancaster, PA USA;
2BSL BIOSERVICE Scientific Laboratories
GmbH, Planegg/Munich, Germany
The development and qualification/validation of cell based potency assays is critical for measuring
biological activity and ensuring consistent product quality. Due to the complex nature of biological
assays, managing outsourced cell based potency assay projects can be challenging.
This presentation will offer solutions for outsourcing cell based potency assays with Eurofins Lancaster
Laboratories, including a highly efficient assay group with more experienced PhDs than any other lab
and assay specific training with clearly expressed performance expectations and a greater than 95%
success rate on assay transfers.
With the largest breadth of services and facility capacity for cGMP biologics stability and release in the
industry, Eurofins Lancaster Laboratories provides world wide cGMP Bioassay testing support with
tailored services to meet any client’s specific needs.
NOTES:
1
www.LancasterLabsPharm.com
Effective GMP
Bioassay Outsourcing
Eurofins Lancaster Laboratories, a global leader in comprehensive laboratory services,
enables pharmaceutical and biopharmaceutical companies to advance candidates from
development through commercialization while ensuring regulatory compliance, cost
effectiveness, and achievement of timelines.
cGMP Bioassay Services
Potency Assays
Assay Capability & project experience
Facilities and Equipment
Data analysis Software
Supporting Assays
Staff and Training
Keys to Success
EU Laboratories
Potency Assays – Project Experience
Assay Types
Proliferation/cytotoxicity assays – multiple substrate type
Apoptosis assay (via caspase3/7 activity)
Signaling molecule (cAMP, phospho-protein, AP)
Cell surface receptor binding
Reporter gene assay
ADCC, CDC
Binding ELISA (direct and competitive)
TR-FRET binding assays
Detection Methods: colorimetric, fluorescence, time-resolved
fluorescence, luminescence
2
Potency Assays – Capability
Tailored Services to Meet Client Needs
Complete assay development
Method optimization
Method transfer
Method qualification and validation
Routine testing to support GMP manufacturing and release
Support for stability testing
Capability to bank cells for client assays
Dedicated cell banking team
Preparation of Master and Working cell banks
Testing/Characterization of cell banks – Sterility, Mycoplasma
(rapid or compendial), IVAA
Potency Assays –Equipment & Data Analysis
Software
Plate Readers – 5 units
Molecular Devices M2 – 2 units
Molecular Devices M5e- 1 unit (additional unit on order)
Molecular Devices L – 2 units
Automated Cell Counter
Beckman Coulter Vi-Cell – 2 units
Automated Plate Washers – 7 units
Biotek Precision Robot
RT-qPCR
Applied Biosystems (2-7500s, 1-7900HT)
Data analysis software (with PLA analysis):
Softmax, StatLIA, PLA 2.1
All instruments and software are fully validated and 21 CFR part 11
compliant
Supporting Services
qPCR
Validated in house qPCR assays
Residual DNA (CHO, E. coli, Human, BHK)
Selected viruses (fPERT, MMV, xMuLV, PCV 1/2)
Development and validation of client specific assays
Segregated PCR facilities
– Reagent prep lab, samples prep lab, instrument lab
– HEPA filtered and pressure controlled
ELISA for host cell and process residuals
HCP assays using generic kits (Cygnus)
Process residual ELISAs (Protein A, Benzonase etc)
Method development and validation available for process/product specific HCP ELISA (later phase projects)
Rapid Mycoplasma - Milliprobe
rRNA targeted Transcription Mediated Amplification system with a 5 day TAT for GMP mycoplasma results – Fully Validated
In Vitro Cytotoxicity – USP <87>
3
Facilities
~4,000 square feet of Laboratory space for analysis
Three dedicated cell based assay laboratories
Two labs HEPA filtered, Positively pressured
One lab BSL-2 classified, HEPA filtered, Negatively Pressured
Dedicated ELISA laboratory
Dedicated Molecular laboratory
Dedicated positives and negatives prep rooms
Four dedicated cell banking suites
2 - Class A/B, 2 – ISO 5/7
>2,000 square feet total space
HEPA Filtered
Positive Pressure to surrounding areas
Validated disinfection, cleaning and environmental monitoring
Staff – Cell & Molecular Biology Services
Highly efficient single-site assay group
27 full time employee
20+ FTE at bench
5 group leaders
7 Ph.D. scientists
2 cell culture technician
All assay scientists with Bachelor or Master degree
Cross training in ELISA, cell based assay and qPCR
Training
Core Training modules completed by all analysts include
Aseptic Technique
Tissue Culture
General related lab equipment (pipettes, balances etc)
ELISA, PCR and cell based assay training modules
Assay Specific Training
One-on-One training at ELLI with experienced analyst
Training at client’s facility
Training by client at ELLI
Data Analysis Software Training
4
Keys to Success
More than a typical customer-vendor relationship
Start early and allow sufficient time for transfer and validation
• Perform Gap analysis of current client method if requested to
prepare for qualification/validation
Performance Expectations - clearly articulated from the onset
• Use of project timeline spreadsheets (i.e. Gantt charts)
Conduct training as a first step for method transfer - ELLI
analysts trained by client’s technical expert at the client site or
at LLI whenever necessary
Direct communication between assay expert at your facility and
the principal scientist managing your assay at ELLI
Weekly/biweekly conference calls to discuss status and resolve
issues
BSL BIOSERVICE
Bioassay Services (EU) Munich, Germany
Instrument Readout Capabilities
Absorbance
Luminescence
Fluorescence (incl. TRF and HTRF)
AlphaScreen ®
AlphaLisa®
Analysis by flow cytometry (HTS module equipped, eight colour)
Radioactivity (HTS module equipped scintillation counter)
BSL – Capabilities
Established Bioassays
Cell Proliferation
Cell Survival/ Cell Apoptosis
Reporter Gene
ADCC and CDC
Cell Migration Assay
Binding Assays (ELISA)
Viral CPE Assay
In vivo Bioassay Biological Assay
Tailor-made bioassays
Pharmacopeial Methods (EP/USP):
G-CSF (Filgrastim and PEG- Filgrastim, EP and USP)
Interferon Beta (EP)
Interferon Alpha 2b (EP)
Erythropoietin (EP)
FSH (Urofollitropin) (EP)
Insulin (USP)
5
Bioassay Personnel - BSL
Each project team is set up depending on specific project requirements
Bioassay core team
Head of department (1 FTE)
5 Scientists (4.5 FTE)
6 Analysts (5.5 FTE)
2 Team assistants (1.6 FTE)
Total: 14 (12.6 FTE)
Experience with Therapeutic Antibodies (including Bispecific), Antibody
Drug Conjugates, Hormones/Peptides and Viral (Like) Particles
GMP compliance trained
2006 and 2011: US FDA two day inspection regarding bioassay validation
and bioassay routine testing, no 483 observations
Bioassay: Facts - BSL
Longstanding experience in development/optimization (DOE), validation and performance of bioassays (1984 - present) with global client base
Performance of bioassays using primary cells, permanent cell lines and animals (mice, rats, rabbits)
Project planning and tracking using Gantt charts
Assay monitoring: Statistical process control of bioassay and cell culture parameters using Minitab
Experience in statistical analysis (in-house and external biostatistician)
Brand-new facility with dedicated laboratory space for cell based bioassays and for ELISA (duplication of previous capacity)
New Space expansion - BSL
March 13, 2014 BSL BIOSERVICE Facts 15
3,500 m² animal facility for
rodent & non-rodent species,
in vivo bioassay
700 m² for in vitro testing
1,000 m² for in vitro bioassay,
viral clearance & biosafety
3 operational facilities
south of Munich,
Germany
6
Conclusion
ELLI has the capacity, facilities, project experience and
scientific intelligence to successfully support client assay needs
throughout the lifespan of the project
Working together with Eurofins BSL, we provide world wide
cGMP Bioassay testing support
Constant scientific exchange and project communications
High efficiency method transfer between sites
Cost effective co-validation
Thank you!
Stegmann Systems
Raiffeisenstrasse 2, C1/C2
D-63110 Rodgau/Hesse, Germany
Phone: 49 61 0677 0100
Website: www.bioassay.de
Company Description
Stegmann Systems is a leading software vendor in the field of bio-statistical software for biological
assay since 1996. The main product of Stegmann Systems is the well-known PLA Software for Analysis
of Biological Assays. PLA is currently used by over 400 companies worldwide in GxP and non-GxP
environments. About 20 developers (software specialists, statisticians, quality specialists) are currently
involved with the development of PLA. Their primary focus is the development of easy-to-use software
that supports the bio-statistical tasks and compliance needs of our clients from the beginning of product
development to production in GxP and non-GxP environments.
NOTES:
New Capabilities of PLA
Ralf Stegmann
Stegmann Systems, Rodgau/Hesse, Germany
PLA is Stegmann Systems leading software solution for the analysis of biological assay. In this
presentation a larger number of updates for the analytical features and technical capabilities of PLA will
be presented. These updates cover several classes of biological assay and advanced calculations as well
as the platform: Parallel-Line Assay, Parallel-Logistic Assay (3-,4- and 5 parameter non-linear
regression), Dichotomous Assay (Quantal Response Assay), Slope-Ratio Assay, Control Charting,
Equivalence Margin Development etc..
NOTES:
Promega Corporation
2800 Woods Hollow Road
Madison, WI 53711 USA
Phone: 608-274-4330
Website: www.promega.com
Company Description
Promega Corporation is a world leader in providing innovative solutions to the life sciences industry.
We develop bioluminescent technologies that deliver more biologically relevant data for biologics and
small molecule drug discovery, and we bring best-in-class technology platforms that deliver data with
least amount of variability, in off-the-shelf and custom formats. Speak with a Promega representative to
learn how we can develop a solution for your discovery needs. For more information about Promega,
visit www.promega.com.
NOTES:
Bioluminescent Technologies for Biological Functional Analysis and Protein-Protein Interactions
Mei Cong
Promega Corporation, Madison, WI USA
Bioluminescence reporter gene assays have been widely adapted in quantifying biological activities. The
application of the new NanoBiT technology in monitoring protein-protein interaction will be discussed
as a new method of measuring biological functions.
NOTES:
Bioassays to Support Biopharmaceutical Development
Session Abstract
Session Chairs:
Katrin Buss, BfArM, Federal Institute for Drugs and Medical Devices and Helena Madden, Biogen Idec
Measurement of biological activity is required throughout all stages of biopharmaceutical and cellular
and gene therapy product development programs. Typically development and implementation of
bioassays takes place in a phase-based manner. Simple methods adequately support early stage
development and are followed by a progression to an MOA-reflective cell-based functional method or
methods. Bioassays are used for release and stability testing, to assess comparability during process
changes, and for understanding the impact of structural changes. This session will discuss strategies for
selection, development, maintenance, and execution of bioassays to support successful development of
biological drugs. Regulatory perspectives on measurement of bioactivity at all stages of development
will be shared and discussed.
NOTES:
Presenter’s Abstracts
Implementation of the Next Generation Effector Function Assays for Comparability Assessments
Poonam Aggarwal
Pfizer, Inc., Chesterfield, MO USA
The development of a biotherapeutic program necessitates an in-depth understanding of the mechanism
of action(s) of the product. In vitro cell-based and binding studies are performed in order to characterize
the potential biological activities of mAb products that can perform distinct functions through their Fab
and Fc regions. The assay panels include binding to the target antigen, target-mediated biological
activity, binding to the Fcγ receptors, and complement, as well as functional assays for Fc-associated
effector functions (i.e., ADCC and CDC assays).
Examples will be presented that includes strategy and development of the bioassays to assess antibody
effector function.
Slides were not available at the time of printing.
NOTES:
The Dual Benefit of Structure Function Studies: Better Understanding of Molecules and Help with
MOA-relevant Bioassays
Carl Co
Biogen Idec, Cambridge, MA USA
Structure activity relationship (SAR) and stability studies must be performed to understand the critical
product quality attributes that are responsible for the efficacy and safety of a molecule. In addition, these
studies generate sample variants that are extremely useful in the development of new MOA-relevant
bioassays. Two case studies are presented which describe two approaches to structure function studies:
in one study, long term stability samples were used to assess the effect of product quality changes on
bioactivity; in a second study, Fc variants and forced degraded samples were generated to better
understand the Fc function of the molecule and to aid with the development of more MOA-relevant and
stability-indicating bioassays
Slides were not available at the time of printing.
NOTES:
Standards and Beyond: Challenges of Application of Old Methods to Next Generation Products
Elena Semenova; Penny Post; Tim Fields; Manon Cox
Protein Sciences Corporation, Meriden, CT USA
Propagation of the virus in developing chicken embryos is the predominant way to manufacture
influenza vaccines. Despite the fact that recombinant technology has been used in the manufacturing of
biopharmaceuticals for more than two decades, the first recombinant influenza vaccine was only
approved early 2013. Difficulties in adaptation of classical and widely accepted from 1978 release
bioassay - single-radial-immunodiffusion (SRID), optimized for traditional products to the new
recombinant vaccine were among many reasons for a lag in market appearance of recombinant vaccine,
which make influenza immunization a reality for people with severe egg-allergies and have a greater
potential to provide necessary number of doses fast enough during pandemics. SRID signal depends on
ability of the influenza antigens to diffuse into the agarose gels, form insoluble complexes with specific
antibodies in pores of the gel and remain there after gel washing. The vast majority of reference antigens
for SRID are produced by WHO Essential Regulatory Laboratories are made from egg-derived HA
proteins without purification step. Among factors which may present challenges in applicability of SRID
assay to new technologies are differences in expression systems, level of purification, and chemical
agents used for inactivation of reference antigens. Regulations and guidance, developed and optimized
for traditional products, may need adjustment when applied to novel innovative and often fundamentally
different products.
NOTES:
3/13/2014
1
Standards and beyond: challenges of
application of old methods to next
generation products
Elena Semenova, Penny Post,
Tim Fields, Manon Cox
March 25, 2014
Recombinant Flu vaccines
Propagation of the influenza virus in chicken embryos is the predominant way to manufacture influenza vaccines.
Various problems, associated with the use of embryonated chicken eggs for inactivated vaccines, such as getting high yield egg-adapted reassortant and possible antigenic change during egg adaptation (one of the reason of low vaccine effectiveness in 2012-2013 season, VRBPAC 2013 transcript), and the necessity to attenuate pathogenic strains of the influenza virus (for live vaccines) could be solved through the use of highly purified recombinant subunit vaccines.
A recombinant influenza vaccine has a greater potential to provide necessary number of doses faster during pandemics and also makes influenza immunization a reality for people with severe egg-allergies.
Flu vaccine and recombinant products timeline
1942-1943 1945 1978 2012 2013 1982
First recombinant Flu
vaccine is approved (Protein Sciences
Corporation)
First recombinant product
(insulin) is approved (Eli Lilly)
Difficulties in adaptation of classical and widely accepted release bioassay (SRID),
optimized for traditional products to the new recombinant vaccine were among
many reasons for a more than 30 years lag in market appearance of recombinant
Flu vaccine
3/13/2014
2
SRID as a potency bioassay for Flu vaccines
The single-radial-immunodiffusion (SRID) assay has been
adopted world-wide in 1978.
SRID measures amount of hemagglutinin (HA): viral surface
protein. Antibodies against HA protects from the Influenza
SRID was optimized to provide consistent results when tested
against all of the licensed influenza vaccine preparations at
time of implementation: whole virus (currently discontinued)
and split vaccines. Both vaccines were propagated in eggs
and inactivated by chemical agents (MS Williams 1993).
Advantages of SRID as bioassay
Simple in performance
Robust
Reproducible
An international collaborative study (Wood et al. 1981) demonstrated test reproducibility
(geometric coefficient of variation between laboratories less than 10% ).
Two EU collaborative studies in 1989 and 1990 have reaffirmed test reproducibility (J. Wood Textbook of Influenza 1998).
Correlation with clinical efficacy Numerous clinical trials have validated the test (Ennis et al. 1977; Pandemic Working Group
of the MRC 1977; Nicholson et al.1979)
Numerous follow up clinical studies have confirmed that the test measures immunologically
active HA (J. Wood Textbook of Influenza 1998).
Measure biologically relevant potency,
Ability to detect individual strains in multivalent vaccine
Proven stability-indicating capacity
5
Disadvantages of SRID
SRID requires the production of antibodies to a strain-specific HA (up to 4 months)
SRID is relative bioassay and requires reference standard with assigned potency,
which production and standardization must be performed after production and
characterization of strain-specific antibodies.
For seasonal influenza virus vaccines, which typically contain three constantly
changing sub-types, new antisera and reference standard must be made and
standardized each time a new strain is incorporated into the vaccine formula
during annual reformulation.
Each of these steps are time consuming and costly. More importantly reagent
preparation may be a major factor in delaying of supplies of a vaccine during
pandemic.
Suboptimal for vaccine which are produced in other than egg-based systems:
in cell culture (J. Wood, Textbook of Influenza, 1998 Chapter 25) and using
recombinant DNA technology.
6
3/13/2014
3
This presentation summarizes points to consider for
adaptation of established standards, which are produced for
traditional Flu vaccine, for measuring of potency of novel
innovative and often fundamentally different products.
Description of SRID assay
Calculation of Flu vaccine potency
Standard Sample
1:1
1:1.5
1:2
1:4
dilution
Potency of sample is assigned by
comparing ring diameter with the
standard with US FDA assigned
potency
3/13/2014
4
Standard and Vaccine in SRID assay
Different vaccines – the same standard
Flu vaccines, which are required to use SRID as a potency bioassay
Inactivated vaccine •Inactivated either by alkylating or crosslinking agents
•Predominately egg-produced Recombinant
vaccine Produced in insect cells
Contains>90 % of rHA Split vaccine •Virion split by detergent •Virion contains 38-44 % of HA (Ruigrok, Textbook of Influenza 1998)
Subunit vaccine •After splitting vaccine is enriched for surface antigens (HA and NA)
Standard for SRID is a freeze-dried inactivated whole virus,
initially optimized for split and whole vaccines.
Whole vaccine •Discontinued at the end of 1970’s due to side effects
Calibration of seasonal/pandemic influenza
antigen working standards
Reagents are prepared by the four WHO Essential Regulatory
Laboratories (ERLs):
• Australia – Therapeutic Goods Administration (TGA)
• Japan – National Institute for Infectious Disease (NIID)
• United Kingdom – National Institute for Biological Standards
and Control (NIBSC)
• USA – Center for Biologics Evaluation and Research (CBER)
One of ERLs (lead ERL) prepares Primary Liquid Standard (PLS) and
a large batch of freeze-dried antigen (inactivated whole virus), which
are distributed to all other ERLs for independent calibration.
Data generated by the ERLs are collected by the lead ERL and
compiled for the final potency value agreement and confirmation.
Manufacturers’ data may be considered, if available. The lead ERL has
final authority to assign a potency value. EXPERT COMMITTEE ON BIOLOGICAL STANDARDIZATION (Geneva, 17-21 October 2011), Generic protocol WHO 2012
3/13/2014
5
Primary Liquid Standard (PLS)
PLS is inactivated whole virus
HA is 38 - 44 % of
total virion mass (Textbook of Influenza, R.W.H.
Ruigrok 1998)
Scheme is based on “Generic protocol for the calibration of
seasonal/pandemic influenza antigen working reagents” WHO 2012
PLS is calibrated by physicochemical means
Points to consider for recombinant Flu
vaccine at PLS calibration step
Co-migration effect for other proteins (different proteins at the same band on SDS-PAGE) can be as high as 25 % (Getie-Kebtie et al, An. Biochemistry 2011).
General guidance for confirmation of accuracy of PAGE band analysis for ERLs: HA content should be between 20% and 50% of total protein. Recombinant protein is > 90 % of HA in the sample and free from influenza viral proteins.
Ratio between HA and total protein for PLS is assigned based on band densitometry analysis of sample which have less than 50 % of HA, thus co-migration effect from other proteins is possible. Even moderate co-migration effect could put potency number “off” for highly pure HA recombinant vaccine.
Freeze-dried working reference standard
PLS with assigned
HA content
freeze-dried antigen
(working reference standard)
SRID assay
Assigning potency to working reference standard
Distribution to manufacturers of Flu vaccine
3/13/2014
6
Points to consider for recombinant
Flu vaccine
As reported previously, the SRID gave unreliable potency results when different chemical agents used for inactivation of the virus were used for preparation of reference standard and vaccine samples (R. Gupta, and W. McCormick, US FDA, WHO/FDA/HC Workshop, June
2010, Ottawa, Canada). These agents modify proteins in a different ways, and therefore inactivated viral preparations may have different gel mobility. The recombinant HA vaccine does not require an inactivation step.
Traditional influenza vaccines contain significant amount of other viral and host proteins, which may be involved in formation of stable complexes. This further increases complexity of the sample post treatment with inactivating agents.
All these facts may contribute to a higher or lower gel mobility of the highly purified recombinant HA proteins compared with
egg-based reference antigens.
Points to consider for recombinant
Flu vaccine (cont)
The protein ring intensity and appearance, which is also likely dependent on the way of HA presentation in the sample, may be different between standard and measured samples, produced in heterologous systems.
Such differences could create problems in automated reading of the gels, which negatively affects accuracy and precision of
the bioassay.
CONCLUSIONS
Regulations and guidance, developed and optimized for traditional products, may need adjustment when applied to novel innovative and often fundamentally different products.
Points to consider outlined above may require either an adaptation of traditional method for the new products, for example through the preparation of compatible reference reagents, or the development of standard-independent physicochemical assays measuring the physiologically active form of the proteins present in the vaccine.
Global Implementation of Bioassays – Things to Consider
Bruce Meiklejohn
Eli Lilly and Company, Indianapolis, IN USA
To be competitive in today’s pharmaceutical industry companies need to be able to develop and
distribute their products on a global basis and can no longer focus on a specific region or country.
Multiple regions add complexity to the already difficult development process. The development of a
biotechnology pharmaceutical product requires multiple areas of scientific expertise and regulatory
understanding to be successful. The biotechnology industry continues to mature and analytical
methodologies advance and are more discriminating for the characterization of complex biological
products. Different countries and regions are in different stages of acceptance of analytical control
strategies where historically the process defined the product and clinical experience established the
safety and efficacy. As the development and commercialization of these products becomes a global
activity the need to meet regional requirements is becoming more diverse. In the past decades ICH
helped to normalize expectations in certain regions of the globe. As the industry continues to grow
regional or specific country expectations emerge making the global development process gain in
complexity. These differences include specifications, testing requirements, shelf life and sample
requirements for the health authorities to name a few. Examples of differences in expectations and
strategies will be discussed to help with clinical trials and post approval life cycle management.
NOTES:
3/13/2014
1
Global Implementation of Bioassays, Things to Consider
Bruce Meiklejohn, Eli Lilly and Company, Indianapolis IN USA
25 March 2014
The opinions expressed in this presentation are solely those of the individual presenter, and do not necessarily reflect the views of Eli Lilly & Company.
Confidential 2014 Eli Lilly and Company
• To be competitive in today’s pharmaceutical industry, companies need to be able to develop
and distribute their products on a global basis and can no longer focus on a specific region or
country. Multiple regions add complexity to the already difficult development process. The
development of a biotechnology pharmaceutical product requires multiple areas of scientific
expertise and regulatory understanding to be successful. The biotechnology industry
continues to mature and analytical methodologies advance and are more discriminating for
the characterization of complex biological products. Different countries and regions are in
different stages of acceptance of analytical control strategies where historically the process
defined the product and clinical experience established the safety and efficacy. As the
development and commercialization of these products becomes a global activity the need to
meet regional requirements is becoming more diverse. In the past decades ICH helped to
normalize expectations in certain regions of the globe. As the industry continues to grow
regional or specific country expectations emerge making the global development process gain
in complexity. These differences include specifications, testing requirements, shelf life,
sample requirements for the health authorities to name a few. Examples of differences in
expectations and strategies will be discussed to help with clinical trials and post approval life
cycle management.
Confidential 2014 Eli Lilly and Company
3/13/2014
2
Background
• To be competitive in today’s pharmaceutical industry companies need to be able to develop and distribute their products on a global basis.
• The development of a biotechnology derived pharmaceutical product requires multiple areas of scientific expertise and regulatory understanding to be successful.
• Companies can no longer focus on a specific region or country.
• Multiple regions add complexity to the already difficult development process.
Confidential 2014 Eli Lilly and Company
• The biotechnology industry continues to mature and analytical methodologies advance and are more discriminating for the characterization of complex biological products.
• Countries and regions have different expectations on analytical control strategies
• Historically the process defined the product and clinical experience established the safety and efficacy.
Confidential 2014 Eli Lilly and Company
Background
Background
• As the development and commercialization of biotechnology products become a global activity, the need to meet regional requirements is becoming more diverse.
• ICH has helped to harmonize expectations in certain regions of the globe.
• Regional and specific country expectations emerge making the global development process increase in complexity.
Confidential 2014 Eli Lilly and Company
3/13/2014
3
Development Complexity
Needed Studies
Release Tests In-Process Tests Stability
Clinical Efficacy/Safety
Bioassay
Additional Characterization
Animal PK/PD
Human PK/PD
CQA Impact Potential
Significant
Minimal
Moderate
Phase of Development
Post Approval Commercial
Pivotal
Tox
FHD
FED
Manufacturing Process
Cell Line
Purification
Manufacturing Site
Fermentation
Scale-Up
Formulation
Chemically Syn Peptide
Structure Complexity
Antibody drug Conjugate Complex Protein
Simple Protein
Recombinant Peptide
Fusion Protein
Confidential 2014 Eli Lilly and Company
CMC Inputs for Global Registrations
Site
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CM
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Plu
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Pro
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Val
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Confidential 2014 Eli Lilly and Company
Manufacturing Site Registrations
Confidential 2014 Eli Lilly and Company
• A manufacturing site may require additional information for a CT or registration submission for certain countries
For example:
―Site registration and the need for GMP certification
• Prior to submission
3/13/2014
4
Confidential 2014 Eli Lilly and Company
• CTD content for some countries outside the United States require additional information
• CMC “Plus” documents include:
COAs
Reagents (Company generated)
Representative Standard Curves
Batch Records
CMC “Plus” Documentation
Confidential 2014 Eli Lilly and Company
• In certain countries the local Quality Control Labs need to release commercial Drug Product: e.g., Japan, Korea, Mexico, Brazil, Argentina
• Transfer of methods to the Quality Control Labs is needed prior to launch [not submission]
• Some health authorities will perform testing in MOH labs, so •
method , reagents ,& instrument details will be needed ―e.g., China, Russia, UAE
Companies may be asked to provide samples and reference
standards
Analytical Method Transfers
Confidential 2014 Eli Lilly and Company
Country Performed By Comments (including whether full retesting or different tests/standards used)
Albania MOH lab Test parameters listed on COA
Algeria MOH lab For the registration, the national laboratory of control tests all the parameters but once the product is registered, the test will be on standard parameters and on each batch imported
Armenia MOH lab only selected finished products; selected tests including ID
Azerbaijan MOH lab only selected finished products; selected tests including ID
Belarus external no other information available
Bosnia MOH lab Quality control of the first imported batch includes full testing of all parameters in accordance with approved Specification. Every subsequent analysis of every subsequent imported batch in
most cases is partial-Control laboratory makes decision.
China State lab National lab determines testing for import to be tested by State lab.
Costa Rica MOH lab Testing on the first imported lot only
Croatia external Only perform partial testing.
Egypt MOH lab Full testing required.
Georgia MOH lab only selected finished products; selected tests including ID
India MOH lab All insulin and insulin analog lots tested.
Iran MOH lab Tests are not performed on each shipment, rather they are done randomly on one shipment
each while MOH informs the agent in advance
Iraq MOH lab Tenders are usually divided in several shipments, tests are done on the first shipment of each
tender
Israel MOH lab Full testing. The MoH will test only first batch of every new product, marketed products
following any variation and all biological products. All other batches will be released by the company's Qualified Person
Jordan MOH lab testing is performed on the first seven shipments to the country
Kazakhstan MOH lab Full retesting of each lot
MOH Testing Upon Import (not registration)
3/13/2014
5
Responsible for Routine Testing
Country Regulatory Requirement for Testing upon
Importation Comments
Argentina Company Test all parameters for all imported batches.
Brazil Company After Nov 2012, biotech products received as fully finished products
are not tested
Canada Company Test all parameters for all imported batches.
Chile Biotech - external lab and MOH Test some parameters for all imported batches. Can reduce testing if
you have 2 years/20 lots of consistency.
EU Company Test all parameters for all imported batches.
Japan Any lab Use JP standards
Korea Company Test all parameters for all imported batches.
Mexico biotech performed in lab authorized by MOH Must transfer methods to company in Mexico who transfers them to MOH. Reduced testing possible after 3 years of importation or 20
lots.
Peru Company
No biotech testing. Micro testing is required on all imported batches. Legislation was for MOH to perform testing only in the first lot
imported after initial approvals and 5 year renewals.
Uruguay Company
Not yet implemented but proposed to test all imported batches in
future.
Confidential 2014 Eli Lilly and Company
Confidential 2014 Eli Lilly and Company
• Registration Samples – Samples typically need to come from commercial supply chain and will need a COA from final packaging and distribution site that reflect commercial specifications
• Other considerations:
Samples need sufficient dating for shipping and testing
Need an import license before you can send samples
Registration Samples
MOH Testing (registration samples only)
Country Performed By Comments (including whether full retesting or different tests/standards used)
Colombia MOH lab Draft regulation would require testing for registration samples
Dominican Republic MOH lab Currently test registration samples only.
Ecuador MOH lab Currently test registration samples only.
El Salvador MOH lab Currently test registration samples only.
Guatemala MOH lab Currently test registration samples only.
Honduras MOH lab Currently test registration samples only.
Nicaragua MOH lab Currently test registration samples only.
Panama MOH lab Currently test registration samples only.
Confidential 2014 Eli Lilly and Company
3/13/2014
6
Certificate of Pharmaceutical Product (CPP)
• The Certificate is needed by the importing country when the product is intended for registration
• Certification is issued by the source country in the WHO format
• Used by countries to assess the quality of pharmaceutical products for registration or importation
• WHO recommends to national authorities to ensure that analytical methods can be confirmed by the national laboratory
• Single product
• Establishes the status of the pharmaceutical product and of the applicant for this certificate in the exporting country.
Confidential 2014 Eli Lilly and Company
Confidential 2014 Eli Lilly and Company
• Exporting and importing country
• Name, dosage form and composition of the product
• Registration and marketing status of the product in the exporting country
• Number of product license and date of issue
• Product information
• Details on the applicant
• Statement to confirm whether or not the document is issued in the format
Examples of the type of information found on a Certificate of Pharmaceutical Product
Certificate of Pharmaceutical Product
Confidential 2014 Eli Lilly and Company
• Many non US countries require a CPP prior to submission, but some can submit without it if local clinical development occurred.
• Examples;
China will allow the company to submit without a CPP if all development [CMC & Clinical] was performed in China
Mexico will allow a company to submit without a CPP if Phase 3 studies occurred in Mexico
Some countries will allow a company to submit without the CPP but need it prior to approval [need to be negotiable]
3/13/2014
7
Regional Considerations include:
• Specifications • Stability and shelf life • Samples/shipping • Import/export • Requirements for the local health authorities testing • Method transfer/Reagents/Training/Equipment • Submission Content • Comparators • ICH acceptance • Language
Confidential 2014 Eli Lilly and Company
Comparators
• Comparators are used to establish the safety and efficacy of a drug in clinical trials
• Regional source (US vs. OUS)
Site
Test Methods
Limits
Comparability
Confidential 2014 Eli Lilly and Company
Case Study - China MOH Testing
• Local regulations require that 3 registration samples be provided to Center for Drug Evaluation (CDE) Any cells needed for Bioassays also need to be provided
Importation laws will apply and required testing and certificate
• A product standard document is created to describe the procedures to be used for quality testing by the MOH There may be an opportunity for a company to prepare this
standard document to include additional detail that is not included in the analytical procedure.
• A positive outcome of this testing is needed before the Import Drug License will be granted
Confidential 2014 Eli Lilly and Company
3/13/2014
8
Case Study - Russia
• Dossier is submitted to Ministry of Health (MOH) for review.
• MOH checks for completeness
• MOH sends the document to its expert organization for evaluation.
• The expert institution completes its review and send results back to MOH. • MOH approves clinical trials applications
• MOH makes final decision on issuance of a registration certificate
Applicants are allowed to communicate with MOH only. Limited interactions MOH controls and regulates all steps of the registration process
Confidential 2014 Eli Lilly and Company
Case Study – Russia Information included in submission
• Application
• •IP rights (patents, trademarks)
• •Manufacturing sites (names and addresses )
• •GMP certificates of manufacturing sites (API, DP, packaging,
testing)
• •CPP issued for Russia
• •CoA for DP
Confidential 2014 Eli Lilly and Company
Case Study - Russia
• Companies must provide drug samples and reference
standards for MOH testing of Drug Substance and Drug Product. Import License
• Required at the time of submission
• MOH sends material to expert lab for testing of drug and API
samples
• Successful testing by the MOH is the first part of the marketing authorization process
Confidential 2014 Eli Lilly and Company
3/13/2014
9
Case Study - Russia
Confidential 2014 Eli Lilly and Company
• A company submitted a registration dossier which contained the full analytical procedures
• No ability to provide additional method details
• Rejection notice from Russia for the application based on the outcome of the quality testing
• There is no provision in the Russian law to allow for the MOH to ask questions to resolve a laboratory issue during testing
Case Study – Russia Key Learning Points
Confidential 2014 Eli Lilly and Company
• A successful marketing application is dependent on MOH testing of DS & DP
• There is no ability for the MOH to contact a company with questions or problems encountered during the testing The only option is to reject the application
• “New” Pre-submission consultations with MOH officials is now allowed since July 28, 2011
• Question - Is there a process that additional information can be provided in future applications when methodology is complex?
Case Study - Korea
• A company did not want to repeat all of the release tests, so they were asking for reduced testing
• The company was asked for a technical rationale and data to support why certain tests were not needed upon importation
• A scientific rationale was provided, KFDA accepted the approach
Confidential 2014 Eli Lilly and Company
3/13/2014
10
Conclusions
• Early planning for method transfers to QC/MOH labs will be critical for complex biotechnology products
• Communication with the local MOH labs need to define the methods that will be transferred
• If all tests will not be repeated at local labs, a robust dataset will need to be available as justification
• For certain countries, i.e. Russia and China, more detailed instructions for complex methods is advised
Confidential 2014 Eli Lilly and Company
Acknowledgements
• Susan Stolz
• Allison Wolf
Confidential 2014 Eli Lilly and Company
Thank you
Confidential 2014 Eli Lilly and Company
Bioassays to Support Biopharmaceutical Development
Workshop
PANEL DISCUSSION – Questions and Answers
Poonam Aggarwaal, Pfizer, Inc., USA
Carl Co, Biogen Idec, USA
Chana Fuchs, CDER, FDA, USA
Bruce Meiklejohn, Eli Lilly and Company, USA
Elena Semenova, Protein Sciences Corporation, USA
Questions to be discussed:
What are the regulatory expectations for correlating early and late-stage assays?
What studies are needed to replace target binding assays with more complex functional methods?
When would a binding assay be considered sufficient throughout development?
What are some strategies for development of bioassays to support development of drug
candidates with multiple MOAs
Can Quality by Design principles be used to support development and implementation of robust
bioassays?
When should structure activity relationship studies be performed?
How can results of structure activity relationship studies support establishment of specifications?
Is it possible to replace potency assays by physicochemical tests based on structure activity
relationship studies?
NOTES:
NOTES:
Bioassay Controls & Control Strategies Session Abstract
Session Chairs:
Evangelos Bakopanos, Health Canada and Sally Seaver, Seaver Associates LLC
Most bioassays that measure the potency of a therapeutic product measure the reaction of a living
system, either cells or animals, to different doses of the therapeutic. The response of this system to a
sample is compared to the response of the reference standard; the potency of the sample is proportional
to the relative shift in the sample curve from the reference standard curve. These assays also have
several procedural steps and custom components which make them highly susceptible to variability and
can impact their ability to generate reliable relative potency estimates.
Every well-developed bioassay has a series of controls or control strategies that ensure that they are
operating as intended. Typical controls for bioassays include procedural measures and assay design
elements to minimize assay variability, such as system/assay and sample suitability or acceptance
criteria. There are also statistical criteria for assessing the similarity of the reference standard and sample
curves.
This session will focus on bioassay controls and control strategies to assess assay validity and sample
validity for monitoring assay performance and ensuring that a given assay remains “fit for use”
throughout the various stages of its lifecycle.
NOTES:
Presenter’s Abstracts
Assay Acceptance Criteria for Multiwell-Plate-Based Biological Potency Assays
C. Jane Robinson
National Institute for Biological Standards and Control (NIBSC), Hertfordshire, United Kingdom
All analytical techniques require criteria to judge objectively whether a test has been executed properly.
Guidance has been developed previously for physicochemical assays but this guidance is not necessarily
appropriate or sufficient for bioassays. A paper entitled “Assay acceptance criteria for multiwall-plate-
based biological potency assays” has been published in BioProcess International 12(1) January 2014,
p30-41, as a draft for consultation. This paper lays out proposed guidelines for Assay Acceptance
Criteria and Sample Acceptance Criteria for bioassays. To prevent the document becoming unwieldy,
this set of guidelines has been limited to biological potency assays performed in multiwell plates.
Analytic dilution assays using multiwell plates are the most widely used platform for in vitro bioassays
and immunoassays, but multiwell plate formats introduce specific artifacts to the measured responses, so
assay design and appropriate acceptance criteria are of critical importance. This presentation will
address issues raised in the draft for consultation, including commonly used criteria, typical limits and
problems which can arise from setting inappropriate criteria. Comments are invited from all interested
parties to assist in compiling the final guidelines and can be submitted to the Biopharmaceutical
Emerging Best Practices Association at www.bebpa.org.
NOTES:
13/03/2014
1
Assay Acceptance Criteria for Multiwell-
Plate–Based Biological Potency Assays
Jane Robinson
Jane.Robinson@nibsc.org
National Institute for Biological Standards and Control, UK, www.nibsc.org .
CASSS Bioassays 2014: Scientific Approaches & Regulatory Strategies
24 – 25 March 2014, Silver Spring, Maryland USA
Quantitative biological assays (potency assays, bioassays) to
measure biological activity are essential
……for specifications, comparability & stability studies,
product & process development, …………
Multiwell plates - most common platform for in vitro bioassays &
immunoassays
CASSS Bioassays 24 – 25 March 2014 Silver Spring MD
• Convenient for handling large
numbers of doses & replicates
• Wide range of plate types with
standardized footprint
• Supporting equipment and
measurement systems
Multiple components of overall assay system must be within defined
limits for execution of a valid assay. Tested before or during assay =
System Suitability Testing (SST)
Available guidance primarily for physicochemical techniques
Bioassay responses susceptible to wide variety of factors
need to use data produced by individual assay to judge whether that
assay executed correctly
Focus on criteria that can be applied to assay results
Assay Acceptance Criteria (AAC) (often considered subclass of
SST). Classification as SST or AAC not necessarily important
SST & AAC must be appropriate to the assay system & purpose &
design of assay
CASSS Bioassays 24 – 25 March 2014 Silver Spring MD
For any analytical technique, need to judge objectively
Is the assay valid?
before considering the results for the test samples
13/03/2014
2
BioProcess International 12(1) January 2014 pages 30-41
CASSS Bioassays 24 – 25 March 2014 Silver Spring MD
Multiwell plate formats
…… introduce specific artifacts to the measured responses
• individual plates & well positions within a plate may be subject to
different conditions. Non-random distribution of samples & doses
(eg. all dilution curves for one sample in edge rows, or reference
standard in one plate & test sample in another) can introduce bias to
the measured relative potencies. Assay design is crucial
• Selection of acceptance criteria is inextricably linked to assay design
• This discussion uses mainly examples from cell-based potency
assays in 96-well plates, but applies to other assay systems (non-cell-
based functional, binding, immunoassays, ….) & plate formats
CASSS Bioassays 24 – 25 March 2014 Silver Spring MD
Assay acceptance & sample acceptance
Propose: 2 separate sets of acceptance criteria:
1) Assay Acceptance Criteria (AAC) based on responses of control
samples and reference standard
2) Sample Acceptance Criteria (SAC) based on responses of each
separate sample
If the plate fails AAC, then there is no processing of test sample data
If one test sample fails its SAC, then that particular test sample potency
quantification fails. Other test samples on the plate are assessed
separately
CASSS Bioassays 24 – 25 March 2014 Silver Spring MD
AAC pass
fail
SAC each
sample
13/03/2014
3
For most biopharmaceuticals, potency is assessed in a bioassay by
comparison of
dose-response curves of test material & reference standard
log dose
resp
on
se
fundamental requirement for obtaining a valid relative potency: reference
standard & test sample must behave similarly in the assay system
The dose–response curves have the same
mathematical form. Any displacement between curves
along the log-concentration axis is constant. The
displacement is used to calculate the relative potency
test sample standard
Assay control sample
AAC based primarily on comparison of the dose-response curves of
control sample(s) & a reference standard
at least one control sample should behave similarly to the standard &
the (expected behavior of) the test samples in this assay system. We
propose the name “assay control sample” for this control
assay control sample & standard are tested at multiple dilutions; other
controls may be tested at multiple / a few / a single dilution
AAC applied to each plate independently dilution curve for
assay control sample & dilution curve for standard run on every plate
CASSS Bioassays 24 – 25 March 2014 Silver Spring MD
Assay control sample
assay control sample as independent of standard & test samples in
origin as possible … with constraint all behave similarly in the assay
standard & assay control sample commonly different lots from the
same production method
using 2 dilution series of the standard is not sufficient: this tests only
the dilution procedure & subsequent handling
……..however ….in early assay development, 2 dilution series of the
standard may be the only possibility
assay control sample & standard should be as similar as possible to
test samples wrt formulation, concentration, etc.
assay control sample, standard & test samples should all be prepared &
tested in same fashion …. but …. an initial step of reconstitution,
dilution, etc, may be necessary
CASSS Bioassays 24 – 25 March 2014 Silver Spring MD
13/03/2014
4
Criterion of similarity of dose-response curves
log dose
resp
on
se
of assay control sample & reference standard & = essential AAC
of test sample & reference standard = essential SAC
• measure the response of each at several doses spread over an
appropriate range
• assessing similarity depends on statistical analysis
• for some pharmacopeial assays, the method of analysis may be
specified
test sample
standard
assay control sample
Four-Parameter Logistic (4PL) non-linear regression
curve fitting
concentration (x)
resp
on
se (y
)
• 4PL curve fit widely used
• sigmoidal curve symmetrical around the inflection point (5PL includes
asymmetry factor)
• if data heteroscedastic, can apply weighting algorithms
• each parameter may provide an acceptance criterion for determining
similarity of the dose-response curves
D = maximum asymptote
A = minimum asymptote
B = Hill Slope C = inflection point
y = D + A – D
1 + (x/C)B
Absolute versus relative values
• A potency assay is comparative: potency is measured relative to that of
the reference standard.
• ……… therefore absolute values for characteristics of a response
curve should not be critical
• ……… however, most assay systems function adequately over only a
limited range of conditions.
• The assay is validated for a defined range of conditions
• This is reflected by a limited range of dose-response curve
characteristics
• An unusually high or low value can indicate that the assay system is
not behaving in the usual manner within the validated range.
Examples: ED50, slope, ratio of upper asymptote to lower asymptote
CASSS Bioassays 24 – 25 March 2014 Silver Spring MD
13/03/2014
5
Evolution of Acceptance Criteria during Assay
& Product Development
Acceptance criteria & assigned values change during assay & product
development process
due to improvement in assay performance, accumulation of
more data on assay performance & stricter requirements at later
stages of product development
• limits on some criteria may be tightened
• some limits may need to be widened: initial data may not reflect the full
variation in assay conditions
• additional criteria may be adopted
• some criteria may be removed: it may become evident that they do not
reflect the validity of the assay & they could cause the rejection of
assays that are fit for purpose. Their values can still be recorded, often
“for information only” & can be useful for monitoring & trending.
CASSS Bioassays 24 – 25 March 2014 Silver Spring MD
Acceptance Criteria in Assay Monitoring &
Trending
The values observed for AAC & SAC can be used for assay & sample
monitoring & trending, helping identify aberrant results & whether the
assay is stable
Monitoring & trending a wide range of assay response characteristics is
useful, particularly in early assay development
Monitoring or trending a characteristic should not carry an expectation
that it will be adopted as an acceptance criterion. This discourages
investigation & selection of optimum acceptance criteria
Statistical analysis of the observed values, giving a
statistical process control (SPC) chart, permits
objective analysis of variation in assay performance
Statistical process limits & AAC & SAC are not the same as
product specification limits but they must be appropriate to
accommodate the product specification limits
CASSS Bioassays 24 – 25 March 2014 Silver Spring MD
Setting values & limits for acceptance criteria
using an assay control chart
Example: assay control sample potency
• multiple independent executions of the assay are performed with the
assay control sample included as a test sample
• the acceptance criterion for the potency of the
assay control sample is set as the
mean ± some multiple of the SD
commonly the statistical process control (SPC) limits are set at
± 3 SD …. but …..if the SD is based on only a few measurements, it
is a very uncertain estimate
• can use tolerance intervals - wide when based on a few measurements
& narrowing with more data
table of tolerance limits:
from 5 assays (multiplier ± 10.75) to 199 assays (multiplier ± 3.03)
Orchard T, Biopharm International, 19(11) 2006: 22-29
can establish initial acceptance criteria with (for example) 25 assays &
then revise as more data are collected
CASSS Bioassays 24 – 25 March 2014 Silver Spring MD
13/03/2014
6
Common current practices which cause
problems
Acceptance too dependent on an individual data point.
Eg: upper asymptote = mean of duplicate responses at max dose.
Max % difference between upper asymptotes assay control sample
& standard.
1 aberrant well at max dose of assay control sample on one plate.
Asymptote is aberrant compared with that of standard.
Plate is rejected.
Reportable value = mean of potencies from 2 replicate plates.
No reportable value is obtained
Inclusion of unnecessary, or unnecessarily tight, criteria.
Eg. in some systems ED50 can vary widely between assays which
are fit for purpose. Setting tight limits on an absolute value for
ED50 can result in rejection of an assay that is fit for purpose
CASSS Bioassays 24 – 25 March 2014 Silver Spring MD
CASSS Bioassays 24 – 25 March 2014 Silver Spring MD
Commonly used acceptance criteria
incorporating information from the delegates at the 2013 BEBPA Bioassay conference
Similarity of dose-response curves
Essential AAC & SAC. Used almost
universally
Slope
Widely used. Ratio – part of similarity testing &/or absolute value. Confidence
intervals or ranges eg 0.8-1.25.
Lower asymptote
Ratio – part of similarity testing &/or absolute value
Upper asymptote
Ratio – part of similarity testing &/or absolute value
Ratio upper to lower asymptote Ratio (=ratio of ratios) part of similarity testing &/or absolute value
Inflection point Not commonly used
ED50
Absolute value, widely used for trending but not commonly as AAC or SAC
……..continued…….
CASSS Bioassays 24 – 25 March 2014 Silver Spring MD
Commonly used acceptance criteria (continued)
Goodness of fit
essential AAC & SAC, used almost
universally. R2 common eg. R2≥0.95 to
R2≥0.98, simple but not sensitive
Note: error “R2≤” in publication of draft
Potency of control Assay control sample, full dose response curve, should be in every assay (& every
plate). At present, not always the case.
Typical limits 70-130% to 90-110%.
Commonly based on historical data
Ratio of values for different control samples
Not used commonly
Minimum number of doses used in curve fit
Common AAC & SAC, all doses used or exclusion of a few permitted, resulting in eg.
a minimum of 6 to 10 doses
Minimum number of doses in “linear” part of dose-response curve
Common AAC & SAC, both for linear & full curve fits. Values generally from 3 to 6
Minimum number of doses in upper &/or lower asymptote
Used in some assays, more during development. Most common value is 2
……..continued…….
13/03/2014
7
CASSS Bioassays 24 – 25 March 2014 Silver Spring MD
Commonly used acceptance criteria (continued)
Minimum dose range used in curve
fit
Sometimes explicit AAC & SAC (eg. dose
range 50-150% should lie within the linear
range). Often implicit, by specifying doses
tested & min number of doses in curve fit
Maximum number of statistical
outliers excluded
Common AAC & SAC, defined as eg.
number of doses or individual points per
curve or number of replicates per dose.
Many assays do not allow the exclusion of
any outliers or only if anomaly attributed to
experimental error
Variability of replicates
Essential AAC & SAC. Replicate wells / dilution series / aliquots, analysed from one
plate or across several. Expressed as %CV,
eg. 10 - 30%, or relative 95% confidence
interval around the mean eg. 80-125% or
75-133%.
Assay Acceptance Criteria for Multiwell-
Plate–Based Biological Potency Assays
Draft for Consultation C. Jane Robinson, Michael Sadick, Stanley N. Deming,
Sian Estdale, Svetlana Bergelson and Laureen Little
BioProcess International 12(1) January 2014 pages 30-41
http://www.bioprocessintl.com/journal/2014/January/Assay-
Acceptance-Criteria-for-Multiwell-PlateBased-Biological-Potency-
Assays-349245
( http://www.bioprocessintl.com/ then Archive then Archive by Issue )
or
http://www.bebpa.org/wp-
content/uploads/2012/11/BPI_A_141201AR05_O_231158a.pdf
( www.bebpa.org then White Papers )
CASSS Bioassays 24 – 25 March 2014 Silver Spring MD
Please send your comments on the draft
guidance ……..
Please note:
• Comments should be received by 31 May 2014
• All comments must be accompanied by a name, affiliation & email
address
• All comments will be given due consideration by the authors
• Any comment submitted may be posted on the BEBPA website
www.bebpa.org/white-papers/
• Comments may be posted in their entirety or extracts may be posted. In
the latter case, it will be noted that only part of the comment is posted
• If you would like your name & affiliation to be posted with your
comments, please state that BEBPA has your permission to do so
CASSS Bioassays 24 – 25 March 2014 Silver Spring MD
by email to the Biopharmaceutical Emerging Best Practices Association
at
bebpa@surewest.net starting subject line with: AAC
“Edging Out” Edge Effects in a Cell-based Assay
Shelley Elvington
Genentech, a Member of the Roche Group, South San Francisco, CA USA
For many cell-based assays, long incubations at 37oC can lead to edge effects. One approach to mitigate
these effects is to limit the number of wells used in the assay to the inner 60. While this approach is
effective in avoiding edge effects, it also limits the amount of space available on the plate for use. Here I
present case studies in which alternative assay plates and cell-handling techniques were used to reduce
edge effects. These approaches can be used to alleviate edge effects without reducing the number of
usable wells on the plate, thereby increasing sample throughput and flexibility in plate format.
Slides were not available at the time of printing.
NOTES:
Near-universal Similarity Bounds for Bioassays
David Lansky
Precision Bioassay, Inc., Burlington, VT USA
Equivalence tests for similarity in bioassay are now broadly accepted; setting bounds for the tests is still
considered challenging. Sensitivity analyses illustrate factorial combinations of non-similarity
constructed via scaled parametric shifts and their impact on potency bias. Parameter-specific and
composite measures of similarity are compared for their ability to detect non-similarity that causes
potency bias. A simple combination to supplement scaled parametric measures will control potential
bias from correlated shifts in non-similarity parameters. With this modification we propose a complete
method for setting equivalence bounds informed by assay performance requirements (product
specifications) rather than assay capability.
NOTES:
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
Near-universal similarity boundsfor bioassays
David Lansky, Ph.D.
Burlington, Vermont, USAdavid@precisionbioassay.com
March, 2014
1 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
Abstract
Equivalence tests for similarity in bioassay are now broadly
accepted; setting bounds for the tests is still considered
challenging. Sensitivity analyses illustrate factorial combinations of
non-similarity constructed via scaled parametric shifts and their
impact on potency bias. Parameter-specific and composite
measures of similarity are compared for their ability to detect
non-similarity that causes potency bias. A simple combination to
supplement scaled parametric measures wil control potential bias
from correlated shifts in non-similarity parameters. With this
modification we propose a complete method for setting equivalence
bounds informed by assay performance requirements (product
specifications) rather than assay capability.
2 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
The Problem
I Equivalence (now) non-controversialI Setting equivalence bounds?
I Set so (known) similar samples passI Set so historical pass rate okI Set to match reject rate of difference test
I None recognize or exploit the meaning ofequivalence measures
I Products w/wide therapeutic window haveI wide potency specificationI wider similarity bounds?
3 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
Assumptions and Approach
I Parallel four parameter logistic model
I i.i.d. N(0, σ2) residualsI Examine nonsimilarity and measures via:
I Sensitivity analysis to nonsimilarityI Bias and variance of non-similarity estimatorsI Ease of setting equivalence boundsI Do equivalence bounds protect potency?
5 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
This work in context:
I Assay capability requires limits on potencybias (CP in USP<1033>).
I Sample acceptance criteria should ensuregood (i.e.; unbiased) potency.
I We demonstrate setting similarityequivalence bounds:
I that control bias in potency, andI can be set without data.
7 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
Four Parameter Logistic
y ∗ =Ai
1 + e−Bi (log(x)−Ci )+ Di + ε
A = Response Range, B = ”Slope”, C = Log EC50, and D = No-dose Asymptote
Universal Scaled Similarity Parameters:
I %∆A = 100 × (ATest − ARef)/ A∗Ref
I %∆D = 100 × (DTest − DRef)/ A∗Ref (Not a typo)
I %∆B = 100 × (BTest − BRef)/ B∗Ref
∗ Long term averages
9 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
Scaled Shifts w/consistent meaning
A: Range
B: Slope
D: No-dose Asy.
Black/Magenta pairs are reference/testA and D × (2/3, 1, 3/2) + 10%,B × (1/3, 1, 3) + 50%
11 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
No-dose Asymptote -5% shift
: B { -35 } : A { -5 }
: B { 0 } : A { -5 }
: B { 35 } : A { -5 }
: B { -35 } : A { 0 }
: B { 0 } : A { 0 }
: B { 35 } : A { 0 }
: B { -35 } : A { 5 }
: B { 0 } : A { 5 }
: B { 35 } : A { 5 }
13 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
No-dose Asymptote 0% shift
: B { -35 } : A { -5 }
: B { 0 } : A { -5 }
: B { 35 } : A { -5 }
: B { -35 } : A { 0 }
: B { 0 } : A { 0 }
: B { 35 } : A { 0 }
: B { -35 } : A { 5 }
: B { 0 } : A { 5 }
: B { 35 } : A { 5 }
15 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
No-dose Asymptote +5% shift
{ -35 }{ -5 }
{ 0 }{ -5 }
{ 35 }{ -5 }
{ -35 }{ 0 }
{ 0 }{ 0 }
{ 35 }{ 0 }
{ -35 }{ 5 }
{ 0 }{ 5 }
{ 35 }{ 5 }
17 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
Experience w/”Scaled shift”bounds
I %∆A:B:D Range:Slope:no-dose AsymptoteI Excellent assays cannot pass 5:35:5
equivalence boundsI Noisy assays do not pass 10:50:10
equivalence bounds
I Visual sensitivity analysis:I 5% for no-dose asymptote and range, OK?I 35% slope seems okI Some combinations look non-similiarI Certain combinations likely to induce bias
19 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
Sensitivity Experiment Plan
I CRD DesignI 6 replicates, 2 samples, 10 dilutionsI Residual SD: 1, 3, 6, and 9% of range
I Factorial Sample PropertiesI potency: 1/2, 1, 2I Shift in no-dose asy: -5%, 0, 5%I Shift in range: -5%, 0, 5%I Shift in slope: -35%, 0, 35%
I Performance Measures:I Geometric bias of potencyI % sample failure
I 999 simulated assays per condition
21 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
Percent Geometric Bias of PotencyPGbias of Potency
Slope.delta
Perce
nt
−10
−5
0
5
10
15
−40 −20 0 20 40
11 1
11
1
1
33 3
33 3
33
3
66 6
66 6
66 6
99 9
99 9
99
9
: A.delta { −5 } : D.delta { −5 }
1 1 11 1 111 1
3 3 33 3 333 3
6 6 66
6 66 66
99 9
99 99 9
9
: A.delta { 0 } : D.delta { −5 }
−40 −20 0 20 40
1 1 11 1 11 1 13 3 33 3 33 3 36 6 66 6 66 6 69 9 99 9 99 9 9
: A.delta { 5 } : D.delta { −5 }
1 1 11 111
3 3 33 3 33 3 36 6 66 6 66 6 69 9 99 9 99 9 9
: A.delta { −5 } : D.delta { 0 }
1 1 11 1 11 1 13 3 33 3 33 3 36 6 66 6 66 6 69 9 99 9 99 9 9
: A.delta { 0 } : D.delta { 0 }
−10
−5
0
5
10
15
1 1 11 1 11 1 13 3 33 3 33 3 36 6 66 6 66 6 69 9 99 9 99 9 9
: A.delta { 5 } : D.delta { 0 }
−10
−5
0
5
10
15
1 1 111
1 13 3 333 3
3 3 36 6 66 6 66 6 69 9 99 9 99 9 9
: A.delta { −5 } : D.delta { 5 }
−40 −20 0 20 40
11 1
11
11 1 13
3 33
33
3 3 366 6
66
66
6 699 9
9 99
99 9
: A.delta { 0 } : D.delta { 5 }
11 1
11
1
11 1
33 3
33
3
33 3
66 6
66
6
66 6
99 9
99
9
99 9
: A.delta { 5 } : D.delta { 5 }
Symbols (1, 3, 6, & 9) indicate the residual SD as % of response range
Color indicates sample potency (magenta=0.5, black=1, green=2).
Background color marks bias: Orange: over 20%, Yellow: 10%-20%
23 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
Bias of Scaled Non-sim. w/1% SD
Median universal Range sim.
Slope.delta
Perc
ent
2
4
6
8
−40 −20 0 20 40
1 1 11 11 13 3 33 3 33 3 36 6 66 6 66 6 69 9 99 9 99 9 9
: A.delta { −5 } : D.delta { −5 }
1 1 11 1 11 1 13 3 33 3 33 3 3
6 6 66 6 66 6 6
9 9 99 9 99 9 9
: A.delta { 0 } : D.delta { −5 }
−40 −20 0 20 40
1 1 11 1 11 1 13 3 33 3 33 3 36 6 66 6 66 6 69 9 99 9 9
9 99
: A.delta { 5 } : D.delta { −5 }
1 1 11 11 13 3 33 3 33 3 36 6 66 6 66 6 69 9 99 9 9
9 9 9
: A.delta { −5 } : D.delta { 0 }
1 1 11 1 11 1 13 3 33 3 33 3 3
6 6 66 6 66 6 6
9 9 99 9 99 9 9
: A.delta { 0 } : D.delta { 0 }
2
4
6
8
1 1 11 1 11 1 13 3 33 3 33 3 36 6 66 6 66 6 69 9 99 9
99 99
: A.delta { 5 } : D.delta { 0 }
2
4
6
8
1 1 11 11 13 3 33 3 33 3 36 6 66 6 66 6 69 9 9
9 9 99
9 9
: A.delta { −5 } : D.delta { 5 }
−40 −20 0 20 40
1 1 11 1 11 1 13 3 33 3 33 3 3
6 6 66 6 66 6 6
9 9 99 9 99 9 9
: A.delta { 0 } : D.delta { 5 }
1 1 11 1 11 1 13 3 33 3 33 3 36 6 66 6 66 6 69 9 9
9 99
9 9 9
: A.delta { 5 } : D.delta { 5 }
25 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
Ratio Non-sim. w/1% SDMedian ratio Range sim.
Slope.delta
Perce
nt
5
10
15
20
−40 −20 0 20 40
1 1 11 11 1
3 3 33 3 33 3 3
6 6 66 6 66 6 6
9 9 99 9 99 9 9
: A.delta { −5 } : D.delta { −5 }
1 1 11 1 11 1 1
3 3 33 3 33 3 3
6 6 66 6 66 6 6
9 9 99 9 99 9 9
: A.delta { 0 } : D.delta { −5 }
−40 −20 0 20 40
1 1 11 1 11 1 1
3 3 33 3 33 3 3
6 6 66 6 66 6 6
9 9 99 9 99 9 9
: A.delta { 5 } : D.delta { −5 }
1 1 11 11 1
3 3 33 3 33 3 3
6 6 66 6 66 6 6
9 9 99 9 9
9 9 9
: A.delta { −5 } : D.delta { 0 }
1 1 11 1 11 1 1
3 3 33 3 33 3 3
6 6 66 6 66 6 6
9 9 99 9 99 9 9
: A.delta { 0 } : D.delta { 0 }
5
10
15
20
1 1 11 1 11 1 1
3 3 33 3 33 3 3
6 6 66 6 66 6 6
9 9 99 9 99 9 9
: A.delta { 5 } : D.delta { 0 }
5
10
15
20
1 1 11 11 1
3 3 33 3 33 3 3
6 6 66 6 66 6 6
9 9 99 9 9
9 9 9
: A.delta { −5 } : D.delta { 5 }
−40 −20 0 20 40
1 1 11 1 11 1 1
3 3 33 3 33 3 3
6 6 66 6 66 6 6
9 9 99 9 9
9 9 9
: A.delta { 0 } : D.delta { 5 }
1 1 11 1 11 1 1
3 3 33 3 33 3 3
6 6 66 6 66 6 6
9 9 99 9 99 9 9
: A.delta { 5 } : D.delta { 5 }
%RatioA = 100× ATest/ARef, %RatioD = 100× DTest/ARef,
%RatioB = 100× BTest/BRef
Median ratio non-similarity >2X true value with residual SD=1%.
Ratio estimates more sensitive to residual SD than universal.
27 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
Chi-Square Equivalence Bound?Median Chi−square sim.
Slope.delta
Chi−s
quare
0.0
0.2
0.4
0.6
−40 −20 0 20 40
1
1 1
1
1
1
1
3
3 3
3
3 3
3
3 3
6
6 6
6
6 6
6
66
9
9 9
9
9 9
9
9 9
: A.delta { −5 } : D.delta { −5 }
1
11
1
11
1
11
3
33
3
33
3
33
6
66
6
66
6
66
9
99
9
99
9
99
: A.delta { 0 } : D.delta { −5 }
−40 −20 0 20 40
11
11
111
1
133
33
333
3
366
66
666
6
699
99
999
9
9
: A.delta { 5 } : D.delta { −5 }
1
1 1
1
1
1
1
3
3 3
3
3 3
3
3 3
6
6 6
6
6 6
6
6 6
9
9 9
9
9 9
9
9 9
: A.delta { −5 } : D.delta { 0 }
1
11
1
11
1
11
3
33
3
33
3
33
6
66
6
66
6
66
9
99
9
99
9
99
: A.delta { 0 } : D.delta { 0 }
0.0
0.2
0.4
0.6
11
111
11
1
133
333
33
336
6
666
66
669
9
999
99
99
: A.delta { 5 } : D.delta { 0 }
0.0
0.2
0.4
0.6
1
1 1
1
1
1
1
3
3 3
3
3 3
3
3 3
6
6 6
6
6 6
6
6 6
9
9 9
9
9 9
9
9 9
: A.delta { −5 } : D.delta { 5 }
−40 −20 0 20 40
1
11
1
11
1
11
3
33
3
33
3
33
6
66
6
66
6
66
9
99
9
99
9
99
: A.delta { 0 } : D.delta { 5 }
11
111
1
11
133
333
3
33
366
666
6
66
699
99
9
9
99
9
: A.delta { 5 } : D.delta { 5 }
With one test sample χ2nonsimilarity = (SSRFull − SSRReduced) /3
Estimates not sensitive to residual, sensitive to potency (potential for bias)
29 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
Sample Pass Rate for 10:50:10
Universal pass rate at 10:50:10
Slope.delta (and target potency)
Perc
ent
0
20
40
60
80
100
−40 −20 0 20 40
1 1 11
1
11
1
13 3 33
3
3
3
3
36
6
6
6
6 6
6
6 6
9
9
99 9
9
9 9
9
: A.delta { −5 } : D.delta { −5 }
1 1 11
1
11
1
13 3 33
3
33
3
366
666 6
6
6 6
9
9
999
9
99
9
: A.delta { 0 } : D.delta { −5 }
−40 −20 0 20 40
1 1 1
1
1 1
1
1 13 3 3
3
3
3
3
3
3
66
6
6
6
66
6
6
9
9
99
9
99
9
9
: A.delta { 5 } : D.delta { −5 }
1 1 11
1
11
1
13 3 33
3
3
3
3
36
6
6
6
6 6
6
6 6
9
9
99 9
9
9 9
9
: A.delta { −5 } : D.delta { 0 }
1 1 11
1
11
1
13 3 33
3
33
3
366
666
6
6
6 6
9
9
99
9
9
9 9
9
: A.delta { 0 } : D.delta { 0 }
0
20
40
60
80
1001 1 1
1
1 1
1
1 13 3 3
3
3
3
3
3
3
66
6
6
6
66
6
6
9
9
99
9
99
9
9
: A.delta { 5 } : D.delta { 0 }
0
20
40
60
80
100 1 1 11
1
11
1
13 3 33
3
3
3
3
36
6
6
6
6 6
6
6 6
9
9
99 9
9
9 9
9
: A.delta { −5 } : D.delta { 5 }
−40 −20 0 20 40
1 1 11
1
11
1
13 3 33
3
33
3
366
66
66
66 6
9
9
999
9
99
9
: A.delta { 0 } : D.delta { 5 }
1 1 1
1
1 1
1
1 13 3 3
3
3
3
3
3
3
66
6
6
6
66
6
6
9
9
99
9
99
9
9
: A.delta { 5 } : D.delta { 5 }
31 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
Universal Similarity - Improved
Universal similarity (10:50:10) w/10% bound on A+D
Slope.delta (and target potency)
Perc
ent
0
20
40
60
80
100
−40 −20 0 20 40
1 1 11 1 11 1 13 3 33 3 33 3 36 6 66 6 66 6 69 9 99 9 99 9 9
: A.delta { −5 } : D.delta { −5 }
1 1 11
1
11
1
13 3 33
3
33
3
36
6
666 6
66 6
9
9
99
999
99
: A.delta { 0 } : D.delta { −5 }
−40 −20 0 20 40
1 1 1
1
1 1
1
1 13 3 3
3
3
3
3
3
3
66
6
6
6
66
6
6
9
9
99
9
99
9
9
: A.delta { 5 } : D.delta { −5 }
1 1 11
1
11
1
13 3 33
3
3
3
3
36
6
6
6
6 6
6
6 69
9
99 9
9
9 9
9
: A.delta { −5 } : D.delta { 0 }
1 1 11
1
11
1
13 3 33
3
33
3
366
666
6
6
6 6
9
9
99
9
9
9 9
9
: A.delta { 0 } : D.delta { 0 }
0
20
40
60
80
1001 1 1
1
1 1
1
1 13 3 3
3
3
3
3
3
3
66
6
6
6
66
6
69
9
99
9
99
9
9
: A.delta { 5 } : D.delta { 0 }
0
20
40
60
80
100 1 1 11
1
11
1
13 3 33
3
3
3
3
36
6
6
6
6 6
6
6 6
9
9
99 9
9
9 9
9
: A.delta { −5 } : D.delta { 5 }
−40 −20 0 20 40
1 1 11
1
11
1
13 3 33
3
33
3
36
6
666
6
66 69
9
99 9
9
9 9
9
: A.delta { 0 } : D.delta { 5 }
1 1 11 1 11 1 13 3 33 3 33 3 36 6 66 6 66 6 69 9 99 9 99 9 9
: A.delta { 5 } : D.delta { 5 }
33 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
Adjusted Geometric Total ErrorPercent Geometric Total Error adj. for failure, universal sim. 10:50:10
Slope.delta (and target potency)
PGTEF
0
5
10
15
−40 −20 0 20 40
1
1 1
11 3
3 3
33 6
6 6
9
9 9
: A.delta { −5 } : D.delta { −5 }
11 1
1 11
1
33 3
66 6
9
9 9
: A.delta { 0 } : D.delta { −5 }
−40 −20 0 20 40
1 1 113 3 36 6 69
9 9
: A.delta { 5 } : D.delta { −5 }
1 1 111 33 333 66 6
99
9
: A.delta { −5 } : D.delta { 0 }
1 1 11 11 13 3 36 6 69
99
: A.delta { 0 } : D.delta { 0 }
0
5
10
15
1 13 3 36 6 69
9 9
: A.delta { 5 } : D.delta { 0 }
0
5
10
15
1 1 111 3
3 333 6
6 69
99
: A.delta { −5 } : D.delta { 5 }
−40 −20 0 20 40
11 1
1
1
1 13
3 3
66 6
9
9 9
: A.delta { 0 } : D.delta { 5 }
1
1 1
3
3 3
6
6 6
9
9 9
: A.delta { 5 } : D.delta { 5 }
PGTEF = PGE“RMSTE
“log“R̂”””
= 100 ∗
0BBBBB@2
vuuutBias2
log2
“R̂”+ σ2
log2
“R̂”,
Psimilar
− 1
1CCCCCA ,
where:I Bias
log2
“R̂” = log2
“R̂”− log2 (R),
I R̂ and R are Estimated and True Potency,I Psimilar = Proportion Similar, and
I PGE (RMSTE) = Percent Geometric Expected Root Mean Square Total Error.
35 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
Total Error After Improvement
PGTEF, universal sim. 10:50:10 w/10% bound on A+D
Slope.delta (and target potency)
PGTE
F
0
10
20
30
40
−40 −20 0 20 40
: A.delta { −5 } : D.delta { −5 }
1 1 11 111
3 3 36 6 69 9 9
: A.delta { 0 } : D.delta { −5 }
−40 −20 0 20 40
1 1 113 3 36 6 69 9 9
: A.delta { 5 } : D.delta { −5 }
1 1 111 3 3 333 6 6 69 9 9
: A.delta { −5 } : D.delta { 0 }
1 1 11 11 13 3 36 6 69 9 9
: A.delta { 0 } : D.delta { 0 }
0
10
20
30
40
1 13 3 36 6 69 9 9
: A.delta { 5 } : D.delta { 0 }
0
10
20
30
40
1 1 111 3 3 333 6 6 69 9 9
: A.delta { −5 } : D.delta { 5 }
−40 −20 0 20 40
1 1 111
1 13 3 36 6 69 9 9
: A.delta { 0 } : D.delta { 5 }
9 : A.delta { 5 } : D.delta { 5 }
37 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
Summary & Recommendations
I Use equivalence bounds for similarity
I Use scaled shifts for non-similarity
I Bias limits from assay purpose
I Chi-square (alone) not adequate
I 10:50:10 reasonable starting bounds
I Do limit positively correlated shifts inRange and No-dose Asymptote
39 / 42
Near-universalsimilarity bounds
for bioassays
D. Lansky
Abstract
Introduction
SensitivitySimulation:Experiment Plan
Results
Summary
Acknowledgements
Acknowldgements
I Consulting clients
I USP and USP bioassay panel members
I Carrie Wager
I Ramiro Barrantes
I Mark Blanchard
I NSF EPSCoR
I NIH SBIR 3R44RR02198-03S1
41 / 42
Health Canada Experiences with Bioassay Controls & Control Strategies
Omar Tounekti
BGTD, Health Canada, Ottawa, ON Canada
Bioassays are complicated analytical methods. They are based on living systems and require several
procedural steps and components which make them highly susceptible to variability. The degree of
variability displayed by bioassays directly impacts their ability to generate reliable relative potency
estimates. Although the development of any bioassay involves identifying sources of variability and
minimizing their impact, not all sources of variability can be defined. Moreover, certain sources of
variability, even when minimized, are unavoidable. Therefore, bioassays require a high level of control
in order to ensure that they are operating as per validation.
Typical controls for bioassays include procedural measures and assay design elements, as well as system
and sample suitability criteria. The specific set of controls (control strategy) applied to each bioassay
will depend primarily on assay type, assay knowledge and development data. This presentation will
focus on case studies where the proposed bioassays controls and control strategy were insufficient to
ensure that the bioassay method performed consistently from assay to assay and in agreement with
validated parameters. More specifically, this presentation will provide examples of gaps in the assay
design, system and sample suitability criteria and will highlight how these deficiencies precluded any
reasonable assessment of data contained in the regulatory file.
NOTES:
1
Health Canada Experiences with Bioassay Controls & Control Strategies
Omar Tounekti, PhD Senior Biologist/Evaluator Biologics and Genetic Therapies Directorate Health Canada
Disclaimer
The opinions of this presentation represent the speaker’s experience.
The contents do not necessarily reflect Health Canada official policy.
Biossays 2014 March 24-25, 2014
Presentation Outline
• Introduction
• Overview of biossays
• Bioassays controls and control strategies
• Case studies
• Concluding remarks
Biossays 2014 March 24-25, 2014
2
• Health Canada's BGTD is the Canadian federal authority responsible for the regulation of biological drugs and radiopharmaceuticals for human use.
• Products regulated by BGTD include: • biotherapeutics (cytokines, hormones, enzymes & monoclonal
antibodies)
• radiopharmaceuticals
• cell and genetic therapies
• viral, bacterial and combination vaccines
• blood and blood products
• cells, tissues and organs for transplantation
Biossays 2014 March 24-25, 2014
Organization
ICH Guideline Q6B
• Potency is the quantitative measure of the biological activity using a suitably quantitative biological assay (also called potency bioassay), based on the attribute of the product which is linked to the relevant biological properties.
• A relevant, validated potency assay should be part of the specifications for a biotechnological or biological drug substance and/or drug product.
• Products”
Biossays 2014 March 24-25, 2014
Test method development and the product life cycle
Biossays 2014 March 24-25, 2014
Preclinical
Clinical
Phase I Phase II Phase III
Post-approval
Definition/Design Evaluation Validation Verification
Knowledge
3
Use of Bioassays in the development and production of biologics
Drug candidate selection
Product characterization
Process validation
In-process testing
Lot release testing
Stability testing
Comparability studies
Biossays 2014 March 24-25, 2014
Examples of procedures used to measure biological activity:
•Animal-based biological assays, which measure an organism's biological response to the product.
•Cell culture-based biological assays, which measure biochemical or physiological response at the cellular level.
•Biochemical assays, which measure biological activities such as enzymatic reaction rates or biological responses induced by immunological interactions.
•Other procedures such as ligand and receptor binding assays, may be acceptable.
Biossays 2014 March 24-25, 2014
ICH Guideline Q6B
Bioassays are indirect (i.e. comparative) quantitative procedures: Bioassays are complex test systems that are susceptible to
many variables.
The performance of bioassays (and hence their biological readouts) can vary from day to day and especially from laboratory to laboratory.
The response of a bioassay system to a test material can not be used by itself to assign an absolute potency value.
The biological response of a test material is measured relative to that of a reference preparation.
Biossays 2014 March 24-25, 2014
Bioassays for Biotherapeutics
4
Relative potency assessment
Biossays 2014 March 24-25, 2014
• Bioassays can exhibit a greater variability than do chemically-based tests due to their reliance on biological substrates (e.g. animals, living cells, or functional complexes of target receptors).
• Inherent variability from instruments, reagents, day-to day, lab-to-lab variation.
• Intrinsic variability inherent in manufacturing biologicals
• Relative potency methodology
Biossays 2014 March 24-25, 2014
Why potency assay controls are critical?
1. Assay design
2. System suitability
3. Sample suitability
Biossays 2014 March 24-25, 2014
Potency assay controls & control strategies
5
Assay design:
• While assay development should be focused primarily on the properties of potency efforts to identify and control variation in the concentration-response relationship are also appropriate.
• Example of factors that may affect bioassay response: cell thawing; plating density and confluence; culture vessels; growth, staging, and assay media; serum requirements; incubation conditions (temperature, CO2, humidity, culture times from thaw); cell harvesting reagents and techniques; cell sorting; cell counting; determination of cell health; cell passage number and passaging schedule; cell line stability; and starvation or stimulation steps.
Biossays 2014 March 24-25, 2014
USP <1032> Design and Development of Biological Assays
System suitability:
• System suitability in bioassay, as in other analytical methods, consists of pre-specified criteria by which the validity of an assay (or perhaps a run containing several assays) is assessed.
• Analysts can assess system suitability by determining that some of the parameters of the Standard response are in their usual ranges and that some properties (e.g., residual variation) of all the data are in their usual range.
Biossays 2014 March 24-25, 2014
USP <1032> Design and Development of Biological Assays
Sample suitability:
• Sample suitability in bioassays generally consists of the assessment of similarity, which can only be done within the assay range.
• Relative potency may be reported only from samples that both show similarity to standard, exhibit requisite quality of model fit, and have been diluted to yield an EC50 (and potency) within the range of the assay system.
Biossays 2014 March 24-25, 2014
USP <1032> Design and Development of Biological Assays
6
Bioassay controls and control strategy are insufficient to
ensure that the method is performing consistently from
assay to assay and in agreement with validated
parameters and precludes any reasonable assessment
of the data contained in the submission/file. More
specifically:
• Gaps in the assay design
• Inadequate system suitability
• Inadequate sample suitability
Biossays 2014 March 24-25, 2014
Recurrent issue related to bioassay controls & control strategies
1. Parallelism (similarity)
2. Assay control sample
3. Replicate variability
4. Parameters of the reference standard response
5. Assay design
Biossays 2014 March 24-25, 2014
Case studies involving bioassay controls & control strategies
• Questionable release, stability and comparability data
provided in the file.
• Request of additional information
• Influence product ‘s Lot Release categorisation
• May lead to the issuance of a Notice of Deficiency
• Sponsor is typically requested to provide an action plan
and an impact assessment of applying new criteria to the
data supplied in the original file.
Biossays 2014 March 24-25, 2014
Consequences of inadequate controls and control strategies
7
• Bioassay results are key at all stages in the product life
cycle, from early research work to final quality control of
finished products.
• Assay choice and design are crucial. Oversights result
often in assays that are difficult to perform, time
consuming and that generate questionable data.
Biossays 2014 March 24-25, 2014
Concluding Remarks
Acknowledgments
Dr Maria Baca-Estrada
Dr Evangelos Bakopanos
Biossays 2014 March 24-25, 2014
Thank you
Bioassay Controls & Control Strategies Workshop
PANEL DISCUSSION – Questions and Answers
Shelley Elvington, Genentech, a Member of the Roche Group, USA
David Lansky, Precision Bioassays, Inc., USA
Tsai-Lien Lin, CBER, FDA, USA
Thomas Anders Millward, Novartis Pharma AG, Switzerland
C. Jane Robinson, NIBSC, United Kingdom
Omar Tounekti, BGTD, Health Canada, Canada
Questions to be discussed:
What aspects of a bioassay are controlled and how is this accomplished?
How and when are system and sample suitability (acceptance) criteria set? When are they
revised?
How and when are statistical requirements set?
What methods are used to monitor trends in system or sample suitability (acceptance) criteria?
How can bioassay controls be used to identify problems?
How can bioassay controls be used to troubleshoot problems?
What happens to bioassay controls following assay improvements?
NOTES:
NOTES:
Poster Abstracts
P-01
Optimization of Assay Design and Data Analysis for a Competitive Antibody-antigen Binding
Assay with Higher Variability
Wei Zhang; Deepthi Kanuparthi; Svetlana Bergelson
Biogen Idec, Cambridge, MA USA
We have developed a competitive binding assay to measure the binding of an antibody to its antigen.
Due to the existence of different forms of the antigen and the different binding affinities of the antibody
to them, this assay showed high variability compared with similar assays. In order to develop an assay
that can accurately measure the antibody-antigen binding, we tested different assay formats, conditions,
plate layouts and data analysis methods. In the end, we overcame the higher variability and developed an
electrochemiluminescent (ECL) competitive binding assay on the Meso Scale Discovery (MSD)
platform for the antibody with satisfactory accuracy, precision, linearity and specificity by (1) using a
plate layout with four independent data blocks; (2) plotting curves with data from two replicates; (3)
transforming raw data with square root; (4) using F-test for curve parallelism test.
NOTES:
P-02
Successful Transfer of a Cell Based Potency Assay for a Biological Product
Evelyn Kilareski1; Anthony Burkholder
1; Weihong Wang
1; Robert Donatelli
1; Miriam Franchini
2; Larry
Anderson2
1Eurofins Lancaster Laboratories, Inc., Lancaster, PA USA;
2Smith & Nephew, Inc., London, United
Kingdom
A cell based potency assay measures the physiological response elicited by a given product. It is often
the preferred format for determining the activity of biological products and is commonly employed for
lot release as well as stability testing. Transfer of a cell based potency assay between laboratories can
pose significant challenges due to the complexity of the assay. For a marketed product, method transfer
is under direct scrutiny by regulatory agencies and therefore is an even more significant undertaking.
This poster presents a case study to demonstrate how a cell-based potency assay can be successfully
transferred to a third party contract testing laboratory.
NOTES:
P-03
A Reporter Gene Bioassay for Potency Assessment of a Therapeutic Monoclonal Antibody
Tianmeng Shao; Hyun Jun Kim; Xu-Rong Jiang; Michael Washabaugh
MedImmune, Gaithersburg, MD USA
We developed and optimized a novel reporter gene bioassay for quantifying the bioactivity of a
therapeutic monoclonal antibody. The antibody blocks the binding of a ligand on the antigen-presenting
cells to a receptor on T cells. The inhibition will sustain T cell activation and T cell immune function by
blocking the negative regulatory signals generated by the binding of the ligand to its receptor. A Nuclear
Factor Kappa-light-chain-enhancer of activated B cells (NFB) cell line has been engineered that
expressed both the receptor and an NFB-luciferase reporter gene. The reporter gene assay can measure
NFB activity in T cell that is proportional to T cell activation. Test samples caused a concentration-
dependent induction of NFB activity in T cells that is measured relative to a reference standard. The
current bioassay format is highly robust, simple to perform, and amenable for use in a regulated
environment.
NOTES:
P-04
Better Cell-Based Assays for Anti-CTLA-4, Anti-PD1/PD-L1, and Bispecific Immunotherapy
Drug Studies
Mei Cong; Pete Stecha; Natasha Karassina; Jim Hartnett; Zhi-Jie Jey Cheng; Frank Fan
Promega Corporation, Madison, WI USA
Cancer immunotherapy was named the 2013 “Breakthrough of the Year” by Science. It aims to stimulate
a patient’s own immune system to treat cancer. Key inhibitor drugs in the market or under clinical
investigation such as nivolumab and lambrolizumab (anti-Programmed cell Death protein 1 [PD1]
antibodies), and ipilimumab (Yervoy, an anti-Cytotoxic T-Lymphocyte Antigen 4 (CTLA-4) antibody)
are providing long-term benefits to cancer patients. Catumaxomab, a bispecific and bifunctional
antibody fusion protein, is another unique way to engage T cells for cancer therapy by allowing targeted
delivery and activation of immunoregulatory signaling pathways. Cytokines and interleukins also belong
to this category by nonspecifically stimulating T cell proliferation. Traditional inhibitory blockade
studies commonly use animal models or freshly isolated peripheral blood mononuclear cells (PBMCs) to
quantify antibody drug biological activity. However, animal models can be cost-prohibitive and cell
acquisition and preparation is labor intensive and the bioassays incorporating such cells have high
inherent variability. Here we demonstrate multiple bioluminescent reporter-based assays that can be
used to rapidly measure potencies of multiple biological immunotherapy drugs. We also demonstrate
that these bioassays reflect mode of action of each drug, and quantify potencies of on-market
monoclonal antibody drugs for cancer.
NOTES:
P-05
Characterization of FcRn Binding Kinetics of Therapeutic Antibodies
LeeAnn Machiesky; Kenneth R. Miller; Xu-Rong Jiang; Michael Washabaugh
MedImmune, Gaithersburg, MD USA
The neonatal Fc receptor (FcRn) recycles IgGs within endothelial cells and rescues them from a
degradative pathway thus increasing antibody half-life. A binding assay has been developed using label-
free surface plasmon resonance (SPR) technology to assess the binding affinity and kinetics for the
interaction between human FcRn and IgG molecules. FcRn - IgG binding kinetics are fit using a
heterogeneous ligand model, which assumes there are two classes of non-interacting sites on FcRn. At
lower IgG concentrations, the higher affinity site is occupied, and at higher IgG concentrations the lower
affinity sites are also occupied. This model generates both a high and low affinity binding constant (KD1
and KD2) for the interaction. We evaluated the binding affinity and kinetics for several different IgG
molecules, across multiple manufacturing lots, including ones which had been engineered to have
enhanced FcRn binding. There was no observable difference in the association rate constant for any of
the IgG molecules; however, an observable difference was measured in the dissociation rate constant
(kd), with IgG molecules that were engineered to have enhanced FcRn binding, having a 10-fold lower
kd value.
NOTES:
P-06
Establishment of an ADCC Screening/Characterization Assay and a CDC Assay for Anti-
TNFalpha Therapeutic Antibodies (e.g. Humira®)
Frances Brauer; Manuela Schmid; Alexander Knorre
BSL BIOSERVICE Scientific Laboratories GmbH, Planegg / Munich, Germany
Cytotoxicity is a mechanism of action of antibodies through which virus-infected or other diseased cells
can be killed, either by components of the cell-mediated immune system (ADCC, Antibody-dependent
cell-mediated cytotoxicity) or by the complement system (CDC, complement-dependent cytotoxicity).
An ADCC surrogate assay using Promega’s ADCC Reporter Assay and antibody-specific target cells
was established for use as screening/characterization assay. Potency determination is performed using 4
parameter logistic fit. This assay can be used for anti-TNFalpha therapeutic antibodies, e.g. Humira
(Adalimumab), Enbrel (Etanercept) and Remicade (Infliximab). The anti-TNFalpha therapeutic antibody
CIMZIA (Certolizumab pegol) which lacks the ADCC-inducing Fc part was used as negative control.
Assay dose response curves with continuously-cultured or with frozen, thaw-and-use target cells will be
shown. In addition, a CDC assay with luminescence readout and human serum as complement source
for anti-TNFalpha therapeutic antibodies was established. A representative dose response curve with 4
parameter logistic fit analysis will be shown.
NOTES:
P-07
Automation of a Bioassay in QC Laboratories for Routine Lot Release and Stability Testing
Lichun Huang; Ariel Margulis; Ai Shih; Wei-Meng Zhao; Parth Sampathkumar; Joseph Marhoul
Genentech, a Member of the Roche Group, South San Francisco, CA USA
As part of continuous improvement efforts, an automated method to determine clot lysis activity has
been developed and validated to replace the plate based manual method. The ACL TOP system is a
fully automated, stand-alone random-access multiparameter coagulation analyzer. Using this system,
the potency method fully utilizes the automated pre-dilution feature and data generated are exported to
enable parallel line analysis for potency calculation. The automated assay passes tightened accuracy
(recovery of 95-105%) and precision (inter-assay CV of <5%) criteria. A high level of inter-instrument
precision allows the instruments to be used interchangeably. The stability indicating properties are
confirmed using a panel of samples stressed under various conditions. The design of comparability is
based on two one-sided tests (TOST) with a criterion of 95% confidence interval of mean difference
between methods falling within +/- 1.7% of the mean. Comparability testing performed using 44
samples that include representative samples from lot release and stability program, stability retains that
past normal shelf-life, stress panel, and high concentration samples to simulate hyper-activities confirms
equivalence between the automated and plate based manual methods. The transfer of the assay to QC
laboratory is evaluated using data tested at recipient laboratory to data obtained at the donor laboratory
in the comparable timeframe. Since there was limited history for the Clot Lysis by ACL TOP assay at
the time of transfer, the maximum acceptable difference between the laboratories was conservatively
established as the 99% confidence limit of the validation SD. Since the implementation and transfer,
method monitoring data showed a tightened control trend with 100% success rate.
NOTES:
P-08
Bioassay Design and Analysis Strategies
David Lansky
Precision Bioassay, Inc., Burlington, VT USA
Bioassay designs are constrained by practical considerations, statistical principles, and variation in
biological materials. Good design, lab technique, and analyses combine to yield high performance
bioassays. Simulation experiments with properties that mimic real bioassays illustrate the impact of
strategic design and analysis choices on bioassay performance over useful ranges of potency and
variation (both within and between assays). This presentation illustrates good designs and analysis
strategies for slope ratio, parallel line, and parallel four parameter logistic curve bioassays. Each of these
strategic approaches has advantages and disadvantages; these are compared.
NOTES:
P-09
Near-universal Similarity Bounds for Bioassays
David Lansky
Precision Bioassay, Inc., Burlington, VT USA
Several versions of composite and parameter-specific measures of non-similarity will be described and
compared. Two approaches to sensitivity analyses yield new ways to examine the potential impact of
various amounts of non-similarity and offer a good rationale for nearly universal similarity equivalence
acceptance limits (scaled parametric shifts). Factorial combinations of various amounts of parametric
non-similarity are used in simulations to compare different candidate measures of non-similarity.
Candidate non-similarity measures are compared using median simulated sample failure rates and
median geometric bias of potency. Scaled parametric equivalence bounds require a modification to
control potential bias from correlated shifts in non-similarity parameters; with this modification we
propose a complete method for setting equivalence bounds informed by assay performance requirements
(product specifications) rather than assay capability.
NOTES:
NOTES:
Recommended