Regulatory Challenges in Bioassay Practices

1. Regulatory Challenges in Bioassay Practices Tim Schofield Managing Director & Head of Non-Clinical Services Arlenda, Inc. tim.schofield.arlenda.com Presented at the 35th Annual Midwest Biopharmaceutical Statistics May 22, 2012, Ball State University, Muncie, Indiana

2. Outline • What is a bioassay? • Bioassay guidelines • Regional differences in bioassay practices • US viewpoint on bioassay practices • QbD for analytical methods

3. What is a bioassay?ICH definition and requirements • Bioassay (Biological Assay) – ICH Q6B (paraphrased) • Definition: The measure of the biological activity using a suitably quantitative biological assay (also called potency assay or bioassay), based on the attribute of the product which is linked to the relevant biological properties. • A valid biological assay to measure activity should be provided by the manufacturer. Examples of procedures used to measure biological activity include: • 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

4. What is a bioassay?FDA requirements • Potency definition • 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. [21 CFR §600.3 (s)] • Tests for potency • 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. [21 CFR § 610.10]

5. What is a bioassay?Distinguishing properties • Measurement of activity rather than mass • Specific to the mechanism of action • Usually reported as relative potency to a reference standard • Highly variable • 10% to 50% RSD (versus 1% to 2% RSD for chromatographic assays) • Biology rather than chemistry • Resource intensive • Time as well as materials • Usually performed on samples in a complex matrix • Therapeutic proteins purified from cell culture • Vaccines produced in living systems

6. Bioassay guidelines • Originally USP <111> and EP 5.3 • <111> was split into two chapters, USP <1032> Design and Development of Biological Assays and USP <1034> Analysis of Biological Assays • <1033> Biological Assay Validation added to the suite “Roadmap” chapter (to include glossary) 6

7. Bioassay guidelines (cont.) • All but chapter <111> are above 1000 and therefore “informational” • Not intended as enforceable (as chapters below 1000) • However, the chapters provide a set of guiding principles which might be considered by regulators in their reviews • Chapter <111> is left to support monographs which reference it • USP is working towards addressing product-specific references to prepare it for further revision 7

8. Regional differences in bioassay practices • Assessing “linearity” and similarity • “Linearity” is the goodness-of-fit to the processing model • Similarity is the equivalence of the bioassay model parameters • Parallelism in parallel line analysis • Equivalence of asymptotes and Hill coefficient in 4 parameter logistic regression • Equivalence of intercepts in slope ratio analysis • Parallel line/curve versus slope ratio analysis • For some vaccines the US requires parallel line analysis while the EU requires slope ratio analysis • Design and performance characteristics are different for the two approaches • Clinical specifications versus process consistency • Growing expectation in the US that potency specifications should be supported by clinical studies

9. Regional differences in bioassay practicesAssessing “linearity” and similarity • Significance testing versus equivalence testing p = 0.08 (p > 0.05, i.e., not significantly different) Conclude parallel! Rewarded for poor assay performance p = 0.02 (p < 0.05, i.e., significantly different) Conclude nonparallel! Penalized for good assay performance

10. Regional differences in bioassay practicesAssessing “linearity” and similarity (cont.) LAL LAL • Paradox: significance tests reward poor work and penalize good work • The greater the precision in the data, the more likely you will fail the significance test • Solution – use an equivalence test • Determine an acceptable range in a metric related to “linearity” or similarity (LAL,UAL) • Demonstrate (TOST) that there’s acceptable similarity • CI includes 0 = no evidence of a difference • CI within (LAL,UAL) = similar a: no evidence of a difference in slopes; however, possibly outside acceptance limit (not similar) b: no evidence of a difference in slopes; but inside acceptance limit (similar) c: a difference in slopes; however, within acceptance limit (similar) UAL 0 N=4 N=6 N=8 a a b b c c Note: Rewarded for good work 10

11. Regional differences in bioassay practicesAssessing “linearity” and similarity (cont.) • Hurdles to an equivalence approach • European regulation adheres to EP 5.3 • The revision of EP 5.3 does not allow for other approaches • Some points of view • “Statistical significance is scientifically important” • The significance approach can be moderated by an examination of the results • Use of historical variability • Moderation of significance level • May lead to subjectivity • “Calibration” to the equivalence approach • Engineer significance approach to duplicate equivalence approach • More straight forward to apply equivalence approach • Establishment of an equivalence margin 11

12. Regional differences in bioassay practicesAssessing “linearity” and similarity (cont.) • Approaches for assigning an equivalence margin – operational approaches • Approach 1 – based on “process capability” of the bioassay • Addresses only the producer’s risk • There is no penalty for a poorly designed bioassay • Approach 2 – based on “process capability” using a tolerance interval on the confidence interval • Penalizes poor assay runs - - 12

13. Regional differences in bioassay practicesAssessing “linearity” and similarity (cont.) • Approaches for assigning an equivalence margin – quality approaches • Approach 3 – based on discrimination between “good” and “bad” behavior • Protects both producer’s and consumer’s risk • However, how do you define/generate “bad” behavior • Some sensitivity to clinical correspondence to “bad” behavior • Approach 4 – ad hoc limits • Based on product or assay knowledge • Should consider potential impact to product quality 13

14. Regional differences in bioassay practicesBioassay design and data processing (cont.) • There are currently regional differences in the accepted processing of some vaccines (flu) • EU requires slope ratio while North America requires parallel line analysis • In slope ratio analysis variability of RP is impacted by increased variability of influential regression points • Slope is influenced by extreme points • Simulation results • True relative potency equal to 2.00 • Impact of increase in assay %RSD • n=100 simulated assays per condition • Little impact on relative potency determination with increase in assay %RSD • Dramatic increase in RP %RSD for slope ratio assay • Assay design should be adapted to parallel line approach (geometric doses)

15. US viewpoint on bioassay practicesValidation of assay format • Some US regulators believe that bioassay validation should be a verification of the procedure for obtaining a “reportable value” • Groupings in time have different variability characteristics than the sum of the variance components • Emphasis on product release ignores other uses • Some merit to this if the validation is not designed to address the issue of short term versus long term variability • Replicate the bioassay under the same set of ruggedness conditions

16. Managing variabilityBeyond random and systematic variability (cont.) • Risk of truncation error and range • Retest rules have the potential to lead to truncation bias in the reportable value • e.g., retest when a measurement is outside the “quantifiable range” of the bioassay • Potential solutions • Assign a value to the low/high result (e.g., ½ the LLOQ in clinical assays) • Demonstrate a range which supports low/high potency samples (without retest) • Retest the series using an adjusted dilution scheme Lot 1 Lot 2 Test Avg Lot 3 Retest Lot 4 Lot 5 16

17. US viewpoint on bioassay practicesBioassay characterization • USP allows for “Use of validation results for bioassay characterization” • Use of variance components to adapt bioassay format • Numbers of runs (assays) and replicates to efficiently manage bioassay variability • Identify potentially significant sources of variability • Update technique or training • Replicate over significant factors

18. QbD for analytical methods • Industry has begun to recognize that analytical methods generate a product – measurements • Like pharmaceutical products, measurements should have adequate quality to meet their intended use – decision making • The fundamental goals of product development are: • Safety and efficacy (hitting the clinical target) • Variance reduction • The fundamental goals of analytical development are: • Accuracy (hitting the analytical target) • Variance reduction 18

19. QbD for analytical methods (cont.) • Many of the concepts associated with QbD for pharmaceutical products translate to concepts related to analytical methods 19

20. QbD for analytical methods (cont.) • The bioassay should be fit for its intended use throughout the bioassay lifecycle • Should perform adequately to support decisions • Decisions are made day-to-day using bioassays • During development • Which formulation provides the best stability? • Does a particular process step impact potency? • What is the self-life of the product? • During manufacture • Should a manufactured lot be released to the market? • Should a process change be implemented? • Has a manufactured lot maintained potency over it shelf-life? • Can a new potency standard be used in the bioassay? 20

21. QbD for analytical methods (cont.) • All decision are made with risk, and risk is costly • Risks during development • Risk of deciding “the process” is suboptimal when it is fit– producers risk – results in excessive development costs or program failure • Risk of deciding the process is fit when “the process” is flawed – consumers risk – results in excess downstream costs to fix the problems • Risks during manufacture • Risk of failing satisfactory product –regulatory burden and lost revenues • Risk of passing unsatisfactory product – potential risk to the “customer” • Decision risk can be managed in several ways • Use sound scientific reasoning and/or experience to guide decisions • When decisions are made on the basis of empirical evidence, decision risk is associated with the strength of the evidence 21

22. QbD for analytical methods (cont.) • Statistical opportunities – supporting evidence based development and control • Statistical thinking • Understanding variability • Managing variability • Communicating uncertainty • Bioassay models and analyses • Bioassay optimization • Bioassay maintenance • Statistical process control • Standard qualification/calibration • Method transfer • Method comparison – in vivo to in vitro 22

23. Summary • Bioassays are utilized throughout the biological product lifecycle to make key development and quality decisions • Statistical approaches facilitate decision making and help mitigate the risks of bioassay variability • Industry and regulatory statisticians should work together to support bioassay development, and should help promote best practices in implementation