Regulatory Challenges in Bioassay Practices
Tim SchofieldManaging 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
Outline
What is a bioassay?
Bioassay guidelines
Regional differences in bioassay practices
US viewpoint on bioassay practices
QbD for analytical methods
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
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]
What is a bioassay?FDA requirements
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
What is a bioassay?Distinguishing properties
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
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
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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
Regional differences in bioassay practicesAssessing “linearity” and similarity
Significance testing versus equivalence testing
Laboratory A
-1.2
-0.8
-0.4
0
0.4
0.8
0.5 1 1.5 2 2.5
Log10 Concentration
Log1
0 R
espo
nse
Standard DataTest DataStandard LineTest Line
Laboratory B
-1.2
-0.8
-0.4
0
0.4
0.8
0.5 1 1.5 2 2.5
Log10 ConcentrationLo
g10
Res
pons
e
Standard DataTest DataStandard LineTest Line
p = 0.02 (p < 0.05, i.e., significantly different)
Conclude nonparallel!
Penalized for good assay performance
p = 0.08 (p > 0.05, i.e., not significantly different)
Conclude parallel!
Rewarded for poor assay performance
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 differencein slopes; however, possibly outsideacceptance 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)
a b c
0
a b c
LALLAL
Note: Rewarded for good work
N=6 N=8N=4
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Regional differences in bioassay practicesAssessing “linearity” and similarity (cont.)
UAL
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
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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
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Regional differences in bioassay practicesAssessing “linearity” and similarity (cont.)
Diffe
renc
e of
Slo
pes
Approach 1
Diffe
renc
e of
Slo
pes
Approach 2 -
-
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
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Regional differences in bioassay practicesAssessing “linearity” and similarity (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
0
2
4
6
8
10
12
14
0 1 2 3 4 5
Slope Ratio Analysis
Standard
Test
Assay%RSD
1% 1.99 (4%) 1.99 (2%)10% 2.06 (45%) 2.02 (19%)20% 1.95 (159%) 2.21 (48%)
Slope Ratio Parallel LineEstimated RP (%RSD)
• 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)
Regional differences in bioassay practicesBioassay design and data processing (cont.)
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
2.8
3
3.2
3.4
3.6
3.8
4
4.2
0 3 6 9 12 15 18 21 24Time (Month)
Regression
0
50
100
150
200
1 10 100 1000
Res
pons
e
Concentration
Dilutional Linearity 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
Managing variabilityBeyond random and systematic variability (cont.)
16
Test
Avg
Retest
Lot 1
Lot 5
Lot 2
Lot 3
Lot 4
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
% 1e100yVariabilit ormatF knˆ
kˆ 2
Replicate2Run
Format variability for different combinations of number of runs (k) and number of minimal sets within run (n)
Number of Runs (k)
Reps (n) 1 2 3 6
1 7.2% 5.1% 4.1% 2.9%
2 6.4% 4.5% 3.6% 2.6%
3 6.0% 4.2% 3.4% 2.4%
6 5.7% 4.0% 3.3% 2.3%
ComponentVariance Estimate
Var(Media Lot) 0.0000Var(Analyst) 0.0014
Var(Analyst*Media LOt) 0.0000
Var(Run(Analyst*Media Lot)) 0.0019Var(Error) 0.0022
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
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QbD for analytical methods (cont.)
Many of the concepts associated with QbD for pharmaceutical products translate to concepts related to analytical methods
Process Concept Analytical Counterpart
Target Product Profile (TPP)•Target clinical performance, manufacturing, and commercial requirements
Analytical Target profile (ATP)•Target analytical performance, testing laboratory, and customer requirements
Critical Quality Attributes(CQAs)•Potency•Aggregation•Purity
Performance attributes (validation parameters)•Precision•Sensitivity•Accuracy
Specifications (acceptance criteria)•80% to 125% potency•Purity > 95%
Acceptance criteria•%GCV < 10%•LLOQ > 1 ng/mL
Critical process parameters•pH, time, temperature
Critical assay parameters•pH, time, temperature
Process control strategy•Comparability protocols•Tech transfer
Assay control strategy•Comparability protocols•Method transfer
Continuous verification•Continuous review and updating of process knowledge
Continuous verification•Continuous review and updating of analytical knowledge 19
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?
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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
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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
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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
