2014 nonclinical biostatistics conference using statistical innovation to impact regulatory thinking...
TRANSCRIPT
2014 Nonclinical Biostatistics Conference
Using Statistical Innovation to Impact Regulatory ThinkingHarry Yang, Ph.D.
Senior Director, Head of Non-Clinical Biostatistics
MedImmune, LLC
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An Old Tried and True Method (Cont’d)
Throw statisticians at the deep end of regulatory interactions
– Low success rate
– Lost potential/opportunities
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A More Effective Approach to Influencing Regulatory Thinking
Identify opportunities
Understand our own strengths
Influence thru collaboration
Opportunities
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Statistical Designs
Completely randomized designs
Randomized complete block designs
Split-plot designs
Cross-over designs
Latin square designs
Factorial designs
Analysis of variance designs
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Did You Use the Right Sample Size N?
A small N may miss biologically important effects
A large N wastes animals
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Facts Science
“A collection of facts is no more a science than a heap of stones is a house.”
Henri Poincare (1854 – 1912)
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Which Model to Choose?
Analysis of variance (ANOVA)
Regression analysis
Repeated measurement analysis
Survival analysis
Meta-analysis
Mixed effect modeling
Non-parametric analysis
Four Case Examples
Widening specification after OOS
Bridging assays as opposed to clinical studies
Acceptable limits of residual host cell DNA
Risk-based pre-filtration bio-burden limits
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Bridging FFA and TCID50 Assays
CRL Question: FFA and TCID50 are different assays but both used for clinical trial material release
Theoretical mean difference
Acceptable Residual DNA Limits: The Problem
The product under evaluation contains a significant amount of residual host cell DNA greater than 500 bp in length.
This may increase the risks of oncogenicity and infectivity of host cell DNA.
Regulatory guidance requires the median size of residual DNA be 200 bp or smaller
Our process can only achieve a median size of 450 bp
Safety Factor
Safety factor (Pedan, et al., 2006)
– Number of doses taken to induce an oncogenic or infective event
.
][0 UEM
mI
OSF m
Om: Amount of oncogenes to induce an eventI0: Number of oncogenes in host genomem: Average oncogene sizeM: Host genome sizeE[U]: Expected amount of residual host DNA/dose
Safety Factor per FDA-recommended Method
Om (ng) 9400*OS 1950GS 2.41E+09I0 1hcDNA (ng) 1Safety Factor 1.16E+10
* Oncogenic dose derived from mouse
If cellular DNA contained an active oncogene it would take 11.6 billion doses to cause an oncogenic event
– If 250 million doses of vaccines are used annually, in less than 46.4 years one oncogenic event may be observed
Oncogenic risk is overstated
The denominator includes amount of fragmented oncogene DNA
)()/( 0 hcDNAIGSOS
OFactorSafety m Amount of
oncogene DNAin final dose
Amount of unfragmentedoncogene DNA
in final dose
Amount of fragmented
oncogene DNAin final dose
=+
Hope
This finding gives us hope that with median residual DNA size of 450 bp (albeit not quite up to the regulatory bar of 200 bp) perhaps the oncogenicity and infectivity risks are already reduced to an acceptable level.
Negotiation with FDA
Standard method overestimates risk
If DNA inactivation step is incorporated in the calculation, the risk might be adequately mitigated
How to Incorporate DNA Inactivation in the Risk Assessment?
Source: http://1.bp.blogspot.com/_vgEA7CHGLe8/SzIAZHWs-vI/AAAAAAAAAVc/vZcmDlRlxSY/s320/miracle.gif
Enzymatic degradationof DNA
Safety Factor Based on Probabilistic Modeling (Yang et al., 2010)
Safety factor
.
][)1( 1
1
0
UEM
mp
OSF
imI
i
m
i
Amount of oncogenes required for inducing an oncogenic event
Expected amount of unfragmented oncogenes in a dose
Proof of the Theoretical Result
Trust me!
How to estimate enzyme cutting efficiency p?
Modeling Length of DNA Segment
After enzyme digestion, any DNA segment takes the form
Let p denote the probability for enzyme to cleave bond c. Thus X has properties
– Represents number of trials until the first cut
– Follows a geometric distribution with parameter p,
• Prob[X=k]=(1-p)k-1p
• Median =
XcBccBB ...21
Length X, random variable
)1log(
2log
p
Safety Factor
Om (ng) 9400Oncogene size 1950MDCK genome size 2.41E+09Median 450hcDNA (ng) 1
Safety Factor 2.34E+11
If cellular DNA contained an active oncogene it would take 234 billion doses to deliver the oncogenic dose used in the mouse studies
– If 250 million doses of vaccines are used annually, it will take approximately 883 years for one oncogenic event to occur
Oncogenic Risk Comparison
Om (ng) 9400Oncogene size 1950MDCK genome size 2.41E+09Median 450hcDNA (ng) 1Safety Factor 2.34E+11
Om (ng) 9400*Oncogene size 1950MDCK genome size 2.41E+09I0 1hcDNA (ng) 1Safety Factor 1.16E+10
FDA Method Our Method
FDA method overestimates oncogenic risk by 19-fold
Reducing residual DNA with median size of 450 bp is adequate to mitigate oncogenic risk
Manufacture of a Sterile Drug Product
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Microbial control during manufacturing is critical for ensuring product quality and safety.
Sterile biologic drug products (finished dosage forms) are typically manufactured by sterile filtration followed by aseptic filling and processing.
Control of microbial load at the sterile filtration step is an essential and required component of the overall microbial control strategy.
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Measures to Mitigate Bioburden Risk
Pre-filtration testing
Filtration
Minimization of manufacturing hold times between process steps
Utilization of refrigerated storage for intermediates
Potential Limitations of EMA-Recommended Bioburden Limit, 10 CFU/100 mL
The limit has no scientific and statistical justifications
It protects neither consumer’s nor producer’s risk
– Probability of rejecting a batch with 9 CFU/100 mL = 33.4%
– Probability of accepting a batch with 11 CFU/100 mL = 50%
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Additional Limitations of 10 CFU/100 mL Bioburden Limit
It does not take into account assay variability and the fact that microorganisms are not homogeneously distributed
Meeting or failing 10 CFU/100 mL acceptance limit may not provide adequate assurance that the true biobruden level is below or above 10 CFU/100 mL
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A Risk-based Approach to Development of Bioburden Control and Pre-filtration Testing Strategy
Driven by product and process knowledge
Identification of types of risks, their associations with testing method and process parameters
Development of control strategy
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Two Types of Risk Associated with Sterile Filtration Process
Drug solution with an unacceptable bioburden level passes the pre-filtration test
Breakthrough of bioburden through the final sterile filter
Both types of risk can be characterized thru probabilities of occurrence
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Sterile Filtration
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FDA guidance requires that filters used for the final filtration should be validated to reproducibly remove microorganisms from a carrier solution containing bioburden of a high concentration of at least 107
CFU/cm2 of effective filter area (EFA)
Upper Bound of Probability of Having at least 1 CFU in Final Filtered Solution
It’s a function of batch size S, pre-filtration test volume V, and the maximum bioburden level D0 of the pre-filtration solution
By choosing the batch size, this probability can be bounded by a pre-specified small number δ.
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Actively Involve in Standard Setting
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)
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Form Consortiums to Develop White/Concept Papers
A-Mab: a Case Study in Bioprocess Development
A-Vax: Applying Quality by Design to Vaccines
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Conduct Innovative Statistical Research on Regulatory Issues
Solutions based on published methods are more likely accepted by regulatory agencies