Transcript Document

Using Statistical
Innovation to Impact
Regulatory Thinking
Harry Yang, Ph.D.
MedImmune, LLC
May 20, 2014
How Do We Influence Regulatory
Thinking?
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An Old Tried and True Method
 Throw statisticians at the deep end of regulatory interactions
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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
Opportunities
 Understand our own strengths
 Influence thru
collaboration
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Three Case Examples
 Acceptable limits of residual host cell DNA
 Risk-based pre-filtration limits
 Bridging assays as opposed to clinical studies
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Acceptable Residual DNA Limits
 Biological product contains residual DNA from host cell
 Residual DNA could encode or harbor oncogenes and infectious
agents
 Mitigate oncogenic and infective risk thru restriction on DNA amount
per dose and size
 WHO and FDA guidelines recommend
– Amount ≤ 10 ng/dose
– Size ≤ 200 base pairs (bp)
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Safety Factor
 Safety factor (Pedan, et al., 2006)
– Number of doses taken to induce an oncogenic or infective event
Om
SF 
.
mi
I0
E[U ]
M
Om :
I0 :
mi:
M:
E[U]:
Amount of oncogenes to induce an event
Number of oncogenes in host genome
Oncogene sizes
Host genome size
Expected amount of residual hose DNA/dose
Revised Safety Factor (Lewis et al., 2001)
Om
SF 
.
m
P * I 0 i E[U ]
M
Om :
I0 :
mi:
M:
E[U]:
P:
Amount of oncogenes to induce an event
Number of oncogenes in host genome
Oncogene sizes
Host genome size
Expected amount of residual hose DNA/dose
Percent of DNA with size ≥ oncogene size
DNA Inactivation
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Relationship between Enzyme Cutting Efficiency
and Median DNA Size (Yang, et al., 2010)
Probability of enzyme cutting thru two adjacent
nucleotides, p, and median DNA size Med satisfy
p  1 2

1
Med
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Safety Factor Based on Probabilistic Modeling
(Yang et al., 2010)
I0 :
mi:
M:
Med0:
E[U]:
Number of oncogenes in host genome
Oncogene sizes
Host genome size
Median residual DNA size
Expected amount of residual hose DNA/dose
Method Comparison
 Theoretically it can be shown FDA methods either over- or underestimate safety factor (Yang, 2013)
Risk-based Specifications
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DNA Content and Size Can Be Outside of
Regulatory Limits without Compromising Safety!
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Establishing Pre-filtration Bioburden Test Limit
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EMA Guidance (2008): Notes for Guidance on
Manufacture of Finished Dosage Form
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EMA Guidance (2008): Notes for Guidance on
Manufacture of Finished Dosage Form
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Risk Associated with Three Different Test Schemes
5%
20 CFU
63 CFU
32 CFU
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Mitigating Risk of Larger Number of Bioburden thru
Sterial Filtration
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Sterile Filtration
 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)
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Upper Bound of Probability p0 for a CFU to Go
Thru Sterile Filter (Yang, et al., 2013)
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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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Maximum Batch Sizes Based on Risks and Prefiltration Test Schemes
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Bridging Assays as Opposed to Clinical Studies
 FFA and TCID50 are different assays but both used for clinical trial
material release (Yang, et al., 2006)
Theoretical mean difference
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Other Ways to Influence Regulatory Thinking
 Serve on committees such as USP Statistics Expert, CMC Working
Groups, Industry Consortiums
 Organize joint meetings/conferences/workshops
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USP 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)
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Parallelism Testing
 Significance vs. equivalence test (Hauck et al., 2005)
 Feasibility of implementation (Yang et al., 2012)
 Method comparison based on ROC analysis (Yang and Zhang, 2012)
 Bayesian solution (Novick, Yang, and Peterson, 2012)
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Testing Assay Linearity
 Directly testing linearity
(Novick and Yang, 2013)
 Testing linearity over a prespecified range (Yang, Novick,
and LeBlond, 2014)
 The method is being
considered to be included in a
new USP chapter on statistical
tools for method validation
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A Few Additional Thoughts
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Conduct Innovative Statistical Research on
Regulatory Issues
 Solutions based on published
methods are more likely
accepted by regulatory
agencies
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Take a Good Statistical Lead in Resolving
Regulatory Issues
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Regularly Communicate with Regulatory
Authorities
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Conduct Joint Training
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References
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H. Yang, S.J. Novick, and D. LeBlond. (2014). Testing linearity over a pre-specified range. Submitted.

H. Yang, N. Li and S. Chang. (2013). A risk-based approach to setting sterile filtration bioburden limits. PDA J. of Pharm.
Science and Technology. Vol. 67: 601-609
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D. LeBlond, C. Tan and H. Yang (2013). Confirmation of analytical method calibration linearity. May – June Issue,
Pharmacopeia Forum. 39(3).
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D. LeBlond, C. Tan and H. Yang. (2013). Confirmation of analytical method calibration linearity: practical application.
September - October Issue. Pharmacopeia Forum
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S. Novick and H. Yang. (2013). Directly testing the linearity assumption for assay validation. Journal of Chemometrics. DOI:
10.1002/cem.2500
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H. Yang. Establishing acceptable limits of residual DNA (2013). PDA J. of Pharm. Sci. and Technol., March – April Issue.
67:155-163
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S. Novick, H. Yang and J. Peterson. A Bayesian approach to parallelism testing (2012). Statistics in Biopharmaceutical
Research. Vol. 4, Issue 4, 357-374.
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H. Yang, J. Kim, L. Zhang, R. Strouse, M. Schenerman, and X. Jiang. (2012). Parallelism testing of 4-parameter logistic
curves for bioassay. PDA J. of Pharm. Sci. and Technol. May-June Issue, 262-269.

H. Yang and L. Zhang. Evaluations of parallelism test methods using ROC analysis (2012). Statistics in Biopharmaceutical
Research. Volume 4, Issue 2, p 162-173
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H. Yang, L. Zhang and M. Galinski. (2010). A probabilistic model for risk assessment of residual host cell DNA in biological
product. Vaccine 28 3308-3311

H. Yang and I. Cho. (2006) Theoretical Relationship between a Direct and Indirect Potency Assays for Biological Product of
Live Virus. Proceedings of 2006 JSM.
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Q&A
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