may 20, 2014 using statistical innovation to impact regulatory thinking harry yang, ph.d. medimmune,...

May 20, 2014

Using Statistical Innovation to Impact Regulatory Thinking

Harry Yang, Ph.D.

MedImmune, LLC

2 04/14/2008 – 6:00pm

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

Understand our own strengths

Influence thru collaboration

Opportunities

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

.][0 UE

M

mI

OSF

i

m

Om: Amount of oncogenes to induce an eventI0: Number of oncogenes in host genomemi: Oncogene sizesM: Host genome sizeE[U]: Expected amount of residual hose DNA/dose

Revised Safety Factor (Lewis et al., 2001)

.][* 0 UE

M

mIP

OSF

i

m

Om: Amount of oncogenes to induce an eventI0: Number of oncogenes in host genomemi: Oncogene sizesM: Host genome sizeE[U]: Expected amount of residual hose DNA/doseP: 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)

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Medp1

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Probability of enzyme cutting thru two adjacent nucleotides, p, and median DNA size Med satisfy

Safety Factor Based on Probabilistic Modeling (Yang et al., 2010)

I0: Number of oncogenes in host genomemi: Oncogene sizesM: Host genome sizeMed0: Median residual DNA sizeE[U]: Expected amount of residual hose DNA/dose

Method Comparison

Theoretically it can be shown FDA methods either over- or under- estimate 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

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20 CFU32 CFU

63 CFU

5%

Mitigating Risk of Larger Number of Bioburden thru Sterial Filtration

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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 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 Pre-filtration 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)

Testing Assay Linearity

Directly testing linearity (Novick and Yang, 2013)

Testing linearity over a pre-specified 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

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

References 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

D. LeBlond, C. Tan and H. Yang (2013). Confirmation of analytical method calibration linearity. May – June Issue, Pharmacopeia Forum. 39(3).

D. LeBlond, C. Tan and H. Yang. (2013). Confirmation of analytical method calibration linearity: practical application. September - October Issue. Pharmacopeia Forum

S. Novick and H. Yang. (2013). Directly testing the linearity assumption for assay validation. Journal of Chemometrics. DOI: 10.1002/cem.2500

H. Yang. Establishing acceptable limits of residual DNA (2013). PDA J. of Pharm. Sci. and Technol., March – April Issue. 67:155-163

S. Novick, H. Yang and J. Peterson. A Bayesian approach to parallelism testing (2012). Statistics in Biopharmaceutical Research. Vol. 4, Issue 4, 357-374.

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

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