2014 Nonclinical Biostatistics Conference

Using Statistical Innovation to Impact Regulatory ThinkingHarry Yang, Ph.D.

Senior Director, Head of Non-Clinical Biostatistics

MedImmune, LLC

2

What Roles Are We Playing in Regulatory Affairs?

3

What Roles Are We Playing in Regulatory Affairs?

To think?

4

What Roles Are We Playing in Regulatory Affairs?

To rule the world?

5

What Roles Are We Playing in Regulatory Affairs?

Or to influence?

6

The Answer Is…

TO INFLUENCE!

7

How Do We Influence Regulatory Thinking?

8

An Old Tried and True Method

Throw statisticians at the deep end of regulatory interactions

9

An Old Tried and True Method (Cont’d)

Throw statisticians at the deep end of regulatory interactions

– Low success rate

– Lost potential/opportunities

10

A More Effective Approach to Influencing Regulatory Thinking

Identify opportunities

Understand our own strengths

Influence thru collaboration

Opportunities

11

Areas Where Statistics Is Value-added

Design of experiment (DOE)

12

Statistical Designs

Completely randomized designs

Randomized complete block designs

Split-plot designs

Cross-over designs

Latin square designs

Factorial designs

Analysis of variance designs

13

Too Many to Choose

14

How to Reduce Variability?

15

Should You Use Control?

16

Should You Be Blinded?

To reduce evaluator’s bias

17

Should You Randomize?

18

How to Minimize Chance of False Claim?

19

How to Maximize Probability of Success?

20

Did You Use the Right Sample Size N?

A small N may miss biologically important effects

A large N wastes animals

21

Facts Science

“A collection of facts is no more a science than a heap of stones is a house.”

Henri Poincare (1854 – 1912)

22

How To Analyze Data with High Accuracy, Precision and Confidence?

23

Which Model to Choose?

Analysis of variance (ANOVA)

Regression analysis

Repeated measurement analysis

Survival analysis

Meta-analysis

Mixed effect modeling

Non-parametric analysis

24

Help Overcome Regulatory Hurdles

25

Be Bold and Innovative

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

26

27 04/14/2008 – 6:00pm

28

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

Anxiety Attack

The Scream, by Edvard Munch, 1893

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

=+

DNA Inactivation

Enzymatic Degradation Inactivates DNA

Benzonase and other ingredients

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

Burden of Proof

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

DNA Inactivation

40

Model of DNA Inactivation Process 

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

Establishing Pre-filtration Bioburden Test Limit

48

Manufacture of a Sterile Drug Product

49

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.

50

Measures to Mitigate Bioburden Risk

Pre-filtration testing

Filtration

Minimization of manufacturing hold times between process steps

Utilization of refrigerated storage for intermediates

51

52

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%

53

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

54

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

55

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

56

Risk Associated with Three Different Test Schemes

57

20 CFU32 CFU

63 CFU

5%

Mitigating Risk of Larger Number of Bioburden thru Sterial Filtration

58

Sterile Filtration

59

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)

60

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.

61

Risk of Bio-burden Breakthrough in Final Solution

62

Determination of Pre-filtration Sample Volume and Batch Size

63

Maximum Batch Sizes Based on Risks and Pre-filtration Test Schemes

64

A Few Additional Thoughts

65

66

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)

66

Form Consortiums to Develop White/Concept Papers

A-Mab: a Case Study in Bioprocess Development

A-Vax: Applying Quality by Design to Vaccines

67

68

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

69

Regularly Communicate with Regulatory Authorities

70

71

Conduct Joint Training

72

Q&A