Using Statistical
Innovation to Impact
Regulatory Thinking
Harry Yang, Ph.D.
Senior Director, Head of Non-Clinical
Biostatistics
MedImmune, LLC
2014 Nonclinical Biostatistics
Conference
What Roles Are We Playing in Regulatory Affairs?
2
What Roles Are We Playing in Regulatory Affairs?
 To think?
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What Roles Are We Playing in Regulatory Affairs?
 To rule the world?
4
What Roles Are We Playing in Regulatory Affairs?
 Or to influence?
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The Answer Is…
 TO INFLUENCE!
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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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Areas Where Statistics Is Value-added
 Design of experiment (DOE)
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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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Too Many to Choose
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How to Reduce Variability?
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Should You Use Control?
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Should You Be Blinded?
 To reduce evaluator’s bias
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Should You Randomize?
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How to Minimize Chance of False Claim?
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How to Maximize Probability of Success?
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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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How To Analyze Data with High Accuracy,
Precision and Confidence?
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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
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Help Overcome Regulatory Hurdles
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Be Bold and Innovative
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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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04/14/2008 – 6:00pm
Bridging FFA and TCID50 Assays
 CRL Question: FFA and TCID50 are different assays but both used
for clinical trial material release
Theoretical mean difference
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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
SF 
Om :
I0 :
m:
M:
E[U]:
Om
.
m
I0
E[U ]
M
Amount of oncogenes to induce an event
Number of oncogenes in host genome
Average oncogene size
Host genome size
Expected amount of residual host DNA/dose
Safety Factor per FDA-recommended Method
 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
Om (ng)
OS
GS
I0
hcDNA (ng)
Safety Factor
9400*
1950
2.41E+09
1
1
1.16E+10
* Oncogenic dose derived from mouse
Oncogenic risk is overstated
 The denominator includes amount of fragmented oncogene DNA
Safety Factor
Om
(OS / GS) I 0 (hcDNA)
Amount of
oncogene DNA
in final dose
=
Amount of
unfragmented
oncogene DNA
in final dose
+
Amount of
fragmented
oncogene DNA
in 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?
Enzymatic degradation
of DNA
Source: http://1.bp.blogspot.com/_vgEA7CHGLe8/SzIAZHWs-vI/AAAAAAAAAVc/vZcmDlRlxSY/s320/miracle.gif
DNA Inactivation
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Model of DNA Inactivation Process
Safety Factor Based on Probabilistic Modeling
(Yang et al., 2010)
 Safety factor
Amount of oncogenes required
for inducing an oncogenic event
SF 
Om
I0

i 1
(1  p)
mi 1
mi
E[U ]
M
.
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
B1cB2c...cBX
Length X, random variable
 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 =

log 2
log(1  p)
Safety Factor
 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
Om (ng)
Oncogene size
MDCK genome size
Median
hcDNA (ng)
9400
1950
2.41E+09
450
1
Safety Factor
2.34E+11
Oncogenic Risk Comparison
 FDA method overestimates oncogenic risk by 19-fold
 Reducing residual DNA with median size of 450 bp is adequate to
mitigate oncogenic risk
Our Method
FDA Method
Om (ng)
Oncogene size
MDCK genome size
I0
hcDNA (ng)
Safety Factor
9400*
1950
2.41E+09
1
1
1.16E+10
Om (ng)
Oncogene size
MDCK genome size
Median
hcDNA (ng)
Safety Factor
9400
1950
2.41E+09
450
1
2.34E+11
Establishing Pre-filtration Bioburden Test Limit
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Manufacture of a Sterile Drug Product
 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
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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 prefiltration test
 Breakthrough of bioburden through the final sterile filter
 Both types of risk can be characterized thru probabilities of
occurrence
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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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Risk of Bio-burden Breakthrough in Final Solution
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Determination of Pre-filtration Sample Volume and
Batch Size
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Maximum Batch Sizes Based on Risks and Prefiltration Test Schemes
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A Few Additional Thoughts
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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
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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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Q&A
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