Transcript Opportunities and Challenges in Utilizing Biomarkers for Drug Development Mark Chang, Ph.D. Director, Biostatistics Millennium Pharmaceuticals, Inc.

Opportunities and Challenges in
Utilizing Biomarkers for Drug
Development
Mark Chang, Ph.D.
Director, Biostatistics
Millennium Pharmaceuticals, Inc. USA
Sept. 27-29, 2006, Washington, D.C. USA
© 2004 Millennium
Pharmaceuticals
Inc.
©2004
Millennium Pharmaceuticals,
Inc.
What to Cover
• Opportunities of Enrichment Strategies
with Biomarkers
• Prognostic and Predictive Biomarkers
• Challenges in Biomarker Validations
• Adaptive Design using Biomarkers
• Optimization using Bayesian Utility
Theory
• Summary & Discussion
©2004 Millennium Pharmaceuticals, Inc.
Biomarker, Surrogate and Clinical
Endpoint
• Biomarker:
– A characteristic that is objectively measured and evaluated as an
indicator of normal biological processes, pathogenic processes,
or pharmacologic responses to a therapeutic intervention
(Biomarkers Definitions Working Group ,2001)
• Surrogate:
– A biomarker that is intended to substitute for a clinical endpoint. A
surrogate endpoint is expected to predict clinical benefit (or harm
or lack of benefit or harm) based on epidemiologic, therapeutic,
pathophysiologic, or other scientific evidence.
• True/Clinical Endpoint
– A characteristic or variable that reflects how a patient feels,
functions, or survives.
©2004 Millennium Pharmaceuticals, Inc.
Why Biomarkers
• Compared to a gold standard endpoint, such as survival, a biomarker
can often have following characteristics:
– Being measured earlier, easier, and more frequently
– Less subject to competing risks, less affected by other treatment
modalities
– A larger effect size
• The utilization of biomarker could lead to:
– Better target population
– Larger effect size
– Smaller sample size
– Faster decision-making
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Enrichment Strategies with a Biomarker
Population
Size
Response
(Treatment A)
Response
(Treatment B)
Sample size
(90% power )
Biomarker
(+)
10M
50%
25%
160*
Biomarker
(-)
40M
30%
25%
Total
50M
34%
25%
* 800 subjects for screening.
©2004 Millennium Pharmaceuticals, Inc.
1800
Impact of Screening Testing
•Target patient size with biomarker (+):
Effects of Biomarker Misclassification
N = N+ Sensitivity + N- (1-Specificity)
•Treatment effect for diagnostic biomarker (+)
patients:
Traget population size
Utility (fixed power)
∆ = [∆+N+Sensitivity + ∆-N-(1-Specificity)] / N
•Definition of utility:
Utility (Fixed N)
Utility = ∆ N Power
Feasibility of diagnostic/screening testing:
Cost, safety, regulatory requirements
Treatment Effect
0.0
0.2
0.4
0.6
0.8
1.0
Specificity
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Prognostic and Predictive Markers
• A prognostic marker informs the clinical
outcomes, independent of treatment.
– NSCLC patients receiving EGFR inhibitors or
chemotherapy => better outcome with a mutation
than without a mutation.
• A predictive biomarker informs the treatment
effect on the clinical endpoint.
– Predictive marker can be population-specific: a
marker can be predictive for population A but not
population B.
©2004 Millennium Pharmaceuticals, Inc.
Treatment-Biomarker-Endpoint
Three-Way Relationship
Biomarker
YB
Treatment
X
RTE = 0
True-endpoint
YE
Pearson’s Correlation
Regression: YT = YB – 2 X
©2004 Millennium Pharmaceuticals, Inc.
Correlation Versus Prediction
10
9
8
Response in true endpoint
•R between marker and endpoint in Test =1
Control
Test
•R in Control =1
•R in Test + Control = 0.9
7
6
5
•Endpoint response in Test = 4
•Endpoint response in Control = 4
4
3
•Biomarker response in Test = 6
•Biomarker response in Control = 4
2
1
0
0
1
2
3
4
5
6
7
8
9
10
Response in biomarker
Note: R = Pearson’s correlation
©2004 Millennium Pharmaceuticals, Inc.
The Regression “Flaw” In Prediction
YT = YB – 2 X
• R² = 1, p-values for model and all parameters = 0,
where the 2 is the separation between the two
lines.
• =>False conclusions:
– The first model is perfect based on model-fitting p-value
and R².
– Both biomarker and treatment affect the true- endpoint.
• Correlation => Prognostic marker
• Correction ≠> Predictive marker
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Multiplicity and False Positive Rate
• Often the same biomarker or compound has
been studied by different companies without
adjustment for multiplicity.
• The unadjusted-alpha used in biomarker
discovery leads to a high false positive rate
• Publication Bias
– A publication of negative findings could save a
large amount of resources and time for the
development.
• Solution: Validation?
©2004 Millennium Pharmaceuticals, Inc.
Validation of Biomarkers
• Prentice's operational criteria
– for binary surrogate (Molenberghs, 2005, Alonso, 2006)
• Proportion of treatment effect on true endpoint
explained by biomarker
– a large proportion required
(Freedman, Graubard & Schatzkin, 1992)
• Internal validation metrics
– Relative Effect
– Adjusted Association
(Buyse & Molenberghs, 1998)
• External validation
– Meta-analysis
• Two-stage validation for fast track program
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Is the Statistical Evidence the Only
Evidence Acceptable?
• Oncology physicians consider PD as a sign of
treatment failure and will provide an alternative
treatment to the patient when PD is observed.
• It is generally accepted that PD will reduce the
expected survival time.
– 2nd line cancer rdpatients have shorter survival time than 1st line
patients,
and 3 line patients have shorter survival time than
nd
2 line patients.
• Is either of the above facts an evidence to prove
Time to PD is a surrogate for survival?
– Do you trust oncology physicians in general?
nd line
– Aren’t there enough evidences out there
to
show
that
2
cancer patients survive longer than 3rd line patients?
– Is the statistical evidence the only evidence acceptable?
©2004 Millennium Pharmaceuticals, Inc.
Latent Survival Analysis with Treatment
Switching
• What to Compare?
• Survival time is latent due to switching
– More effective drug => more patients switching
– ITT analysis could failed=> Dramatically inflate type-I error
• Statistical methods:
– Statistical inference for trials with treatment switching (Shao,
Chang & Chow, 2003).
– Mixed exponential model for trial with treatment switching
(Chang, 2006)
– Mixture of Wiener Models (Brownian motions) for adaptive
treatment switching (Chang, Lee & Whitmore, 2006).
©2004 Millennium Pharmaceuticals, Inc.
Biomarkers in Reality
• Sample size is often insufficient for validation
• A biomarker is often not validated adequately
• Precision of prediction of treatment effect on
true-endpoint is lower using biomarkers
• Soft validation scientifically (e.g., pathway,
physicians’ overall options) is important
©2004 Millennium Pharmaceuticals, Inc.
Scenarios with Biomarkers
• Same effective size for biomarker and trueendpoint, but biomarker response is earlier
• Bigger effective size for biomarker and
smaller for true endpoint
• No treatment effect on true endpoint; limited
treatment effect on biomarker
• Treatment effect on true endpoint only occurs
after biomarker response reaches a
threshold.
• A probability is associated with each of the
above scenarios.
©2004 Millennium Pharmaceuticals, Inc.
What is the utility of partially
validated biomarkers?
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Adaptive Design Using Biomarkers
• An adaptive design is a design that
allows modifications to some aspects
of the trial after its initiation without
undermining the validity and integrity
of the trial.
• Adaptive design using biomarker:
–
–
–
–
Response-adaptive randomization
Drop-loser/Adaptive dose selection design
Sample size re-estimation
Adaptive target population
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Adaptive Design Using Biomarkers
•
•
•
•
Futility design
Interim analysis with biomarker
Final analysis with true-endpoint
Correlated endpoints
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Prior Knowledge about Treatment Effect
Scenario
Ho1
Effect Size
Ratio*
0/0
Prior
Probability
0.2
Ho2
0/0.25
0.1
Ha
0.5/0.5
0.7
*The ratio of the effect size of the true endpoint at final to the effect size of the
biomarker at interim analysis
©2004 Millennium Pharmaceuticals, Inc.
Comparison of Various Designs
in Different Scenarios
Design
Scenario
Classic
Ho1
Ho2
Ha
Ho1
Ho2
Ha
Ho1
Ho2
Ha
Seamless
phase II/III
Adaptive
Design using
Biomarker
Power
0.89
0.94
0.89
Expected
N/arm
70
116
145
75
95
100
58
80
98
Early Stopping
boundary
Z = 0.0 (p=0.5)
Z = 1.0
(p=0.15)
Note: Correlation coefficient r = 0.5. One-sided Alpha = 0.025. Maximum N per group = 100. Interim
analysis performed at info-time = 0.5. 20,000 simulation runs per scenario. In the classic design one-sided
alpha = 0.2 for phase II and 0.025 for phase III. Sample size = 50/group and 100/group. Z = test statistic with
standard normal distribution.
©2004 Millennium Pharmaceuticals, Inc.
Bayesian Decision Theory
for Optimizing Adaptive Design
• Many different scenarios of reality with
associated probabilities (prior distribution)
• Many possible adaptive designs with associated
probabilistic outcomes (good and bad)
• Evaluation Criteria: Utility
• Bayesian optimal design = maximum expected
utility under financial, time, regulatory, and other
constraints
– For each design, calculate the utility for each design
and weighted by its prior probability to obtain the
expected utility for the design. The optimal design is
the one with maximum utility.
©2004 Millennium Pharmaceuticals, Inc.
Bayesian Optimization
Design
Classic
Expected Utility
$56M
Integrated phase II/III design
using biomarker
Z=0
Z = 1.0
$61M
$58M
•Assumptions:
Per-patient cost in the trial = $50k
Value of approval before deducting the trial cost = $100M
Time savings are not included in the calculation.
©2004 Millennium Pharmaceuticals, Inc.
Different Perspectives about Utility of A
Drug: Benefit-Risk Ratio (BRR)
• Patient:
– The BRR applied to Me.
• Investigator:
– The BRR applied to my patients
• Regulatory Body:
– The BRR shown by the patients in the pivotal studies.
• Sponsor:
– The BRR for patients in the trials, future patients, and potential
benefits for patients with other diseases.
• Common ground: personalized medicine?
©2004 Millennium Pharmaceuticals, Inc.
Personalized Medicine Requires Some
Fundamental Changes in Drug
Development World
• Things worked before may not work in
the new century.
• Alpha-requirement does not control
ineffective drugs into market effectively.
• Philosophical differences between Asian
and Western countries in drug
development
• Rules of Bayesian Approaches
©2004 Millennium Pharmaceuticals, Inc.
Summary and Discussion
• Biomarkers provide tremendous
opportunities, and challenges in drug
development
• Adaptive design using biomarkers can be
beneficial even when they are not fully
validated.
©2004 Millennium Pharmaceuticals, Inc.
References
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in Health Science. Sept. 6-8, 2006, Perugia Italy.
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