©2004 Millennium Pharmaceuticals, Inc. © 2004 Millennium Pharmaceuticals Inc. Opportunities and...

©2004 Millennium Pharmaceuticals, Inc.© 2004 Millennium Pharmaceuticals Inc.

Opportunities and Challenges in Utilizing Biomarkers for Drug Development

Mark Chang, Ph.D.

Director, BiostatisticsMillennium Pharmaceuticals, Inc. USA

Sept. 27-29, 2006, Washington, D.C. USA

©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


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.


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


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% 1800

* 800 subjects for screening.


Impact of Screening Testing

•Target patient size with biomarker (+):

N = N+ Sensitivity + N- (1-Specificity)

•Treatment effect for diagnostic biomarker (+) patients:

∆ = [∆+N+Sensitivity + ∆-N-(1-Specificity)] / N

•Definition of utility:

Utility = ∆ N Power

Feasibility of diagnostic/screening testing:

Cost, safety, regulatory requirements0.0 0.2 0.4 0.6 0.8 1.0

Utility (fixed power)

Effects of Biomarker Misclassification

Treatment Effect

Traget population size

Utility (Fixed N)

Specificity


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.


Treatment-Biomarker-Endpoint Three-Way Relationship

True-endpoint YE

BiomarkerYB

RTE = 0

Treatment X

RBE = 0.90R TB =

0.45

Pearson’s Correlation

Regression: YT = YB – 2 X


Correlation Versus Prediction

0 1 2 3 4 5 6 7 8 9 100

1

2

3

4

5

6

7

8

9

10

Res

pons

e in

true

end

poin

t

Response in biomarker

Control Test

•R between marker and endpoint in Test =1

•R in Control =1

•R in Test + Control = 0.9

•Endpoint response in Test = 4•Endpoint response in Control = 4

•Biomarker response in Test = 6•Biomarker response in Control = 4

Note: R = Pearson’s correlation


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


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?


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


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 patients have shorter survival time than 1st line

patients, and 3rd line patients have shorter survival time than 2nd 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?– Aren’t there enough evidences out there to show that 2nd line

cancer patients survive longer than 3rd line patients?– Is the statistical evidence the only evidence acceptable?


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).


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


Scenarios with Biomarkers

• Same effective size for biomarker and true- endpoint, 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.


What is the utility of partially validated biomarkers?


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


Adaptive Design Using Biomarkers

• Futility design

• Interim analysis with biomarker

• Final analysis with true-endpoint

• Correlated endpoints


Prior Knowledge about Treatment Effect

Scenario Effect Size Ratio*

Prior Probability

Ho10/0 0.2

Ho20/0.25 0.1

Ha0.5/0.5 0.7

*The ratio of the effect size of the true endpoint at final to the effect size of the biomarker at interim analysis


Comparison of Various Designs in Different Scenarios

Design Scenario Power Expected N/arm

Early Stopping boundary

Classic Ho1

Ho2

Ha 0.89

70

116

145

Seamless phase II/III Adaptive

Design using Biomarker

Ho1

Ho2

Ha 0.94

75

95

100Z = 0.0 (p=0.5)

Ho1

Ho2

Ha 0.89

58

80

98Z = 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.


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.


Bayesian Optimization

Design Classic Integrated phase II/III design

using biomarkerZ = 0 Z = 1.0

Expected Utility $56M $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.


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?


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


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.


References

1. Molenberghs G., Buyse, M. and Burzykowski, T. The history of surrogate endpoint validation, in The evaluation of surrogate endpoint, Burzykowski, Molenberghs, and Buyse (eds.) 2005. Springer.

2. Chakravarty, A. (2005), Regulatory aspects in using surrogate markers in clinical trials. in The evaluation of surrogate endpoint, Burzykowski, Molenberghs, and Buyse (eds.) 2005. Springer.

3. Fleming, T.R. and Demets, D.L. (1996) Surrogate endpoint in clinical trials: are we being misled? Annals of internal medicine, 125, 605-613.

4. Buyse, M. et. al. Statistical validation of surrogate endpoint. Drug Information Journal, 34, 49-67 & 447-454.5. Freedman, L.S. (1992) Statistical validation of intermediate endpoints for chronic diseases. Statistics in Medicine, 11, 167-

178.6. Chang, M. Bayesian Adaptive Design Method with Biomarkers, Biopharmaceutical Report, Summer 2006. p.7-11.7. Simon, R. Adaptive Signature Design, Clin Cancer Res 2005; 11(21). Nov. 1, 2005.8. Alonso, A., et al. (2006), A unifying approach for surrogate marker validation based on Prentices’ criteria. Stat. In med.

25:205-2219. Weir, C.J. and Walley, R.J. (2006) Statistical evaluation of biomarkers as endpoints: a literature review. Stat. In med. 25:183-

20310. Qu, Y. and Case, M. (2006). Quantifying the indirect effect via surrogate markers. Stat. In med. 25:223-23111. Biomarkers Definitions Working Group Bethesda, Md. Biomarkers and surrogate endpoints: Preferred definitions and

conceptual framework. CLINICAL PHARMACOLOGY & THERAPEUTICS (2001).12. Kevin Carroll. Biomarkers in Drug Development: Friend or Foe? Biopharmaceutical Report, Summer 2006. p.3-6.13. Chang, M. (2006). Improving the Efficiency of drug development using Bayesian approaches. Int J Pharmaceutical

Medicine. Submitted.14. Chang, M. (2006). Analysis and Modeling of Clinical Trial with Adaptive Witching. Conference on Analysis of Latent Variables

in Health Science. Sept. 6-8, 2006, Perugia Italy.15. Shao, J., Chang, M., and Chow, S.C. (2005). Statistical inference for cancer trials with treatment switching. Statistics in

Medicine, 24, 1783-1790. 16. Chang, M, Chow, S.C. & Pong, A. (2006). Adaptive design in clinical research: issues, opportunities, and recommendations.

Journal of Biopharmaceutical Statistics, 16: 299–309, 2006


Breakthrough science. Breakthrough medicine.SM

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