Biomarkers & Personalized Medicine: Practical Considerations for Drug Development

1. Biomarkers & Personalized Medicine:Practical Considerations for Drug Development Dominic G. Spinella, Ph.D. Pfizer Oncology Translational Medicine Head

2. Some Definitions • Biological Marker (Biomarker):A characteristic that is measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention. Biomarkers may relate to efficacy, safety, differentiation etc. • Diagnostic:A biomarker that has applicability in clinical use or patient management (e.g. to identify a sub-population of patients who would benefit most from a drug or suffer adverse events from a drug). • Surrogate Endpoint:A biomarker accepted by regulatory agencies as a substitute for a clinical endpoint (e.g. HIV load for the stage of AIDS, LDL level for the risk of coronary artery disease, blood pressure for the incidence of stroke & hemoglobin A1C for the control of diabetes).

3. Some Definitions • Personalized Medicine:“Use of new methods of molecular analysis to better manage a patient’s disease or predisposition towards a disease. It aims to achieve optimal medical outcomes by helping physicians and patients choose the disease management approaches likely to work best in the context of that patient’s genetic and environmental profile” (from the Personalized Medicine Coalition) • Molecular signature:A constellation of several or many discrete molecular characteristics (DNA, RNA or Protein) that collectively constitute a biomarker of disease or drug response • Hypothesis-dependent marker/signature:A biomarker or potential biomarker that is derived a priori from intrinsic understanding of a disease or pathogenic process. • Hypothesis-independent marker/signature:A biomarker that is derived post-hoc using –omics analysis of biologic samples from patients with different phenotypes (e.g. drug responder vs. non-responder)

4. The Hope (Hype?) of Personalized Medicine • Understanding the molecular basis of disease: Which therapy or combination of therapies to use • Defining molecular changes or markers associated with disease progression, response to treatment and relapse: When to treat with a particular regime. • Identifying markers associated with safety & toleration: Choosing the safest therapies and correct dose. • Identifying the right population for clinical trials • efficacy may only be evident in a subset of patients, rather than being uniform across the whole population • Rescue a “failed” drug • Better understand the molecular characteristics of responsive vs. non-responsive patients

5. Examples of PM using Biomarkersin Current Drug Labels Adapted from: Frueh et al (2008) Pharmacogenomic biomarker information in drug labels approved by the United States Food and Drug Administration: prevalence of related drug use. Pharmacother, 28:992-8

6. Most current PM Biomarkers are “obvious” • A patient with a poorly active variant of a drug metabolizing enzyme will have a different PK profile (and greater risk of over-exposure) to drugs that are eliminated via pathways that employ that enzyme (e.g. UGT1A1+ patients exposed to irinotecan). • Patients in which a drug target is poorly expressed (or not expressed at all) in their disease will likely not respond to the drug (e.g. Her-2 negative patients for trastuzumab, even ER- breast cancer patients exposed to tamoxifen – probably the first example of PM). • Patients who have mutations in pathways downstream from the drug target that render that target irrelevant, will not derive much benefit from the drug (e.g. Kras mutant patients exposed to EGFR inhibitors). • DUH!! • Despite the “obviousness” of these biomarker hypotheses, establishing and proving such associations is extraordinarily difficult. Why?

7. Challenges to the use of biomarker approaches to Clinical Drug Development and PM • We don’t know as much as we think we do! Example: Sorafenib, a multi-tyrosine kinase inhibitor, was originally developed as an inhibitor of Raf (an intermediate of the growth factor signaling cascade that is mutated in some cancers). The “obvious” approach of testing it in BRAF driven melanoma, would have led to drug failure (it has no real benefit here). Only after clinical activity was fortuitously discovered in a Phase 1 setting in RCC patients was it recognized that its clinical benefit resulted from the fact that it is also inhibits VEGF receptor tyrosine kinase.

8. We don’t know as much as we think we do!

9. We don’t know as much as we think we do!

10. We don’t know as much as we think we do!

11. Challenges to the use of biomarker approaches to Clinical Drug Development and PM • We don’t know as much as we think we do! • Failure to distinguish between predictive and prognostic markers.

12. Prognostic vs. Predictive Factors Prognostic Factor: Any measurement that is associated with clinical outcome in the absence of therapy, or with the application of a standard therapy that all patients are likely to receive (a predictor of the natural history of the disease). Predictive Factor: Any measurement associated with response or lack of response to a particular therapy, where response can be defined using any of the clinical endpoints commonly used in clinical trials (eg, ER for patients with breast cancer). Clark GM. Mol Oncol 2008, doi:10.1016/j.molonc.2007.12.001

13. Marker + Patients Treated with Experimental Therapy Median Survival = 12 months • Cannot determine if Experimental Therapy confers meaningful benefit over Standard Therapy • Cannot evaluate prognostic value of Marker X • Cannot assess predictive value of Marker X

14. Can we study stratified patients treated only with the experimental therapy? Marker + Patients Marker - Patients NO, because we can’t tell if marker+ patients would have done better than marker- patients regardless of treatment (i.e. it might be a prognostic marker) We really need a control group!

15. Can we study an enriched population only? Marker + Patients Experimental Therapy Standard Therapy NO, because even though marker+ patients do better on treatment relative to standard therapy, we can’t tell if the treatment might have been equally better than standard in marker– patients (i.e. it may not be marker of either prognosis or drug effect). . . . only if we will be satisfied with half of an answer.

16. Treatment A Treatment A Randomize Randomize Treatment B Treatment B What we really need is a Biomarker-based Study Design Marker+ Register Stratify Test Marker Marker-

17. Predictive but not prognostic Marker + T S T S Marker - Predictive vs. prognostic marker evaluation Marker is predictive (only patients who are marker+ show the treatment effect), but notprognostic (marker- patients do the same as marker+ patients on standard care).

18. Prognostic but not predictive Marker + T S T S Marker - Predictive vs. prognostic marker evaluation Marker is prognostic (marker+ patients do better than marker- patients on both the treatment arms and the SOC arms), but notpredictive (even though treated patients do better than SOC all patients, i.e. there is a drug treatment effect, the magnitude of the difference is the same in both marker- patients and marker+ patients).

19. Marker + T S T S Marker - Predictive vs. prognostic marker evaluation Marker is neitherpredictivenorprognostic (treatment is equivalently better than SOC in both marker+ and marker- patients)

20. Predictive and prognostic Marker + T S T S Marker - Predictive vs. prognostic marker evaluation Marker is bothpredictiveandprognostic (treatment is better than SOC in both marker+ and marker- patients, but the magnitude of the treatment effect is greater in the marker+ patients)

21. Challenges to the use of biomarker approaches to Clinical Drug Development and PM • We don’t know as much as we think we do! • Failure to distinguish between predictive and prognostic markers. • Data over-fitting and reliance on retrospective analyses. The vast majority of retrospective analyses with hypothesis independent (or other) approaches fail to be confirmed in prospective studies. The statistics of determining the “significance” of differential gene expression (for example) when there are tens of thousands of analytes in only dozens of samples is extremely dicey.

22. Challenges to the use of biomarker approaches to Clinical Drug Development and PM • We don’t know as much as we think we do! • Failure to distinguish between predictive and prognostic markers. • Data over-fitting and reliance on retrospective analyses. • Grafting a retrospective –omics analysis onto a failed Phase 3 study will rarely “rescue” the drug – even if it successful in identifying the molecular characteristics of a “responsive” subset.

23. It takes an average of 15 years for a new oncology drug to obtain FDA approval Development costs escalating: >1.5B (including cost of failures) Patient accrual is a rate limiting step in drug development. Oncology Ph 3 trials can take 3–4 years to accrue the target number of patients (<5% of US adult cancer patients participate in clinical trials) Average time from peak market sales to LOE is 4-6 years. There is not enough time to LOE remaining after a failed Phase 3 study to complete a new prospective registrational study incorporating a biomarker hypothesis generated from retrospective analysis of a preciously failed Phase 3 to make this a viable strategy! Oncology Drug Development Timelines

24. Challenges to the use of biomarker approaches to Clinical Drug Development and PM • We don’t know as much as we think we do! • Failure to distinguish between predictive and prognostic markers. • Data over-fitting and reliance on retrospective analyses. • Grafting a retrospective –omics analysis onto a failed Phase 3 study will rarely “rescue” the drug – even if it successful in identifying the molecular characteristics of a “responsive” subset. • Lack of incentives. Even successful drugs work only in a subset of patients who receive them. Better defining that subset post-marketing (where the N is large enough) may lead to better efficacy in that now-smaller population (and a label restriction), but it will not lead to higher reimbursement…

25. Challenges to the use of biomarker approaches to Clinical Drug Development and PM • We don’t know as much as we think we do! • Failure to distinguish between predictive and prognostic markers. • Data over-fitting and reliance on retrospective analyses. • Grafting a retrospective –omics analysis onto a failed Phase 3 study will rarely “rescue” the drug – even if it successful in identifying the molecular characteristics of a “responsive” subset. • Lack of incentives. Even successful drugs work only in a subset of patients who receive them. Better defining that subset post-marketing (where the N is large enough) may lead to better efficacy in that now-smaller population -- and a label restriction -- but it will not lead to higher reimbursement… • Making binary decisions (“treat” / “don’t treat”) on continuous data.

26. Where do you draw the treat / don’t treat cut-off? Intermediate risk Number of patients High risk Low risk Very low risk Very high risk Number of risk factors for disease X

27. Challenges to the use of biomarker approaches to Clinical Drug Development and PM • We don’t know as much as we think we do! • Failure to distinguish between predictive and prognostic markers. • Data over-fitting and reliance on retrospective analyses. • Grafting a retrospective –omics analysis onto a failed Phase 3 study will rarely “rescue” the drug – even if it successful in identifying the molecular characteristics of a “responsive” subset. • Lack of incentives. Even successful drugs work only in a subset of patients who receive them. Better defining that subset post-marketing (where the N is large enough) may lead to better efficacy in that now-smaller population -- and a label restriction -- but it will not lead to higher reimbursement… • Making binary decisions (“treat” / “don’t treat”) on continuous data. • Regulatory and partnering indemnification of co-diagnostics.

28. Personalized Medicine: Some Conclusions and predictions • The march of science is inevitable and biomarker/PM approaches to clinical care will continue to advance. • Given the current regulatory climate, initial focus will be on prediction of adverse drug responses. • PM is expensive – at least in its initial stages. There will be substantial resistance on the part of payors to reimburse PM/BM/diagnostic tests unless true clinical benefit can be definitively established. • Empirical approaches will trump molecular approaches in all cases where the cost/risk : benefit ratio favors it. • Despite the hype, the impact of PM on medical practice will be evolutionary rather than revolutionary.