A Framework for Biomarker and Surrogate Endpoint Use in Drug Development

1. A Framework for Biomarker and Surrogate Endpoint Use in Drug Development Janet Woodcock M.D. Acting Deputy Commissioner for Operations November 4, 2004

2. Agenda • Current Definitions • Limitations of current conceptual and developmental framework • Towards robust use of biomarkers in drug development • Towards regulatory acceptance of surrogate endpoints

3. Definitions • NIH Definitions Working Group • Development of terms and definitions • Overall conceptual model of biomarkers and surrogate EP • Offshoot of FDA/NIH Consensus conference on topic • Clin Pharm Thera 69:89, 2001

4. Biomarker Definition • A characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention • FDA draft Pharmacogenomics Guidance further defines possible, probable and known valid biomarker categories depending on available scientific information on the marker

5. Clinical Endpoint Definition • A characteristic or variable that reflects how a patient feels, functions or survives • (Note that, except for survival, all these involve some sort of intermediary measurement)

6. Surrogate Endpoint Definition • A biomarker intended to substitute for a clinical endpoint. A surrogate endpoint is expected to predict clinical benefit (or harm, or lack of benefit) based on epidemiologic, therapeutic, pathophysiologic or other scientific evidence

7. Use of Biomarkers in Clinical Medicine • Diagnosis • Tool for staging disease • Indicator of disease status • Predict and/or monitor clinical response to an intervention

8. More at Stake than Efficient Drug Development • Biomarkers are the foundation of evidence based medicine-who should be treated, how and with what • Absent new markers, advances in more targeted therapy will be limited and treatment will remain largely empirical • It is imperative that biomarker development be accelerated along with therapeutics

9. Examples of Biomarkers in Clinical Medicine • Electrocardiogram • PET brain image • Serum chemistries • Auto-antigens in blood • Bone densitometric measurement • Pulmonary function test • Neonatal Apgar score

10. Use of Biomarkers in Early Drug Development and Decision Making • Evaluate activity in animal models • Bridge animal and human pharmacology via proof-of-mechanism or other observations • Evaluate safety in animal models • Evaluate human safety early in development

11. Examples of Biomarkers in Early Drug Development • Serum chemistries • Cell surface protein expression • Drug pharmacokinetic measurements • Drug metabolizing isoenzyme phenotype • Serum transaminases • Genomic expression profile • Drug distribution or receptor occupancy via imaging

12. Use of Biomarkers in Later Drug Development and Decision Making • Evaluate dose-response and optimal regimen for desired pharmacologic effect • Use safety markers to determine dose-response for toxicity • Determine role (if any) of differences in metabolism on above • Rolan. Br J Phamacol 44: 219, 1997

13. Biomarkers in Later Clinical Development • Psychometric testing • Pain scales • Imaging studies • Culture status (antimicrobials) • Pulmonary function tests • Serum chemistries • Electrocardiogram

14. Use of Surrogate Endpoints in Later Drug Development • Efficacy: Use to asses whether drug has clinically significant efficacy • Safety: Use to predict the safety profile when used in the “real world”

15. Surrogate Endpoints in Drug Development • Blood pressure • Intraocular pressure (glaucoma) • HgB A1c • Psychometric testing • Tumor shrinkage (cancer) • ACR criteria (rheumatoid arthritis) • Pain scales (pain)

16. Limitations of Current Conceptual and Developmental Framework • Biomarkers represent bridge between mechanistic understanding of preclinical development and empirical clinical evaluation • Regulatory system has been focused on empirical testing: skewing overall clinical evaluation towards “all empirical” • Early mechanistic clinical evaluation often lacking

17. Limitations of Current Conceptual and Developmental Framework • Business model for biomarker development is lacking • Consequence: no rigorous pursuit of evidence to “Qualify” marker or to assemble data for regulatory approval • Exploration of clinical relevance is generally ad hoc

18. Urgent Need to Overcome Current Obstacles • New opportunities to link biomarker development to the drug development process • Requires clear regulatory framework for the technical evaluation that is required • Need to spur new business models

19. Limitation of Current Conceptual Framework for Development of Surrogate Endpoints • Current model for surrogacy based largely on cardiovascular and HIV experiences in the 1990’s • CAST outcome: • Surrogate: suppression of VBP’s • Mortality increased in treatment arms Temple. “A regulatory authority’s opinion about surrogate endpoints”. Clinical Measurement in Drug Evaluation. Wiley and Sons. 1995

20. Surrogate Endpoint Development • HIV epidemic in 1990’s spurred evaluation of the use of surrogate endpoints • Rigorous statistical criteria for assessing correlation of candidate surrogate with clinical outcome* • No surrogate EP has met these criteria • *Prentice. Stat in Med 8: 431, 1989

21. Surrogate Endpoint Development • HIV RNA copy number is now used as early drug development tool, surrogate endpoint in trials, and for clinical monitoring of antiviral therapy • Lack of complete correlation with clinical outcomes has not compromised utility • Successful development of antiretrovirals and control of HIV infection

22. More Fundamental Problems with the Current Framework for Surrogate Endpoints • There is no “gold standard” clinical outcome measurement • Survival: data show that desirability of longer survival dependent on quality of life, in many individuals’ estimation • Generalizability of any single outcome measure can be limited by trial parameters

23. More Fundamental Problems with Current Framework for Surrogate Endpoint Development • Many clinical outcomes are multidimensional—a single outcome measure may miss domains of interest • Very difficult to capture both benefit and harm within a single measure—very unlikely for a biomarker. The concept of “ultimate clinical outcome” includes parameters such as duration of observation that are important dimensions. • However, knowledge about these dimensions could be acquired outside of the biomarker measurement

24. Problems with Surrogate Endpoint Framework • Per-patient view of outcomes very different from population mean view of outcomes. • Newer (and older, e.g., metabolizing enzymes) biomarkers provide information at the individual level

25. Problems with Surrogate Endpoint Framework • For above reasons, should view drug development as “progressive reduction of uncertainty” about effects—or “increasing level of confidence” about outcomes • Multidimensional NOT binary information set

26. Problems with Surrogate Endpoint Framework • No single measurement contributes all knowledge • Population mean findings may not be valid for any given individual

27. Future of Surrogate Endpoint Development • Composite outcome measurements • Responder rather than population mean analyses • Individualized therapy

28. Future of Surrogate Endpoint Development • With these evaluations, also will require larger treatment effects to provide face validity • Basic problem is that drugs don’t work very well on a population basis right now

29. Towards Robust Use of Biomarkers in Drug Development • Biomarkers must be USED to be accepted • Add-on costs in clinical trials have been a significant barrier • Requires government-academic-industry collaboration and focus

30. Towards Robust Use of Biomarkers in Drug Development • Diagnostic and imaging industry sector needs to be fully engaged • FDA must provide regulatory framework

31. Development of New Biomarkers • New biomarkers can revolutionize both development and use of therapeutics and preventatives • Requires commercial development of the particular biomarker technology • Regulatory pathways for efficient development of therapeutic/biomarker pair also needed

32. Towards the Regulatory Acceptance of Surrogate Endpoints • Further exploration of conceptual framework needed: re-assessment of the idea of “validation”; perhaps adoption of new nomenclature • More emphasis on multidimensional approach to efficacy • Greater emphasis on safety biomarkers

33. Towards the Regulatory Acceptance of Surrogate Endpoints • Replace idea of “validation” with understanding of degree of certainty in various dimensions • Usefulness of any surrogate will be disease-, context-, and to some extent intervention-specific. • Develop framework for understanding usefulness of surrogate as evidence of effectiveness (or safety) in a context-specific manner

34. Summary • Important public health need for development of additional biomarkers to target and monitor therapy • This requires use in clinical trials during drug development • Business model/regulatory path for such markers is not clear to industry • Clarification and stimulus required

35. Summary • Definitions for biomarkers, clinical outcomes and surrogate endpoints have been developed • Further development of the model needed in order to increase use and utility of markers in drug development • Single measurements will rarely capture all dimensions of clinical outcomes

36. Summary • A multidimensional and continuous model needs to replace the current single dimension, binary model of clinical effect • Outcomes happen to people, not populations. In order to target therapy, individual outcomes, (e.g. responder analyses, individual AEs etc.) will need to be correlated with biomarker status

37. Summary • FDA is considering development of these concepts as part of its “Critical Path” Initiative. • Development would include process for refining general framework as well as individual projects on biomarker and surrogate endpoint development