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Mapping the Future in Oncology Drug Development June 5, 2010 Copyright © Quintiles 2010 Faculty – Paul Bunn, Jr., MD, Professor and Director, University of Colorado Cancer Center – Richard Gaynor, MD, Eli Lilly – S. Gail Eckhardt, MD, Professor of Medical Oncology, University of Colorado Cancer Center – Denis Lacombe, MD, MSc, Scientific Director, European Organisation for Research and Treatment of Cancer – John Smyth, MD, Emeritus Professor of Medical Oncology, University of Edinburgh – Daniel Haller, MD, Professor of Medicine, University of Pennsylvania – King Jolly, PharmD, Senior Vice President Innovation, Quintiles – Eric Groves, MD, PhD, Executive Global Strategic Drug Development Director, Quintiles Scientific program and objectives Title Speaker Welcome and Introductory Remarks Paul Bunn, Jr. Biomarkers as an Approach to Improving Productivity Richard Gaynor Improvements in Trial Design Gail Eckhardt Denis Lacombe Regulatory Opportunities John Smyth New Pilot Examples Exploring these Novel Approaches Daniel Haller King Jolly Panel Discussion and Audience Questions Paul Bunn, Jr. Objectives • Advance understanding of the development of appropriate biomarkers • Identify potential improvements in clinical trial design • Enhance knowledge of strategies to navigate the regulatory environment • Raise awareness of best practice to improve the outcome of drug development Mapping the Future in Oncology Drug Development Richard B. Gaynor, M.D. Vice President, Eli Lilly and Company Copyright © Quintiles 2010 What are the Challenges of Personalized Medicine? • Biomarkers • • Which one? When? How? Reproducibility • Trial design • • Prospective vs. retrospective Where to use the biomarker? • Sampling • • • Sufficient numbers of patients Mandatory vs. voluntary Collection/storage • Diagnostic and drug co-development • • Regulatory hurdles Pharma/biotech partnering with diagnostic companies • Realities of personalized medicine What is a Biomarker? • • • • Definition: “A characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” (BDWG 2001) Types of biomarkers: predictive, prognostic, pharmacologic Oncology molecular biomarkers • Central Dogma: DNARNAProtein • Current Landscape: • DNA: mutations, copy number, modification (methylation, etc) • RNA: gene, microRNAs, expression levels • Protein: • Methodology: IHC, Western blot, ELISA • Question: Amount of protein, protein modification, protein location in the cell Other biomarkers: Histology, clinical (rash, hypertension, neutropenia) BDWG. Biomarkers and surrogate endpoints. 2001. Genomic Causes of Cancer Mutation GGT kras R12 AGT CGT TGT GAT GCT GTT K-RAS BRAF EGFR JAK2 Notch1 PIK3CA Amplification Deletion HER2 c-MET PTEN VHL Translocation BCR-ABL EML4-ALK Halilovic and Solit 2008; Mok et al. 2009; Karapetis et al. 2008; Ross et al. 2009; Smolen et al. 2006; Wang et al. 2006; Hoebeek et al. 2005; Shaw et al. 2009; Sawyer 1999. Next Steps for Genomics • Large scale projects such as NIH’s The Cancer Genome Atlas (TCGA) aim to integrate the increasingly complex information available from cancer biology • The International Cancer Genome Consortium (ICGC) is coordinating efforts to sequence 500 tumors from each of 50 cancers • The ultimate goal to merge the multiple layers of information from gene to the clinical phenotype to personalize therapy Clinical Gene Genome Pathways Outcomes Ledford. The Cancer Genome Challenge. 2010. http://cancergenome.nih.gov/ Issues in Development of a Companion Diagnostic as a Predictive Marker • Marker validation in pre-clinical and translational studies • Prevalence of the marker(s) • Low (<10%): Enrichment designs • Moderate (10–50%): Stratified by marker positivity • High (>50%): Unselected population - prospective or retrospective analysis • • • • Optimal phase of clinical development for inclusion Reproducibility and validity of assays Evolution of assay technology Timing and logistics of performing assays Mandrekar and Sargent. J Clin Oncol. 2009;27:24. When is a Biomarker Truly Necessary? Biomarkers are especially important in diseases with low response rates in the overall population CML Patients All Breast Cancer Patients HER2+ Breast Cancer Patients All NSCLC Patients EGFR MT+ NSCLC Patients Gleevec 90% RR Herceptin 10–15% RR Herceptin 35–45% RR Iressa 10–15% RR Iressa 60–70% RR Slamon et al. NEJM 2001; Kantarjian et al. NEJM 2002; Vogel et al. JCO 2002. 20:3; Douillard et al. JCO 2010. Trial Designs to Validate Predictive Markers • Retrospective • Prospective • Unselected Design • Enrichment Design • Adaptive Design Marker Based Strategy Design: Prespecified Stratification Factor • • • • Biomarker or diagnostic test is not used for randomization Biomarker investigated as a prespecified stratification factor Allow for identification of a treatment by diagnostic test result interaction Rationale for this design: results of the testing will not be readily available at the clinical sites for informing randomization All subjects All subjects tested but result not used for randomization Tested Drug Control Noncombination Product Example, FDA Drug-Diagnostic Co-Development Concept Paper, 2005. Overall Survival by K-ras Mutation Status: Advanced CRC HR = 0.98 P = 0.89 HR = 0.55 P <0.001 Karapetis et al. NEJM 2008;359:17. Marker-Based Strategy Design: Marker + and Non-Marker-Based Arms • • After marker status is known, each patient is randomly assigned to either have therapy determined by their marker or to receive therapy independent of marker The predictive value of the marker is assessed by comparing the outcome of all patients in the marker-based arm to that of patients in the non-marker-based arm Marker-Based Strategy Test – Treatment A Test + Treatment B Test Marker Register Randomize Non-MarkerBased Strategy Treatment A Randomize Treatment B Sargent et al. JCO 2005;23:9. Marker Based Strategy Design: Enrichment for Marker + Patients • Testing is considered a prerequisite for use • Clinical efficacy and safety are linked • Reasonable certainty that the drug response will only occur in biomarker positive patients Randomize Marker + All Subjects Marker Testing Treatment A Treatment B Marker – Noncombination Product Example, FDA Drug-Diagnostic Co-Development Concept Paper, 2005. Trastuzumab in HER2+ Breast Cancer HR = 0.48 P <0.0001 HR = 0.67 P = 0.015 Romond et al. NEJM 2005;353:16. Innovative Designs: Adaptive Trials • Randomize between at least two arms within biomarkerdefined strata • Different signatures, different allowed drugs • Evaluate success in an ongoing manner • Drop poor performers • “Graduate” good performers to phase III trials • Ongoing trials: ISPY-2 (Breast), BATTLE (NSCLC) Zhou et al. Clinical Trials 2008;5:3. Challenges in Developing a Diagnostic • Regulatory landscape is challenging • Limited precedence • Rapidly changing technology and science • Analytical and clinical validation and quality systems • Sampling issues • Mandatory vs. voluntary • Pharma/Biotechs often have limited experience in diagnostic development • Partnerships • Shared risk • Trial design for registration • Prospective vs. retrospective What is the Path Forward for Diagnostics • Partnerships between diagnostic companies and pharma • Improving diagnostics • Sample collection methodologies • Reproducibility and speed • Using diagnostics early to streamline development • • • • • Enriching for the right patient Informing adaptive designs Leading to rationale combinations Go/No Go Decisions for development Indications that specify the right patient to be treated Conclusions • We need a deeper understanding of cancer biology – Define cancers by pathways rather than location/histology – Genetic characterization of newly diagnosed cancers • Invest in diagnostics/biomarkers/imaging earlier in process • Change development paradigm to prospectively validate diagnostics – Retrospective analyses pose significant regulatory hurdles – Prospective development requires larger phase II trials • Invest in novel platforms – e.g., Novel imaging modalities, circulating tumor cells . . .