Presentation on theme: "Biomarkers and Surrogate Endpoints in Drug Development Technical and Regulatory Considerations Biomarkers and Surrogate Endpoints in Drug Development Technical."— Presentation transcript:
Biomarkers and Surrogate Endpoints in Drug Development Technical and Regulatory Considerations Biomarkers and Surrogate Endpoints in Drug Development Technical and Regulatory Considerations Gracie Lieberman, Genentech 2006 FDA/Industry Workshop
Content Changing landscape Efficacy surrogate endpoints used for approval – case studies –Herceptin –Iressa Surrogate endpoints in proof of concept trials –Selecting sub-population –Selecting dose Role of mechanism-based biomarkers –PET studies in cancer
Changing landscape In the past years: –Better molecular understanding of diseases –Earlier, more precise diagnosis –New targeted, improved therapies Impact on clinical trials –Assessment of improvements in clinically meaningful endpoints require enrolling many patients and then following them for a long time. –Assessment of improvements in clinically meaningful endpoints require enrolling many patients and then following them for a long time. Emerging need –Develop strategies for reducing the time and cost of drug development. –Develop strategies for reducing the time and cost of drug development. –Use of surrogate endpoints either in proof-of-concept or label- enabling trials. Defined in clinical practice and used by clinicians to monitor and treat patients New mechanism-driven biomarkers
Endpoint considerations Study defined endpoints supporting product labeling –Demonstrate clinically meaningful benefit –Relevant to a specific indication and study population –Reliable and reproducible Study defined endpoints supporting early decisions –Correlated with clinically meaningful outcomes –Relevant to a specific indication and study population –Sensitive to small sample sizes Pharmacodynamic markers Surrogate endpoints
Case Studies Herceptin – PFS as an endpoint Iressa – Risks of accelerated approval
Herceptin in MBC Herceptin a is recombinant DNA-derived humanized monoclonal antibody that targets HER2, the protein product of c- erbB-2. In September 1998 Herceptin was approved for: –First line treatment in combination with paclitaxel in MBC patients whose tumors overexpress HER2. The primary endpoint used was not overall survival (OS) but progression free survival (PFS).
PFS as a primary endpoint How to assure objectivity and minimize bias –Randomized, placebo control study Weekly placebo infusions for months Impact on enrollment –Strict schedule of efficacy assessment –Independent review of radiographic images Process for collecting images Assessment of skin lesions required distribution of cameras to sites –Strictly define rules for missing data Independent review of images not available
PFS as a primary endpoint The challenge continues –21% enrolled in the first year –Protocol amendment to remove placebo Impact on primary endpoint (PFS) –Invoke real time independent review of radiographic images Primary endpoint had to be confirmed by the review committee Patients and investigators compliance –Turn-around review time was critical –Offer cross-over to control patients
PFS as a primary endpoint Conclusions –In September 1998 Herceptin was approved based PFS –Survival as a secondary endpoint was statistically significant –65% of control patients crosses-over to receive Herceptin –Two years later the survival benefit continued to be present Sub-group analysis impacted by crossover
Case Studies Herceptin – PFS as an endpoint Iressa – Risks of accelerated approval
Iressa in NSCLC Iressa a quinazoline-based small molecule, an EGFR TK inhibitor In phase I objective responses observed in NSCLC Two phase II monotherapy trials –Response rate (RR) as primary endpoint –Two dose groups May Accelerate approval for 3 rd line monotherapy use based on RR
Accelerated approval - risks Need to conduct large, confirmatory trials –What if negative? Despite meaningful responses in recurrent NSCLC patients, Phase III trials failed to show any significant clinical benefit Approval was revoked in June 2005 –The medicine should be used only in cancer patients who have already taken the medicine and whose doctor believes it is helping them. New patients should not be given Iressa because in a large study Iressa did not make people live longer. What went wrong? –Patient selection ? –Dose/schedule ?
Can this be avoided? Demonstrating clinical benefit with molecular- targeted agents is more complex than with conventional cytotoxic agents –Selection of sub-population: who is most likely to benefit –Identification of optimal biological dose Answers before phase III – is this achievable Proof-of-concept trials –Is PFS a sufficient endpoint
Sub-population selection Complex process Tissue samples required –Blood/serum feasible –Tumor samples are challenging Missing data Archival samples not always relevant Randomized, controlled studies required –Stratification by biomarker for sub-population selection At randomization or during analysis –Not possible to distinguish between a prognostic and predictive biomarker without a proper control
Biomarker based population selection PFS with no control arm Gordon et al. J Clin Oncol. 2005;23:16S (abstract 5051). Gordon et al. J Clin Oncol. In press. pHER2+ tumors trend toward longer PFS Treatment effect or natural course of disease? Median PFS pHER2 positiven=820.9 weeks pHER2 negativen= weeks pHER2 unknownn= weeks All subjectsn= weeks All patients treated with pertuzumab
Dose/schedule selection Complex process –May be indication specific –May be regimen specific Typical trial –Randomized –3 arms Control/lower dose/higher dose subjects per arm –PFS as primary endpoint How is dose selected –Better efficacy compared to control - winner
Time to event endpoints Optimal vs. sub-optimal dose Probability that the observed HR 0.75 True HR Number of events We need to do better
Mechanism-based biomarkers Demonstrating clinical benefit with molecular- targeted agents is more complex than with conventional cytotoxic agents –Escalating clinical trials costs and large numbers of patients required for currently used clinical endpoints mandate becoming more efficient in determining how well new agents can address unmet medical needs. –That efficiency can be achieved by validating correlations between specific biological mechanisms of disease and clinical outcomes. –Easier said than done!
Mechanism-based biomarkers Technological advances provide great opportunity for the development of biomarkers –Molecular and cellular techniques Tissue samples –Tumor/blood/surrogate –Imaging technologies Current pre-clinical models still have limit ability to predict clinical effects –Biomarkers need to be co-developed with the novel agent In early phases – no clinical data This will benefit second generation of the new agents or new indications –Systematic way of analyzing and interpreting data
Mechanism-based biomarkers PET imaging Use of surrogate endpoints in cancer prevention
PET studies Speed development Provide information about the activity of molecular pathways Determine if new agents are hitting the target Measure treatment effect Tissue samples are not required
FDG PET in NSCLC FDG PET in NSCLC Wolfgang Weber et al. J Clin Oncol. 2003;21:2651
FDG PET in Lymphoma L. Kostakoglu, J Nuc Med 43:
Challenges How to define metabolic response –Change in standard uptake values (SUV) that based on re-tests can be reliably detected Arbitrary cut-off –Optimized thresholds correlated with outcomes Based on analysis –What adjustments made for minimum p-values Not applicable to other treatments or indications Use of core labs in multi-center trials Not ready as a surrogate efficacy outcome for combination trials Not all lesions are PET avid
Cancer Prevention Preventing heart diseases –Lowering cholesterol / blood pressure Surrogate biomarker endpoints for cancer prevention trials –Establishment of long term safety and efficacy for preventive drugs is critical –Process for accelerated approval based on biomarkers will be needed Colorectal adenomas –Current development of mechanism-driven biomarkers is critical for future cancer prevention trials.