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Drug/Diagnostic CoDevelopment Paths to Getting the Right Drugs to the Right Patients Gracie Lieberman - Genentech The 2011 Midwest Biopharmaceutical Statistical Workshop Personalized Health Care – Fantasy or Reality? Here’s my tumor’s DNA sequence The Changing Landscape The past decade has seen a great increase in the understanding of cancer pathogenesis at a molecular level. Many signaling pathways have been identified and implicated in cancer development. Most of the today’s focus in oncology drug development is on targeted therapies which are expected to be active only in subsets of patients The target of interest is often a key driver of tumor genesis and therefore may serve as the basis for a candidate predictive biomarker (e.g.. HER2-gene, mEGFR, mBRAF) True targeted therapy requires not just a selective agent but a means of identifying appropriate patients (i.e. a diagnostic) Implications for Development Rational targeted drug development strategies aim to identify patient populations likely to derive greatest clinical benefit from a new molecular entity (NME). Which target: selective or multiple Which disease Reliance on different nodes in the pathway (RTKs, PI3K mt, mTOR) Translatability of Proof Of Concept (POC) across diseases, line, combinations Which patient (role of diagnostics) Putative Dx markers have strong links to tumor biology Are pathway alterations predictive of target dependence What is the size of the targeted population Single agent vs. combination Can we select patients based on any of them? Chemotherapy, EGFR, MEK, VEGF….. Appropriate comparator arm? How hard to hit the target (dose and schedule) Which Way to Rome? Drug-Dx Co-development Current regulatory paradigm requires early biomarker discovery Target Discovery Drug Preclinical Dev’t Phase 1 Phase 2 Phase 3 FDA filing, approval, launch + Dx Biomarker discovery Biomarker Assay Dev’t Clinical Validation of biomarker hypothesis PMA filing, approval, launch FDA draft white paper on Drug-Dx co-development To maximize clinical benefit from our therapeutics: Informed decision making around indication choice Enable patient selection Predictive biomarker: a test that can be done before treatment to predict whether a particular treatment is likely to be beneficial Pharmacodynamic biomarker: a test that can be done pre- and posttreatment to confirm target modulation Clinical Biomarker Strategy “Follow the tumor genetics” Focus on high prevalence oncogenic mutations, amplification, tumor suppressors Ensure assays are available for putative biomarkers Path to Companion Diagnostics enabled Biomarker testing incorporated in trial design Mandatory tumor tissue Exploratory assays to evaluate other promising biomarkers qRT-PCR gene signatures Mutation and copy number profiling Multiple PD biomarkers to confirm pathway inhibition PI3K/MEK Pathways: multiple nodes are candidates for molecules targeting core oncogenic pathways; potential for broad application, combinations, with strong biomarker hypotheses GF PTEN Prostate RTK P P GBM P P PI3K Ras PTEN S6K Bladder Gastric AKT mTOR Scaffold PDK1 Breast Ovarian HNSCC HCC Raf MEK ERK Proliferation Survival Invasion Melanoma Pancreatic NSCLC Endometrial CRC PI3K p110 oncogenic mutations in: 37% Endometrial 29% Breast 25% Colon 13% Bladder PIK3CA amplified in 30% ovarian, lung PTEN mutant/lost in: breast, prostate, glioblastoma, melanoma, pancreatic, endometrial, ovarian, lung, head and neck, hepatocellular, thyroid Ras Preclinical Biomarker Discovery: Breast Cancer Her2 Amplified Trastuzumab, lapatinib Basal-like “Triple Negative” No targeted therapy Luminal Hormonal therapies Vimentin (myoepithelial basal) E-cadherin (epithelial) Sorlie et al, PNAS, 2001 PHC assessment to guide development strategy • Preclinical data in mBC suggests that cell lines harboring PI3K/PTEN alterations are highly sensitive to the pathway inhibitors What does Dx hypothesis usually look like? Scientific evidence based on the Mechanism of Action (MOA) In-vitro and xenograft assays Availability of a well defined biomarker hypothesis One or at most two researchgrade assays Single summary measure Appropriate cutoff available or to be derived for ordinal/continuous biomarkers Clinical evidence from our and competitor’s trials (Phase I is unlikely to yield useful efficacy information) Prevalence of the proposed biomarker (description of the distribution if continuous) and known prognostic characteristics (given the line and indication) Literature reports Public databases Should we consider a separate tumor registry to address the question? If prognostic, is genomic drift a potential issue? Archival vs. Fresh Biopsies Dedicated studies may be necessary to investigate assay or biomarker properties, prevalence and prognostic significance Underlying Assumption Is there a strong Dx hypothesis? yes no Is Dx to be used in the label? no yes Is activity in Dx (-) exceedingly unlikely? yes Patient selection To be used in the indication section of1the label Is Dx to be used in the label? yes WHY? no Dx will be a coprimary or 2-ary endpoint (testing prespecified hypotheses) no Pause Define path forward; Consider: costs, risks and timelines Co-primary could be used in the indication or clinical sections of the label; 2-ary in the clinical section of the label All comers trial; Exploratory data mining To be used for publications, hypothesis generation; Rescue/retrofit Dx PHC Assessment Development Strategy PHC Assessment Strong Dx hypothesis No activity in Dx- Strong Dx hypothesis Some activity in Dx- No strong Dx hypothesis Exploratory Stage Development Strategy Patient selection through all phases of development Selected Complex, larger phase IIs with stratification Complex phase IIIs Stratified No selection or stratification Data exploration All Comers Basic Principles Prospectively Defined Dx markers are required for any label enabling action. Dx markers must be defined prior to pivotal trials and hence planned for in a Clinical Development Plan (CDP). Dx. Marker evaluation often in phase II. Retrospective Analyses are only considered “hypothesis” generating. Incorporating Dx - Impact on components of CDP Target Product Profile (TPP) Phase I trials Selection for quick signal seeking Phase II (POC) trials Parallel development of companion diagnostic Complex issues become more complex More unknowns, more questions to answer Phase III trials Clinical Validation of Dx Design depends on Phase II outcome Selection, stratification or all-comers Longer and Costlier but this is reality! Phase II Considerations Objective: simultaneous Rx/Dx evaluation Scientific rationale and pre-clinical data - main determinants of the scenario prior to Phase II Strategic and operational considerations Statistical considerations Co-primary endpoints Value added and feasibility of stratification Defining cut-offs for continuous biomarker Go/No Go decision algorithm Dedicated efforts to investigate assay and/or biomarker properties Reproducibility, prevalence, prognostic value Strategic considerations How does the proposed POC trial fit into the clinical development plan (CDP)? The molecule CDP timeline and other ongoing/planned POC trials The TPP with the Dx component (clinical and commercial considerations) Rank speed, PTS and cost according to their relative importance taking into account company portfolio and competitive landscape The same Dx hypothesis may be addressed Need to be informed with regards to the Dx by the proposed POC trial The follow up molecules which need to be informed by this trial In Stratified scenario, need to define and enable joint Rx/Dx GO decision prior to Phase III Operational Considerations Population Dx prevalence Tissue testing Turn-around time Assay-failure proportion Enrollment rate Number of qualified sites and the ramp-up curve Evaluation of sensitivity to the assumptions Protocol nuances… Statistical Considerations The design of the POC trial needs to enable a decision on the population and co-primary endpoints in Phase III, i.e. all the Dx subsets of primary interest need to be sufficiently populated Single Arm vs. Randomized Controlled trial: in PHC setting the value of randomized trial is higher than in All Comers development Propose a decision algorithm based on operational characteristics and evaluate its robustness to the departures from the assumptions under a variety of underlying treatment/biomarker scenarios Markers with pre-defined cutoffs: the proposed sample size in each biomarker sub-group of primary interest should be roughly similar to the size of the All Comers trial without the Dx Significantly larger sample sizes may be required for a continuous biomarker, possibly on the order of 100’s or 1000’s of events. As of today, the question of identifying an appropriate cutoff for a truly continuous biomarker remains an open problem from all, statistical, regulatory and clinical perspectives. Selected Phase II Trial? Issues to consider: Strength of available scientific evidence (what is the PHC assessment?) Timelines (selected vs. stratified) Regulatory considerations Operational considerations Number of sites Would we have to propose Stratified Phase III? What do we not learn by restricting POC to selected population and when may it be justifiable? Safety and Ethical concerns Adaptive Phase II Design considerations One-arm response driven trials: modification of Simon’s two stage design to include evaluation of Dx subsets Randomized time-to-event trials: dynamic patient allocation ratio adjustment based on early readout In theory allows for arbitrary number of Dx subgroups and active treatments Generally involves a Bayesian approach with continuous updating Relies on the relevance of the early end point to the clinical outcome of interest Increased operational complexity In practice, sample size requirements or operational complexity can make complex adaptive designs intractable for the initial proof of concept evaluation. Understanding Dx sub-groups is key Phase III Decision Criteria Based on Phase II Result Phase II Results HR is large in AC HR is small in AC HR is small in Dx+ Selected AC if HR in Dx+ and Dxis similar Co-primary (Dx+, AC) if HR in Dx+ << HR in DxSelected if HR is large in Dx- HR is large in Dx+ Stop AC Phase III Considerations Study Objective Establish risk/benefit Clinical Validation of Dx Implementation issues Analytically validate Dx assay before applying it to specimens in pivotal trials Accruing / prospective stratification based on Dx assay Analysis approaches Test two hypothesis, All comers Dx positive subgroup Appropriately control for type I error Clearly define your decision tree – “freebies” there are no End of Phase III Decision Criteria Phase III outcome Not statistically significant in all Comers Statistically significant in all Comers All Comers claim if no diff. b/w Dx- & Dx+ groups Statistically significant in Dx+ group SELECTION CLAIM Greater benefit claim if clinically meaningful diff. b/w Dx- & Dx+ Selection Claim if no improvement in Dx- group “Rescue” or “Retrofit” Diagnostics J. Woodcock, 2010 Old Drugs – New Tests Biomarker not known at the time of study initiation New scientific advancements/new technologies Data not analyzed with that biomarker as part of the hypothesis Biomarker discovery – generation of new hypotheses Inform future molecules Retrofit Dx/Exploratory Analysis Prospective/Retrospective Study Completed or post-interim-analysis trial Clinical outcomes data unblinded and analyzed without the biomarker data Overall treatment effect on the primary outcome in the intent to treat population (everyone randomized) Statistically persuasive result, usually p value < 0.025 onesided Patient samples collected prior to treatment initiation Diagnostic hypothesis/analysis plan - prospectively specified Analysis is retrospective If no statistical significance on primary endpoint everything further is exploratory R. T. O’Neill; FDA; Dec 16, 2008 Components of good biomarker analysis plan Role of randomization - fairness of comparison Marker availability – impact of convenience samples Marker performance How many hypothesis How many outcomes Model selection Marker performance and prevalence may explain study to study heterogeneity Statistical control of false positive conclusions – Bias due to missing data Over-fitting can lead to bias Validation methods Data to generate the hypothesis vs. data to confirm the hypothesis Cutoff Selection for Continuous Biomarkers Sparse literature and examples; no FDA guidance Ideal approach: biologically or clinically meaningful cutoff; multi-modal distribution due to underlying biology. Common approaches (data driven): good for exploration, but not good enough for implementation in subsequent confirmatory trials Percentiles: use medians, quartiles, etc. Optimization: e.g. find cutoffs that maximize – treatment effect differences in marker+/-; – marker+ subset that meets a pre-specified treatment effect. multiplicity issues Explore effect vs. cutoff profiles Developing a Complex Predictor Uncharted area for predictive application: Multi-step processes involving many decisions & tuning: No HA-approved examples of complex predictor-based companion diagnostics predictive of a specific drug Only related examples: tests for prognosis of breast tumor recurrence: Oncotype DX (21-gene qPCR), Mamma Print (70gene array) Feature selection, model building, performance evaluation Choice of options & algorithms, especially the performance metric, should be guided by the clinical objective and the nature of dataset Major concern: high variance & over fitting due to large p (features), small n (sample size) Validation strategy important Reality check: Set realistic expectation on the feasibility given the sample size, data quality and potential impact Summary “Biomarker qualification is a promising new route for establishing novel diagnostics in drug development. The controversy over clinical utility of diagnostics in drug therapy is a reflection of the underlying progress in understanding the basis of variability in human responses to interventions; in this regard, it is good news. Most scientific progress is ushered in by disputes and disagreements; hopefully, these will not cause us to lose sight of the promise of safer or more effective drugs in the near future.” Janet Woodcock; Assessing the Clinical Utility of Diagnostics Used in Drug Therapy. Clinical Pharmacology &Therapeutics; Volume 88 Number 6; December 2010 Acknowledgements Cheryl Jones Jane Fridlyand Ru-Fang Yeh Mark Lackner Biostatistics Predictive Biomarker Guidance 2010 Team Thank You!