Dancey Clinical Trials Vancouver Dancey 20110302 Final.Ppt [Compatibility Mode]


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LifeSciences BC - Clinical Trials in the 21st Century

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Dancey Clinical Trials Vancouver Dancey 20110302 Final.Ppt [Compatibility Mode]

  1. 1. High Content Clinical Trials – Design and Infrastructure Janet Dancey, MD, FRCPCProgram Leader, High Impact Clinical Trials, Ontario Institute for Cancer Research Director, Clinical Translational Research, NCIC Clinical Trials Group Clinical Trial Design for the 21st Century Vancouver British Columbia March 2nd 2011
  2. 2. Types of Trials• High Impact (correlation with clinical outcome) Multi-institutional Fewer samples, complex analyses E.g. phase 2 trials and phase 3 trials, population studies Require standardization across sites and/or more robust assays Address clinical-biological correlations, more likely to have clinical impact• High Content (Dense sample collection/analyses) Single/Oligo-institutional trials Multiple samples (number and type), complex analyses e.g. Phase 1 trials to assess novel agent Important for early development/evaluation Address biological questions: target/pathway inhibition 2
  3. 3. Changes in Clinical Trials Pre-Clinical Develop- Phase I Phase II Phase III mentScarcity of drugdiscovery Pre- Biomarker – Proof of Clinical Phase II-III – Proof of mechanism Commercialization Develop- principle (Predictive (Pharmacodynamic Biomarkers) ment Biomarkers) Abundance of drug discoveryAdapted from Eli Lilly and Company, Lillian Siu 3
  4. 4. Trial Designs and ModificationsTrial Phase Purpose Biomarkers Modifications0 Define dose Target modulation Normal Volunteers Selected agents PK Pre-surgicalI Metastatic Dose/schedule Target Inhibition Expanded cohorts to PK evaluate target , Toxicity toxicity or screen Activity activityII Metastatic Activity Predictive markers RandomizedIII Metastatic Clinical benefit Predictive markers Subset analysesIII Adjuvant Clinical benefit Predictive Subset analyses Prognostic 4
  5. 5. Phase 1 Trials: Considerations• Primary goal: To identify an appropriate dose/schedule for further evaluation Small• Design principles: patient Maximize safety numbers Minimize patients treated at biologically inactive doses Optimize efficiency• Study population: Heterogenous Patients for whom no standard therapy Refractory Tumours Expect target modulation but not anti-tumour activity 5
  6. 6. Where/when do biomarkers play a role? Target Versus Toxic Effects 1.0 Off Target Toxicity Target Effect in TumourProbability of Effect Target Toxicity Target Toxicity Dose/Concentration/Exposure 6
  7. 7. PLX4032, a V600EBRAF kinase inhibitor: correlation of activity with PK and PD in a phase I trial. Puzanov, K. L. J Clin Oncol 27:15s, 2009 (suppl; abstr 9021)Patients pERK pERK KI67 KI67 PK Imaging PRE PRE µM*h 4 range range range range mean PD (4) 50-100, 10-40, 20-60%, 5-25%, AUC0- median median median median 24h ~ 60; 11 45%; 12.5% 126 5-fold 4-fold µM*h 2 70 2 30 -50% 3-5% 500 - PR (1) 35-fold 10-fold 1000 PET (2) Target Pathway Tumor 7
  8. 8. Phase I Predictive MarkersTarget Drug Test Phase I ORR (%)PARP Olaparib (AZD2281; KU- BRCA1/2 9/21 (44%) Ovary, 0059436) breast, prostateHedgehog GDC-0449 Mutation 18/33 (56%) BasalSMO (PTCH/SMO) CellEML4-ALK PF-02341066 Translocation 20/31 (61%) LungBRAFV600E PLX4032 (RG7204) Mutation 19/27 (70%) Melanoma Fong et al NEJM, 2009; von Hoff et al NEJM 2009; Kwak et al ECCO/ESMO 2009: Chapman et al ECCO/ESMO 2009; 8
  9. 9. Biomarker Designs for Late Phase Clinical Trial• Target Selection or Enrichment Designs• Unselected or All-comers designs Marker by treatment interaction designs (biomarker stratified design) Adaptive analysis designs Sequential testing strategy designs Biomarker-strategy designs• Hybrid designs
  10. 10. Types of Trials – Stratified Medicine Molecular Analysis Study Rx pop. Requirements – CLIA/GLP Laboratory, Fast analysis of patient samples Smaller number of patients enrolled in trialWhole population Rx Molecular Analysis Requirements – Larger number of patients enrolled in trial, GLP – like assay/laboratoryIs there a strong hypothesis and compelling rationale?Is there a validated assay?NOTE: The population size screened does not change 10
  11. 11. Challenges to Designing Trials to Prove Personalized Medicine• Contingent on the following assumptions: Drug(s): Are effective in modulating target(s) of interest Biomarker (Mutations): Are functional “drivers” - activating or inactivating and there is no effect in the biomarker negative group Resistance mechanisms do not set in fast enough that override any antitumor activity 11
  12. 12. Target Selection/Enrichment DesignsIf we are sure that the therapy will not work in Marker- negative patients ANDWe have an assay that can reliably assess the Marker THENWe might design and conduct clinical trials for Marker- positive patients or in subsets of patients with high likelihood of being Marker-positive
  13. 13. IPASS-Schema East Asian Never smoker/light R Gefitinib former smoker A 250 mg daily Pulmonary N Adenocarcinoma D No prior treatment O M Paclitaxel 200 mg/m2 I Carboplatin AUC 5-6 Z E 1° Endpoint PFS 2° EGFR Biomarker 13Mok et al N Engl J Med 2009;361:947-57
  14. 14. IPASS-Gefitinib or Carboplatin–Paclitaxel in Pulmonary Adenocarcinoma.Mok et al N Engl J Med 2009;361:947-57 14
  15. 15. Prospective/Retrospective Design• Well-conducted randomized controlled trial• Prospectively stated hypothesis, analysis techniques, and patient population Prospective• Predefined and standardized assay and scoring system• Upfront sample size and power calculation• Samples collected during trial and available on a large majority of patients to avoid selection bias• Biomarker status is evaluated after the analysis of clinical outcomes Retrospective• Results are confirmed by independent RCT(s) 15
  16. 16. Marker-based Strategy Design Marker-Guided Randomized Design Randomize To Use Of Marker Versus No Marker Evaluation Control patients may receive standard or be randomized M+ New Drug Marker Determined Treatment Control RandomizeAll Patients New Drug Randomize Treatment Control OR Standard Treatment Control• Provides measure of patient willingness to follow marker-assigned therapy• Marker guided treatment may be attractive to patients or clinicians• Inefficient compared to completely randomized or randomized block design 16
  17. 17. Example: ERCC1: Customizing Cisplatin Based on Quantitative Excision Repair Cross-Complementing 1 mRNA Expression Cobo M et al. J Clin Oncol; 25:2747-2754 2007• 444 chemotherapy-naïve patients with stage IIIB/IV NSCLC enrolled,• 78 (17.6%) went off study before receiving chemotherapy, due insufficient tumor for ERCC1 mRNA assessment.• 346 patients assessable for response: Objective response was 39.3% in the control arm and 50.7% in the genotypic arm (P = .02). 17
  18. 18. Trial Designs With Biomarker Stratification• Restricting to 1 tumour type and 1 mutation Multiple examples – BRAF – melanoma – EML4-ALK – Lung cancer – HER2 - Breast• Inclusion of multiple mutations/biomarkers with tumour- focused question: A few examples – BATTLE - NSCLC – I-SPY 2 – Locally Advanced Breast Cancer• Inclusion of multiple tumour types with mutation-focused question Emerging studies proposed – ALK, PI3K 18
  19. 19. One Tumour/One Mutation• Restricting to 1 tumour type and 1 mutation Multiple examples – BRAF – melanoma – EML4-ALK – Lung cancer – HER2 - Breast Unless data are compelling and there is a well characterized assay this design is risky and restrictive (e.g. BRAF mutation in melanoma), Logistics are formidable but can be overcome 19
  20. 20. Multiple Tumours with One Mutations• Inclusion of multiple tumour types with mutation- focused question Emerging studies proposed – ALK, PI3K, BRAF, etc Facilitates accrual but – Same mutation may have different degrees of functionality in different tumor types (continue to stratify by histology and mutation) – Different mutations of the same gene may confer different sensitivities 20
  21. 21. MDACC Experience with Mutation Directed Therapy• Phase I trial patients from Oct 08 to Nov 09• 217 pts tested for PIK3CA mutations: 25 pts (11.5%) harbour PIK3CA mutations 21% in endometrial, 17% in ovarian; 17% in CRC; 14% in breast; 13% in cervical and 9% in SCCHN Of these 25 pts, 17 pts were treated with PI3K-AKT-mTOR pathway inhibitor 6/17 pts (35%) achieved PR 15/241 pts (6%) without PIK3CA mutations treated on same protocols responded Janku et al. Mol Cancer Ther 2011 21
  22. 22. Multiple Markers within One Histology• Inclusion of multiple biomarkers with tumour- focused question: A few examples – BATTLE - NSCLC – I-SPY 2 – Locally Advanced Breast Cancer Need to get different drugs from multiple pharma companies, big sample size Complex collaborations Large, multi-center trial 22
  23. 23. BATTLE (Biomarker-based Approaches of Targeted Therapy for Lung Cancer Elimination)• Patient Population: Stage IV recurrent NSCLC• Primary Endpoint: 8-week disease control rate [DCR]• 4 Targeted Treatments• 11 Markers• 200 patients• 20% type I error rate and 80% power for DCR > 30% Zhou X, Liu S, Kim ES, Lee JJ. Bayesian adaptive design for targeted therapy development in lung cancer - A step toward personalized medicine (In press, Clin Trials, Trials, 2008). 23
  24. 24. Four Molecular Pathways and Four Putative Targeted Therapies in NSCLC:EGFR K-ras / B-raf VEGF/VEGFR RXR/Cyclin D1Erlotinib Sorafenib ZD6474 Erlotinib + Bexarotene Biomarker Profiles: 24 = 16 marker groups 16 mark groups x 4 treatments = 64 combinations 24
  25. 25. Kim et al. AACR 2010 25
  26. 26. Phase 2 – I-SPY-2Breast Cancer Patients, candidates for neoadjuvant therapy 26
  27. 27. I-SPY2 Neoadjuvant Trial 27
  28. 28. “Druggable” Mutations4540353025201510 5 0 Breast Ovary CRC NSCLC Melanoma PIK3CA PTEN AKT1 BRAF KRAS NRAS Courtesy of P. Bedard 28
  29. 29. Trials of the (near) FutureMultiple Multiple Multiple Drugs IssuesHistologies MutationsBreast EGFR ScientificLung RAFColon MEK MethodologicalMelanoma PI3KGlioblastoma AKT RegulatoryEtc CDK4 OperationalEtc Etcetc Etc Cultural 29
  30. 30. Translation• Successful translation of science into innovative therapies requires more and better science integration of target, agent and test discovery and development better management of supporting activities, such as specimen and data management and collaboration for the trial and its conduct in the clinics 30
  31. 31. Gaps in Drug Development Preclinical Clinical Approval andDrug Discovery Development Development Marketing Phase I, II, III More intelligent and coordinated biomarker research Better Preclinical More understandi models that efficient ng of better clinical trial oncogenic predict for safety and designs and pathways and their efficacy methods potential for therapeutic targetingBetter Science, Collaboration, Coordination, Precompetitve Space 31
  32. 32. Biomarker Development & Application Group 4 markers – Clinical Application – Determine economics, laboratory proficiency for broad clinical application Knowledge Translations Preclinical To Clinical Translation and Application Group 3 markers – Clinical Validation Test in an established or defined clinical setting, drug, therapy; Multiple sites with ability to accrue a large number of patients. Choose biomarker/assay that can be used across sites Choose a drug/clinical setting with clear cut evidence of efficacy so can understand clinical correlations with biomarker; Outcomes serve as a baseline for evaluating new assays, therapies, interventions or Late Clinical new biomarkers after evaluating the biomarker with established agents Evaluation Commercialization Collect data for cost effectiveness as well as clinical outcomes Group 2 markers – Clinical Proof of ConceptProof of concept in humans but requires specialized centres due to specimen, assay, technology requirements 2a: evaluate potential to move to group 3 Early Clinical 2b: likely will stay specialized due to specific requirements Determine if sufficient clinical evidence to justify moving to group 3 Evaluation Group 2 biomarker pipeline: safety, early clinical data, preclinical rationale, assay standardization, feasibility. Group 1 - Exploratory Markers Laboratory Pre-clinical evidence is promising. More direct interrogation of pathways/biology at mechanistic level in mouse model and other pre-clinical models TranslationalNeed organized effort to chose potential “winners’ that should be selected to move into Research humans 32
  33. 33. On the Next Clinical Trial 33
  34. 34. Challenges • Research & Development • Collaborations • Regulatory • Commercial / Economics • SocietalAddressing the above to enable high content trials requires systems changes 34
  35. 35. High Content Time: What we need• Science and Technology Development Translate best science with the best chance of clinical impact Move toward quantitative assays/imaging• Collaborations Reward teams Build partnerships multidisciplinary, multi-institutional, multi-organizational collaborations Inter-institutional organization and communication• Operations and infrastructure Core – administration, structure, organization, informatics, education, data quality Support development/optimization of assays and tests; HQP to ensure standardization, regulatory compliance Quality control for specimen collection, storage and analysis and data – Reduce variability across samples, patients and time – Improve biomarker interpretation 35
  36. 36. My Biases and Beliefs• The integration of biospecimens with reliable clinical data is critical• Highest quality biospecimens are collected on standardized protocols for prespecified purpose(s) and maintained in central facilities with appropriate quality control/quality assurance.• Highest quality clinical data are collected in randomized controlled clinical trials.• Highest quality biomarker studies are evaluated in clinical trials well supported hypothesis well evaluated assays appropriate biospecimens with results correlated to appropriate clinical outcomes with statistical design that provides certainty in the results.• The specific resources to conduct high quality biospecimen research must be available. 36
  37. 37. My Biases and Beliefs• Clinical research (and life) is a series of compromises some of which are worth making and some of which are not. 37