Innovations and Combinations for Novel Anticancer Strategies

Transcription

1 Innovations and Combinations for Novel Anticancer Strategies Axel-R. Hanauske, MD, Ph.D., MBA Senior Medical Fellow Oncology Early Phase Eli Lilly and Company 2017 Eli Lilly and Company

2 Disclosure Information I am an employee of Eli Lilly and Company. I will not discuss off-label or investigational treatments in my presentation. 6/19/ Eli Lilly and Company 2

3 Oncology Drug Development: The Traditional Rocket Model Lead Optimization Pre- Clinical Develop -ment Phase I Phase II Phase III Registration Global Launch Global Optimization Candidate Development Commercialization 2017 Eli Lilly and Company

4 Cost of Cost Development of (Millions (Million $) $) Cost of Developing a New Drug Over Time $ $ $ 54 $ Adapted from: DiMasi et al. J Health Econ. 10: 107, 1991; Tufts Center for the Study of Drug Development; data for 2000 and 2015 are estimates

5 R&D Efficiency by Year Jack W. Scannell et al; Diagnosing the decline in pharmaceutical R&D efficiency: Nature Reviews/Drug Discovery, Volume 11, March Eli Lilly and Company

6 How Does Anticancer Drug Late Development Compare? Probability of Success in Phase III studies by therapeutic area: Autoimmune: 63% Respiratory: 61% Endocrinology: 60% Infectious Disease: 55% Neurology: 55% Cardio-Vascular: 46% Oncology: 34% M.Silverman; BioStrategics Consulting GmbH, 2012; retrieved 11-Nov-2012; 2017 Eli Lilly and Company

7 Estimated New Cases in USA in 2010 Cancer type Estimated New Cases 2010 # Pipeline Products Phase II + III Cancer type Lung (123) 1,2 Non- Hodgkin s Lymphoma Estimated New Cases 2010 # Pipeline Products Phase II + III (92) Prostate Kidney Breast (82) Thyroid Colon + Rectal and # of Pipeline Compounds US 2015: 836 drugs+vaccines in clinical development 3137 clinical trials active Endometrial (Ovarian) (48) Bladder Pancreatic Melanoma (skin: 53) Leukemia (106) informedrx Drug Information Services. RxOutlook; American Cancer Society. Cancer Facts and Figures, 2010; 1 Buffery Am Health & Drug Benefits 8: 216, Accessed: June Eli Lilly and Company

8 How to Develop the Right Drug for the Right Patient in a Disease with Heterogeneity at Multiple Different Levels Define diversity in disease Genetic somatic & germline Functional Tumor components Segregate by defined criteria Target/pathways Biologic processes chromosomes mutations copy changes Predict clinical benefit of a drug Signatures of target dependence Host genetic makeup overexpression protein IHC on tissue arrays

9 Types of Innovative Trial Designs Master Protocol Basket Trials Umbrella Trials Platform Trials Either Or or Neither Strategy trials Bayesian Adaptive Platform Designs etc.

10 Master Protocol Design

11 Hallmarks of Master Protocols One scoping study protocol Beneath: Separate parallel drug trials with Different biomarkers Different treatment designs Require collaboration of many stakeholders: Academic Industry Diagnostic Pharmaceutical Regulatory

12 Why Master Protocols and not Separate Studies? Enhanced genomic screening efficiency Inclusion of wide array of molecular subtypes willingness of patients and HCPs to participate Deletion/insertion of new subprotocol by amendment instead of completely new protocol development and faster accrual c/w separate studies More rapid clinical development Better streamlined clinical development

13 Master Protocols: Basic Design Renfro Ann Oncol October 11, 2016 Substudies often with low sample size: Single arm trials > focus on large efficacy signal Randomized trial > focus on large treatment effect (vs. marker-negative or SOC)

14 Master Protocols: Higher Efficiency than separate studies if Use of common genomic platform or diagnostic tests Screening for variants of multiple genomic targets in each tumor sample (requires sufficient tumor material) Rapid inclusion of patients based on screening results Organizational setting that allows for addition/deletion of subprotocols

15 Master Protocols: Faster Clinical Development than separate studies if Upfront Regulatory/Institutional review of Master Protocol: Less administrative delay: Subsequent addition of subprotocols are considered amendments and may be reviewed accordingling Simplified process of deletion/addition of new substudies or changes in substudies No need to put complete Master protocol or unaffected substudies on hold Potential for faster approval by Regulatory Agencies in case of convincing treatment effects

16 Master Protocols: Stakeholder Enthusiasm if Availability of a large number of different substudies Inclusion of all marker negative substudy All patients signing the ICF know there will be a treatment option for them Simplified local processes: IRB approval Contracting

17 Basket Design

18 Hallmarks of Basket Protocols Terminology used differently in literature Pragmatic definition: TUMOR TYPE-AGNOSTIC APPROACH = HISTOLOGY-REPLACING APPROACH Hypothesis: The response to targeted therapy is determined by the underlying molecular variant and (largely) independent of histology Renfro Ann Oncol October 11, 2016

19 Basket Trial: Basic Design Enrollment based on molecular variant Subtrials may consist of different histologies or tumor types Randomization within molecular cohorts is uncommon and may be challenging (different SOCs across tumor types) Usually single-arm substudies, hypothesis-generating objectives Renfro Ann Oncol 11-Oct_ 2016; Billingham Lancet Oncol 17-Feb-2016

20 Drug-Development-Oriented Basket Trial: Potential Design Renfro Ann Oncol October 11, 2016

21 Basket Trials: Pros and Cons Prerequisites: 1. Drug must sufficiently inhibit target 2. Tumor must depend on target Benefits: Access to trial for patients with rare tumors (but must have respective molecular marker) Testing may be done locally Small cohorts (usually single arm) may suffice to detect activity Quick results Challenges: Molecular variant(s) may not be the only driver of tumor Contextual complexities in various histologies Single biomarkers may be inferior to multi-gene signature Structural variants may need to be complemented with functional studies Different tumor types have different prognoses: single primary endpoint (eg ORR) may skew results

22 Basket trial: NCI MATCH ( Molecular Analysis for Therapy CHoice ) Phase II study, Target: patients in 25 substudies (N=30), screen 6000 pts US only, > 1071 sites participating in the NCI s National Cancer Trials Network TTYPES: Advanced, recurrent, refractory solid tumors Lymphoma Multiple Myeloma 24 drugs, including: Afatinib AZD5363 (AKT inh.) Binimetinib Crizotinib Dabrafenib Dasatinib Defactinib AZD4547 (FGFR Inh.) Nivolumab Osimertinib Palbociclib GSK (PI3K-ß inh.) Sunitinib Taselisib Tametinib Trastuzumab emtansine Vismodegib accessed 23-Jan-2017 Co-PEPs: ORR, 6-mo-PFS Goal: increase ORR from 5% to >16% Re-bx allowed to understand temporal molecular development and re-assignment of pts

23 Umbrella Design

24 Hallmarks of Umbrella Protocols Terminology used differently in literature Pragmatic definition: TUMOR TYPE-BASED APPROACH = HISTOLOGY-REFINING APPROACH Hypothesis: The response to targeted therapy is primarily determined by histologic context Renfro Ann Oncol October 11, 2016

25 Umbrella Trials: Characteristics Screening and enrollment before identification of a targetable molecular variant Use of central laboratory for standardized screening Subprotocols may be single-arm or randomized Renfro Ann Oncol October 11, 2016

26 Umbrella Trials: Pros and Cons Benefits: Conclusions are specific for a given tumor type Tumor heterogeneity limited to one tumor type For randomized substudies: Potential to better understand the difference of targeted therapy vs SOC Potential to differentiate between prognostic and predictive markers Easier path to negotiate approval with Regulatory Agencies Challenges: Requires Strong collaboration between academia and industry Defined marker profile, comparability of cohorts (bx, assay, Tx) Feasibility Subclassification into rare populations (particularly with rare cancers to start out with) speed of accrual Randomization requiring a larger sample size may be challenging Appearance of new SOCs during trial conduct changes the environment

27 Umbrella Trial: FOCUS 4 Design Kaplan J Clin Oncol 36, 4562, 2013

28 Platform Design ( Standing Trial )

29 Hallmarks of Platform Protocols Randomized One control arm, several experimental arms Allow for exit and entry of experimental arms Use of Bayesian hierarchical models for decision making Biomarker-positive subpopulations not necessarily separated Adaptive randomization with mid-trial randomization ratio shifts Renfro Ann Oncol October 11, 2016

30 Platform Protocols: Design Randomize Control Exp 1 Exp 2 Exp 3 Exp 4 Potential Advantages: Reduced overall sample size Exp 5 Improved efficiency compared to sets of indivdual two-arm trials 30

31 Platform Design ( Standing Trial ) - Adapted for Early Phase Drug Development -

32 Platform Protocols for Phase I Combination Trials One Master Protocol Includes design for dose-escalation AND expansion cohorts Identical Primary Endpoint(s) for all cohorts One backbone agent Tumor-type defined or open Exit and entry of combination cohorts allowed through amendment Use of Bayesian models for decision making allowed Biomarker-positive subpopulations may be used in combination cohorts

33 Platform Protocols: Design Platform Phase 1 Protocol with Expansion Cohorts Backbone compound + Combo agent 1 Backbone compound + Combo agent 2 Backbone compound + Combo agent 3 Backbone compound + Combo agent 4 Backbone compound + Combo agent 5 Potential Advantages: Increased flexibility to include new scientific evidence into ongoing trial Backbone Increased speed to identify active combination compound regimens + Improved efficiency to identify active combination regimens Combo agent 6 33

34 Strategy Trials

35 Hallmarks of Strategy Protocols 2-arm randomized trials: non-targeted SOC vs. targeted tx Cross-over in case of PD SHIVA trial: SOC vs. targeted therapy Refractory solid tumor screened: 741 pts Randomized: 195 pts R R Targeted therapy Physician s choice - if PD on targeted tx cross-over to Physician s choice - 10 regimens with 11 targeted agents were studied PEP: PFS RES: mpfs 2.3 vs. 2.0 months (HR 0.88, p=0.41) NCI MPACT: non-targeted therapy > targeted therapy Renfro Ann Oncol October 11, 2016, LeTourneau Lancet Oncol 16: 1324, 2015; Catenacci Lancet Oncol September 2, 2015

36 Strategy Trial: NCI MPACT Design ( Molecular Profiling-based Assignment of Cancer Therapy ) DNA repair, PI3K RAS/RAF/MEK 2:1 Do Chin Clin Oncol 4: 31, 2015

37 NCI MPACT Total of 4 experimental arms Anticipated sample size: 700 pts screened > 180 pts randomized to the initial 4 arms Possibility to add additional cohorts PEPs: ORR, 4-month PFS Trial is ongoing

38 Bayesian Adaptive Trials

39 Hallmarks of Bayesian Adaptive Design Randomized trial with Master Protocol Subprotocols may leave or enter during trial Pts assigned to specific targeted therapy arms based on molecular signature Adaptive change of randomization based on treatment s observed activity randomization probability if activity is observed randomization probability if unpromising activity Requirements to perform well: swift marker assessment quick endpoint to inform adaptive randomization slow accrual rate to ensure subsequent pts benefit from randomization adaptation Renfro Ann Oncol October 11, 2016

40 Bayesian Adaptive Design Notable Trials: BATTLE-1 BATTLE-2 I-SPY2 Renfro Ann Oncol October 11, 2016

41 Bayesian Adaptive Design: BATTLE-1 ( Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE)-1 ) First completed, biomarker-based, adaptive randomized clinical trial (N = 255) Mandatory bx 11 pre-specified biomarkers (After 1 pt in each subgroup) 20 different treatment-by-biomarker subgroups, evaluation of performance of each treatment arm in each marker group Liu Chin Clin Oncol 4: 33, 2015

42 Bayesian Adoptive Design: Pros and Cons Benefits: Identification of potential new predictive biomarkers Enhanced individual ethics through adaptive randomization Greater efficiency compared to stand-alone randomized Phase II trials Real-time biomarker analysis (if possible) Challenges: Prespecification of biomarkers without information whether they are prognostic or predictive Combination of markers may dilute predictive effects of individual markers Rare biomarkers may lead to late start of adaptive randomization Need of short-term outcome measure to guide adaptive randomization Patients with advanced cancer may not be eligible for treatment with all compounds due to medical condition

43 Novel Study Designs: Summary Potential Benefits: Greater efficiency in drug development Faster detection of innovative cancer agents More accurate selection of patients Exposure of less patients to potentially inactive agents Less resource consumption Potential Challenges: Retention of integrity of trial designs Susceptibility for bias Complexity of safety monitoring Robustness of results observed Overestimation of beneficial effect Assessment by Competent Authorities unclear how central Ct approval ( 2018) will handle new designs

44 THANK YOU

45 The Pharmaceutical Industry has Increased its Oncology Efforts EFPIA: A Vision Towards a Life Science Strategy in Europe, Brussels 2014; May 4, 2015

46 # of Pipeline Compounds: USA drugs+vaccines in clinical development 3137 clinical trials active logy-report-2015.pdf Accessed: June