Translational Modeling

Transcription

1 sbv Improver Symposium October 30, 2013 Athens, Greece Translational Modeling Impact on Critical Decision Making in Drug Development Carolyn Cho, M&S, MSD. Views expressed are of the speaker and do not represent the position of MSD

2 Industry Challenge and Opportunity Cost of failures Cause of failures Probability of clinical phase transition Paul et al., Nat Rev DD. 2010, 9: ; Kola & Landis, Nat Rev DD. 2004, 3: ; Dimasi et al., CPT Many pharmaceutical industry reviews highlight Diminishing ROI across the industry Identification of root cause in order to improve success #1 improvement: increase probability of technical success (pts) at phase II Implicit willingness to embrace all approaches that impact pts Opportunity for M&S to save the day make impact

3 Highest Value Gap: Translation to Clinic Discovery teams do not come forward with a full characterization of clinical relevance Animal models are too unnatural including genetic homogeneity Mechanisms of action do not translate to clinical response Clinical teams do not consider biology Clinical study populations are genetically diverse Clinicians are ruled by regulatory path Translation from Discovery to Clinical establishes a bridge to answer key clinical questions that cannot be fully addressed in a clinical study Does the compound reach the site of action? At that site, is the desired pharmacological effect accomplished? How much modulation of the molecular target gives the desired clinical effect? How do we apply clinical data to select a lead compound and learn from failures? What is the therapeutic window of safety vs. efficacy? Addressing these key questions will increase probability of success in Phase 2

4 The Need for Quantitative Assessment A Motivating Example Are Antibiotics On The Farm Risky Business? by Dan Charles The Salt, U.S. NPR Heated controversy: Antibiotic treatment of livestock Increases health and survival of animals and is good for public food supply Creates public health risk: can generate antibiotic-resistant bacterial strains which can cause severe illness and mortality in people Evidence to support both sides of the argument requires risk:benefit analysis "In order to make effective, science-based decisions, we have to move beyond the 'cans' to actually calculating the probabilities" H. Scott Hurd, Iowa State University, Director, WHO Collaborating Center for Risk Assessment and Hazard Identification in Foods of Animal Origin

5 M&S at MSD Translational PKPD

6 Understanding the Critical Questions M&S at MSD How much improvement is required in efficacy or safety to truly be Best in Class or First in Class? How will we know and when? What dose is required? Are there subsets of patients who respond differently? Why? Commercialization Do we understand variability and uncertainty in critical Phase II and III biomarker? How does it relate to clinical outcomes? Phase I Discovery Based on nonclinical results, can human efficacious dose be predicted? Critical questions need quantitative answers to enable decision making

7 M&S at MSD Is experimentalists and quantitative scientists collaborating to use integrated models that enable key development decisions and optimize patient outcomes. Responsibilities of quantitative scientists Sr. quantitative science managers are decision makers M&S Network resources teams. Includes all quantitative functions across clinical development Quantitative scientists are clinical development team members. Must work as a team member to provide M&S and communicate results scientifically (no black box ) Implement QA standards via technology platform, best practices and review processes Metrics: impact and quality

8 M&S at Merck & Co. Is Expanding Need for Extending Clinical M&S Network Initial quantitative questions were clinical ( confidence in compound, below left). Now they are increasingly around target biology ( confidence in target, below left) M&S tools are available at a variety of pipeline stages (below right) Establishing industry process requires diverse technical training and motivating examples Bai & Abernethy, Ann Rev Pharmacol Toxicol. 2013; 53: Vicini & van der Graaf, CPT. 2013; 93:

9 M&S Expertise: Strength from Diversity Educational Background of M&S Staff Majority of M&S staff are PhDs Clin Pharm / Pharm Sciences Years of Experience of M&S Staff Frequency of Responses Statistics Engineering Bioinformatics / Mathematics Biology / Biochemistry Frequency of Responses years 3-5 years 6-10 years >10 years 0% 1-5% 6-10% Percent of Staff 11-15% 16-20% 21-25% 26-30% 31-35% 36-40% 41-45% 46-50% 51-55% 56-60% 61-65% 66-70% 71-75% 76-80% 81-85% 86-90% 91-95% % J Stone, et. al. J. Clin. Pharmacol. 2010; 50:20S-30S 0% 1-5% 6-10% 11-15% 16-20% 21-25% 26-30% 31-35% 36-40% 41-45% 46-50% 51-55% 56-60% 61-65% 66-70% 71-75% 76-80% 81-85% 86-90% 91-95% % Percent of Staff

10 What is Translational PKPD (TPKPD)? Interplay between quantitative models and experiment design (learn and confirm) TPKPD allows for a framework to integrate all available information, including competitors & lead Strategically, enables informed decisions & improved drug hunting..the integration of in silico, in vitro and in vivo preclinical data with mechanism-based models to anticipate the effects of new drugs in humans and across levels of biological organization. Mager and Jusko CPT 83(6):

11 Use of Biomarkers (BMx) A new product development tool kit - containing powerful new scientific and technical methods such as biomarkers for safety and effectiveness - is urgently needed to improve predictability and efficiency along the critical path from laboratory concept to commercial product. FDA Critical Path Initiative 2004 Courtesy of M. Cleary, BioMarkers, MSD

12 Example 1 of 2 Drug Induced Liver Injury

13 Drug Induced Liver Injury (DILI) Observation limited to long-term drug-based treatment in a large population Is there an early biomarker? Is acute injury predictive? The concordance between clinical trial toxicities & animal toxicities is ~71% (150 compounds tested) The 2 clinical toxicities with the poorest pre-clinical correlation: (a) Liver transaminase signal - 55% concordance 17/31 (b) Hypersensitivity reactions - 36% concordance 5/14 DILI is multi-factorial Epigenetic Physiological Olson H, et al. (2000) Regulatory Toxicology and Pharmacology 32, Immune Metabolic BioActivaton DILI Hepatic Transport Mitochondrial Courtesy of M. Cleary, BioMarkers, MSD

14 Bioactivation signature discovered in rat liver mrna bioactivation assay developed through collaboration of I&A, DMPK, and SALAR Identified signature in rat liver by comparing compounds with specific DILI propensities. Signature detects 40% of clinical DILI compounds not detected by traditional preclinical assays with 100% specificity (67 commercial compounds tested to date). Assay being performed on all PCC candidates. Signature derivation for other DILI mechanisms (transport, mitochondrion, immune) in progress. Compounds with Human DILI BA Positive Courtesy of M. Cleary, BioMarkers, MSD

15 Proteomic-Based Profiling Alternative approach: proteomic profiling Standardization of experimental protocol Standardization of analytical approaches SBV Improver Alexopoulas et al., Mol Cell Prot. 2010; 9:

16 Imaging-Based Profiling Alternative approach: high content screening (HCS) profiling Standardization of cell culture Selection of parameters Xu et al., Toxicol Sci. 2008; 105:97-105

17 Translational Safety: Hamner Institute Simulation of Drug-Induced Liver Injury (DILI-Sim) Mechanistic model originally developed by Entelos, translated to Matlab-based format Now hosted by the Hamner Institute at Duke University Academic-industry consortium Questions of interest: Can pathways identified from profiling inform mechanisms of importance? How to use profiling data to inform parameter estimates?

18 Government-Based Consortia FDA-EMA hosted safety consortium C-Path Consortium-led Predictive Safety Consortium German consortium Virtual Liver

19 Impact / Learning Critically important for all stakeholders: patients, regulators, developers, researchers Critical need: standards and qualification Multiple approaches being taken Molecular profiling Image-based profiling Physiological mechanism modeling Emerging consensus that profiling methods will be most informative approach Drivers: U.S. FDA and other governmental regulatory agencies Major barrier: Analysis-ready data sharing

20 Example 2 of 2 Osteoporosis

21 Osteoporosis Osteoporosis results from an imbalance between bone resorption and bone formation favoring bone resorption Low bone mineral density (BMD) accepted as biomarker of skeletal integrity and risk of fracture Microarchitectural deterioration thought to be more directly relevant Therapeutic approaches to osteoporosis Bisphosphonates inhibit osteoclast activity. Can have GI side effects Cathepsin K (CatK), expressed primarily in osteoclasts, is an attractive target for selective inhibition Courtesy of M. Cleary, BioMarkers, MSD

22 Is there a Reliable Biomarker for Bone Fracture? Imaging assessment of BMD Dual energy X-ray Absorptiometry (DXA) Lumbar BMD and bone strength correlated in vitro Total hip Cheng et al., Bone 1997 Courtesy of M. Cleary, BioMarkers, MSD

23 Is there a Reliable Biomarker for Bone Fracture? Finite Element Analysis of Radius Images Demonstrates Increased Bone Strength with Odanacatib in Rhesus Displacement High resolution image Computational estimate of bone strength Peak Stress (MPa) - Experimental Validation of FEA strength estimates with ex vivo mechanical testing ALN ODN VEH INT y = 1.20x R 2 = 0.90 Using Validated FEA procedure Estimated failure load (N) vehicle alendronate odanacatib (low) (HR-pQCT ultradistal radius) FEA estimate Model means and relative standard errors p < 0.01 p < 0.05 percent change from estimated baseline mean Peak Stress (MPa) - Ex-Vivo FEA Cabal et al. 2011, 3 rd Joint ECTS-IBMR Meeting Month FE-Estimated Peak Load increased 27% for ODN, 7% for ALN, and unchanged for VEH after 18 months of treatment

24 Finite Element Simulation of Fall Loading ORRIS QCT Data Cabal et al. 2010, ASMBR

25 Impact / Learning Bone strength is not a registration end point It provides mechanistically-based deep phenotyping Modeling work started in early phase and developed with the program. Physiological model will support filing Highlights general need for physiological model qualification guidance Bone model qualified via peer-review publication Focus on fit-for-purpose Should such models be verified? Verification requires reproducibility: public repositories and crowd sourcing efforts (DDMORE, VHP, SBV) Position paper in progress

26 Example 2 of 2 Osteoporosis

27 Challenges for Translational M&S Communication: internal industry, academia, regulators Generation of useful preclinical data: optimal endpoints, PK, time course Early engagement with regulatory at IND stage Availability of standard methodology, models, best practice Four pillars of M&S: which is systems biology? Biostatistics Biometrics / Pharmacometrics / PKPD modeling systems pharmacology Molecular profiling & pathway modeling systems biology Physiological mechanism modeling systems biology Readiness to accept model-based recommendations Common understanding, comfort in underlying assumptions, to make regulatory decisions in short time Credibility of models community generated qualification Moving from advisory to regulatory e.g. EMA

28 Applying tm&s to Drug Development Decision Making Question driven translational M&S: Enables team s ability to understand and explore impact of variability and uncertainty Integrated, multidisciplinary models developed through quantitative disciplines and the team. Ultimate power is in ability to extrapolate controlled perturbations to clinical effect and explore thousands of simulated trials/ outcomes Models should be continuously developed over time