The Impact of QSP Modeling and Simulation: Crystalizing the Facts Ronda K. Rippley, PhD Sandy Allerheiligen, PhD QSP Congress October 21, 2015
Fully Defined Quantitative Systems Pharmacology Honestly don t have a clear picture in my head as to the difference between (t)pkpd and QSP perhaps the latter is more like PBPK (vs compartmental models), i.e., grounded more by biology (and what we think we know) versus empiricism A holistic approach that integrates available and informative data from a multitude of sources to guide intelligent decision-making in drug development Multiscale (subcellular, tissue, disease) mechanistic models systems biology 2011 NIH white paper: QSP aims to understand how drugs modulate cellular networks in space and time and how they impact human pathophysiology Creation of multiscale models that ultimately span knowledge of molecules, cells, tissues, and patients will be particularly critical for evaluating target selection and testing therapeutic proof of concept 2
Quantitative Pharmacology (more empirical, top down, leveraging statistical modeling) Systems Pharmacology (complex, mechanistic models, or bottom-up modeling) Understanding Variability (adherence, ) Comparator Models PK/PD Models 3
What is Quantitative and Systems Pharmacology (QSP)? QSP is an integrative science incorporating relationships among disease, drug characteristics, and individual variability to leverage existing knowledge and guide future research Understanding of Variability (Adherence) Decision Models: Benefit-Risk Comparator Models Mechanistic Disease Models Integrated Models PK/PD Models NIH QSP White Paper, 2011 Developed from NIH Workshops in 2008 and 2010 Right Target Right Drug Right Dose Right Patients 4
Have We Realized the Promise of QSP? How Do We Define Success? Many benefits to our understanding of disease biology, pathophysiology, and the consequences of system perturbations Ultimately we are all here to contribute to improving the course of human health: True north is impacting patient benefit The Advise Your Mom test: does or could Drug X provide a meaningful improvement over less expensive standard of care medicines? Impact derives from exploring questions that lead to decisions: enabling go/no go optimizing nonclinical and clinical plans interplay of patient variability and dosing decisions regulatory approvals 5 http://www.gettyimages.pt/detail/ilustra%c3%a7%c3%a3o/digital-illustrationof-multi-generation-ilustra%c3%a7%c3%a3o-royalty-free/88795453
What s Behind the Failures? Success/impact may rely too heavily on being at just the right place, moving just the right speed when lightning strikes What key learnings? Focus on the questions Foster effective interdisciplinary collaboration Enable integrated, end-toend quantitative framework Management sponsorship 6 http://geekynerfherder.blogspot.com/2012/05/movie-posterart-back-to-future-1985.html
It s Not About the Model: Focus on the Question Since when can weathermen predict the weather, let alone the future? Dosing: What dose and regimen will confer desired efficacy/safety? Translation: Are nonclinical results predictive of clinical effect? Do we understand variability and uncertainty in critical biomarkers? How does this relate to clinical outcomes? Discovery/ Development Issue Communicate What is the Question? Frame and Address Quantitatively Target selection: How much improvement is required in efficacy or safety to truly meet unmet medical need? Patient segmentation: Are there subsets of patients who respond differently? Why? How will we know and when? What are tomorrow s questions? Critical questions need quantitative answers to enable decision making
Successful Implementation Requires Co-ownership Between Experimental and Quantitative Functions Basic Research Pharmaceutical Sciences Safety Toxicology In silico Chemistry ADME Quantitative Pharmacology & Pharmacometrics Statistics Heath Outcomes Informatics Early Clinical Development Late Stage Clinical Development Clinical Operations Critical interplay between experimental design/data generation/interpretation and model development Collaboration Accountability Synergy Modeling Experiments
Failures Result from a Variety of Gaps Gaps in Evidence Surprises Problems Unexpected Outcomes Unmet Medical Need Pre-Clinical R&D Early Development Late-Stage Development Regulatory Review Commercialization Modeling & Simulation Discipline-focused excellence is contributor to formation of walls Allerheiligen S, Grasela T, ASCPT 2015 Cain S. The Rise of the New Groupthink. NY Times. January 13, 2012.
Integration of Modeling Approaches A Critical Yet Exceedingly Difficult Ambition Close interdisciplinary collaboration ensures a thorough understanding of the data and that key questions are being addressed by the models In vitro/vivo pharmacology, biology, clinicians, academia, and regulatory Disease area working groups Dedicated staff and training Translational and biomarker plans in place earlier Lack of collaboration has three important consequences Dueling Models: Team discards them all Persistence of unintended or unrecognized knowledge gaps that increase risk of failure Research plans that rely on usual and customary practice rather than identified needs and requirements Management sponsorship is critically important Allerheiligen S, Grasela T, ASCPT 2015 Cain S. The Rise of the New Groupthink. NY Times. January 13, 2012. 10
Infrastructure Challenge: Can We Implement Models at the Speed of Business? Data curation Tool Hardware Model qualification and validation Which of these challenges can we solve together? 11
Harnessing the Diversity of QSP Disciplines Robust QSP approaches rely on multidisciplinary contributions and the inherent diversity of perspectives and backgrounds assuming you can get past the language and cultural barriers! Qualification and variability understood differently by engineers or systems modelers compared to pharmacometricians Core focus on detailed mechanistic biology (academia) vs. middleout translatability (industry) Does this call for a new way of collaborating among industry, centers of excellence (CROs), and academic centers? Drive to common understanding of approach to models, qualification, and grounding in key questions Seek out collaborations to strengthen integration of disease expertise 12
Past Progress has Focused on M&S in Development Next Up: Discovery and Patient Access Target ID & Validation Candidate Selection Clinical Development Systems biology/pharmacology models: identify new targets, mechanistic description of system, often a definition of QSP In Silico PK Prediction (QSAR) to optimize molecular design and translational PK/PD modeling to select candidates and dose Patient Access PopPK and PK/PD modeling and simulation to understand variability, dose and project efficacy, therapeutic window Decision models to weigh benefit:risk Cost effectiveness, comparator modeling, patient segmentation, understanding adherence 13
Breaking Ground: Impacting Targets and Lifecycle Management Discovery Decisions Pathway/target probability of success Differentiation potential Properties (physchem, PK, PD) for desired outcomes Optimize populations/combinations Rank order molecules Customize Core models for targets of interest Build Mechanistically based QSP models in key therapeutic areas Product Decisions Predict clinical outcomes for potential studies in Life Cycle Management
Some Final Thoughts on Challenges and Opportunities How to truly integrate the various approaches to inform critical questions? How do we qualify systems pharmacology or mechanistic QSP models? How to leverage real world data to truly understand disease and disease progression? How do we incorporate this very different type of data into the large disease models? How can mechanistic QSP models be used to impact regulatory decision making? 15
The Future is Now! http://www.losgatostheatre.com/ Congress Covers Key Themes: What Questions should QSP be Answering? Utilizing a Robust QSP Strategy Harnessing the Added Value of QSP Overcoming Computational Challenges Building a Pre-Competitive Systems Toxicology Culture The future? To be able to truly predict the right target for the right disease for the right drug for the right patient at the right dose 16
Acknowledgments Sandy Allerheiligen Prajakti Kothare Julie Stone Matt Rizk Dan Tatosian Brian Topp Bret Musser Brian Mattioni Dinesh De Alwis Lokesh Jain All of my colleagues in Quantitative Pharmacology & Pharmacometrics at Merck The QSP Congress Organizing Committee 17