Guiding model-driven combination dose selection using multi-objective synergy optimization
Irina Kareva, PhD, Associate Director, Quantitative Pharmacology, EMD Serono (US-based Merck KGaA)
Irina Kareva received her PhD in Applied Mathematics for Life and Social Sciences from Arizona State University in 2012. Her research has been focused on mathematical modeling of cancer as an evolving ecological system, with particular focus on applying insights from ecology and evolution to understanding cancer initiation and progression. She did her post-doctoral fellowship at Tufts Medical Center in Boston, prior to joining EMD Serono (US business of Merck KGaA) in 2016, where she is currently an Associate Director in Quantitative Pharmacology Department. Dr. Kareva has co-authored 3 books and over 50 publications, and conducts both applied and basic research. Her current work is focused primarily on fit-for-purpose mechanistic modeling of compounds in immunology and immuno-oncology to enable rational safe and efficacious First-in-Human (FIH) dose projections both for monotherapy and combination therapy.
Despite the growing appreciation that the future of cancer treatment lies in combination therapies, finding the right drugs to combine and the optimal way to combine them remains a nontrivial task. Herein, we introduce the Multi-Objective Optimization of Combination Synergy – Dose Selection (MOOCS-DS) method for using drug synergy as a tool for guiding dose selection for a combination of preselected compounds. This method decouples synergy of potency (SoP) and synergy of efficacy (SoE) and identifies Pareto optimal solutions in a multi-objective synergy space. Using a concenptual combination therapy model, we explore properties of the MOOCS-DS algorithm, including how optimal dose selection can be influenced by the metric used to define SoP and SoE. We also demonstrate the potential of our approach to guide dose and schedule selection using a model fit to preclinical data of the combination of the PD-1 checkpoint inhibitor pembrolizumab and the anti-angiogenic drug bevacizumab on two lung cancer cell lines. The identification of optimally synergistic combination doses has the potential to inform preclinical experimental design and improve the success rates of combination therapies.