Model-supported patient stratification using multi-objective synergy optimization in combination therapy
Irina Kareva, Founder and Principal, Quercus Insights
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.
The challenge of stratifying patients for combination therapy is both technically demanding and clinically crucial. We build upon our previous framework for identifying Pareto optimal doses, which balance synergy of efficacy and potency (a measure of toxicity) such that no further improvement is possible in one without detriment to the other. We apply the methodology in the context of a combination of an immune checkpoint inhibitor and an antiangiogenic agent and extend it to address interpatient heterogeneity. We demonstrate that depending on patient-specific drug sensitivities, different regimens may be Pareto optimal for different subgroups. We also highlight that there exist subpopulations for whom no meaningfully efficacious combinations exist, suggesting that these individuals are not good candidates for this drug combination. In situations where measurable biomarkers are unavailable, we propose an initiation protocol with explicit, practical criteria, allowing estimation of patient-specific sensitivities to both monotherapies and combination therapy. This approach provides a potential way to both find the right combination therapy for a patient, and to find the right patient for existing therapy.