Mathematical modeling suggests improved clinical outcomes of second-generation PARP inhibitors with reduced toxicity

High-grade serous ovarian cancer (HGSC) is one of the most lethal gynecological cancers. Recent clinical trials have shown the remarkable benefits of maintenance treatment with Poly-ADP Ribose Polymerase inhibitors, particularly in patients with tumors deficient in homologous recombination DNA repair. However, the large Phase III clinical trials are limited in their ability to evaluate various dosages and treatment durations, and the effects of toxicities and treatment interruptions on clinical outcomes remain unknown. In this study, we developed a computational framework for virtual clinical trials taking into account both tumor dynamics (growth selection and evolution of resistance mechanisms) and clinico-pharmacological features (pharmacokinetics, dosing, hematological toxicity, dose interruptions, and reductions). Our model replicates the clinical outcomes observed in the landmark SOLO-1 clinical trial. The branching process model revealed a heterogeneous patient population with distinct tumor dynamics, highlighting fitness and selection pressure as determinants of a poor response. Through a virtual trial approach, we show that managing toxicity via treatment interruptions or dose reductions does not compromise the clinical benefits of the treatment. Importantly, we present evidence that further reduction of hematological toxicity could significantly improve clinical outcomes in first-line PARPi maintenance treatment in ovarian cancer. SignificancePhase III clinical trials are limited in their capacity to investigate different dosing schedules, and the number of participants is relatively small. Furthermore, the effects of toxicities on trial outcomes are frequently unknown. Virtual clinical trials using a mechanistic mathematical model combined with statistical evaluations can overcome these limitations. In this work, we demonstrate the application of a computational framework for optimizing drug scheduling and testing of the next generation of PARP inhibitors, providing insights to guide future clinical trial design.