Abstract
Treatment of High-grade serous ovarian cancer (HGSOC) is often ineffective due to frequent late-stage diagnosis and development of resistance to therapy. Timely selection of the most effective (combination of) drug(s) for each patient would improve outcomes, however the tools currently available to clinicians are poorly suited to the task. We here present a computational simulator capable of recapitulating cell response to treatment in ovarian cancer. The technical development of the in silico framework is described, together with its validation on both cell lines and patient- derived laboratory models. A calibration procedure to identify the parameters that best recapitulate each patient's response is also presented. Our results support the use of this tool in preclinical research, to provide relevant insights into HGSOC behavior and progression. They also provide a proof of concept for its use as a personalized medicine tool and support disease monitoring and treatment selection.