Note: Most research in resistance has been studied in isolation, not accounting for the complexity tumor heterogeneity and the patient heterogeneity. This model was presented at one of the plenary sessions, considers the complex pharmacokinetics and variability of drug concentration in patients, specifically the change in concentrations of multiple drugs over a period of time, to predict tumor evolution under different treatment schedules. They found that the concentration changes over time due to the patient’s metabolism and when or how often the drug is administered. The mathematical model for this study was presented at the plenary session at AACR.
Despite the clinical success of the third-generation EGFR inhibitor osimertinib as a first-line treatment of EGFR-mutant non-small cell lung cancer (NSCLC), resistance arises due to the acquisition of EGFR second-site mutations and other mechanisms, which necessitates alternative therapies. Dacomitinib, a pan-HER inhibitor, is approved for first-line treatment and results in different acquired EGFR mutations than osimertinib that mediate on-target resistance. A combination of osimertinib and dacomitinib could therefore induce more durable responses by preventing the emergence of resistance. Here we present an integrated computational modeling and experimental approach to identify an optimal dosing schedule for osimertinib and dacomitinib combination therapy. We developed a predictive model that encompasses tumor heterogeneity and inter-subject pharmacokinetic variability to predict tumor evolution under different dosing schedules, parameterized using in vitro dose-response data. This model was validated using cell line data and used to identify an optimal combination dosing schedule. Our schedule was subsequently confirmed tolerable in an ongoing dose-escalation phase I clinical trial (NCT03810807), with some dose modifications, demonstrating that our rational modeling approach can be used to identify appropriate dosing for combination therapy in the clinical setting. Read more.