Keywords: optimal dosing, NSCLC, pharmacokinetics, cancer evolution
Establishing an optimal drug administration schedule is a critical procedure in the treatment of cancer, but it has become a strenuous task in cancer research. Furthermore, combination therapy is routinely used due to the emergence of multiple resistant mechanisms in tumor cells, and this increases the number of possible drug dosing schedules. Hence, in-silico clinical trials are an inexpensive method to find an optimal drug regimen. Here, we incorporate data on drug kinetics in serum (pharmacokinetics, PK) with an evolutionary dynamics model based on multi-type branching processes, to predict cancer growth under specific dosing regimens in order to select the most favorable dosing schedule. We expanded PK models to include effects explained by the patients’ clinical characteristics and inter-patient variability using a non-linear mixed effects framework. Next, we applied our approach to a phase-1b clinical trial (TATTON) administering osimertinib and selumetinib to EGFR-mutant lung cancer patients. We analyzed tumor growth of simulated patients under three trial arms used in TATTON and under other proposed dosing schedules, and compared these proposed schedules to the original schedules from TATTON. We observed a wide-ranging distribution of relative improvements, with few subjects (whose PK predictors were evaluated at the median population values) performing worse under the recommended schedules. Most subjects older than the median population age and heavier than the median population weight had remarkable relative improvements under the proposed schedules, demonstrating the importance of adjusting for patient PK predictors for optimal dosing. We propose that researchers can optimize dosing schedules at the individual level by obtaining the clinical data and PK parameters of each patient after a small time period under a traditional dosing schedule, and then change drug administration by selecting the most promising schedule based on our model’s predictions.