Speaker
Description
Resistance to targeted therapies remains a challenge in the treatment of metastatic non-small cell lung cancer (NSCLC). Standard Maximum Tolerated Dose (MTD) protocols often accelerate the competitive release of drug-resistant cell populations, leading to treatment failure. Evolutionary therapy (ET) offers an alternative approach, seeking to forestall resistance by exploiting density- and frequency-dependent competition between drug-sensitive and drug-resistant cells. The aggressive nature of NSCLC and clinical toxicity considerations motivate the exploration of dynamic dosing strategies.
In this study, we explore the theoretical efficacy of various treatment strategies in NSCLC using a data-driven mathematical modeling framework. Building on two-population ordinary differential equation (ODE) models validated against longitudinal tumour-burden data from NSCLC patients treated with Osimertinib, a tyrosine kinase inhibitor used as a first-line therapy, we compare different static and dynamic dosing protocols. Specifically, we focus on adaptive protocols where drug dosages are dynamically adjusted based on relative changes in tumour volume observed between clinical assessments. We also analyze how different clinical parameters, such as inter-test intervals and baseline doses, influence the Time to Progression (TTP).
