Pharmacodynamic Modeling

Determining Optimal Scheduling of Bevacizumab and Pemetrexed/Cisplatin Dosing in Non-Small Cell Lung Cancer via Mathematical Modeling

Bevacizumab-pemetrexed/cisplatin combination therapy (BEV-PEM/CIS) is a first line therapeutic for non-small cell lung cancer (NSCLC). BEV-PEM/CIS has a narrow therapeutic window – the range of dosages which both reduce the size and spread of the cancer and do not lead to overdose or a damaging accumulation of side-effects. Those side-effects include damage to rapidly dividing healthy cells such as bone marrow and blood cell progenitors. For this reason, BEV-PEM/CIS dosages cannot be scaled to treat aggressive NSCLC. Recent literature suggests that administering bevacizumab and pemetrexed/cisplatin sequentially, rather than concomitantly, would greatly improve the efficacy of the combination therapy without leading to additional side-effects. Unfortunately, the optimal gap between bevacizumab and pemetrexed/cisplatin administration in humans has not been determined.

To address this need, we have developed a robust preclinical mathematical model of BEV-PEM/CIS. We then scaled that mathematical model to make a first prediction of optimal BEV-PEM/CIS administration in humans. Below is an interactive three-dimensional surface representing the predicted tumor growth over time with respect to the gap between bevacizumab and pemetrexed/cisplatin administration which we hope will give readers an intuitive sense of the behavior of the model. The predicted optimal gap between bevacizumab and pemetrexed/cisplatin administration is 1.2 days. Dosing sequentially with an optimal gap rather than concomitantly improved predicted therapy efficacy (defined as relative tumor volume reduction) by 89.0% over 85 days of treatment. Mathematical modeling is an indispensable tool for scheduling optimization as it allows the researchers to simulate a large set of in silico experiments and make practical predictions based on those experiments without the need for considerable time and resource investment in in vivo studies. Those predictions can then be used to guide future studies, greatly accelerating drug development and optimization. Read a pre-print of the full paper here!