Speaker
Description
We explore the practicality of guiding treatment scheduling based on the convexity (or concavity) of dose-response curves, which provides a straightforward comparison of continuous treatment and high-dose / low-dose alternatives. Concave dose-response functions predict that the daily administration of a dose of x may be less efficacious than a regimen that switches equally between 120% of x and 80% of x (every other day). Convex dose-response provide the opposite prediction (high / low dosing is best). However, treatment fails due to the evolution of resistance, indicating that dose-response is changing in time with mutation or plasticity driven resistance mechanisms. Drug holidays have been suggested as re-sensitization method for tumors.
Using mathematical modeling integrated with in vivo data, we predict the dose-dependent rate of resistance onset for targeted therapy, which we show is a concave function of dose. Our integrative modeling framework predicts a trade-off between maximizing response (continuous protocols) and maintaining drug sensitivity (high / low protocols), suggesting re-sensitization is possible. Thus, we propose alternative switching treatment protocols to balance this trade off: continuous followed by high / low (or vice versa), subsequently validated in a non-small cell lung cancer in vivo model. This convexity-based approach to treatment scheduling illustrates the effectiveness of incorporating principles of convexity into protocol design.
