Prediction of Optimal Drug Schedules for Controlling Autophagy

TL;DR

This paper develops a mathematical modeling approach to predict optimal drug schedules for controlling autophagy, aiming to improve therapeutic strategies by minimizing drug use while effectively regulating cellular processes.

Contribution

It introduces a novel control engineering framework using nonlinear ODE models to identify optimal drug dosing schedules for autophagy regulation, applicable to multiple drug classes.

Findings

Identified optimal drug schedules for six drug classes and pairs.

Demonstrated the approach's generalizability to different cell signaling networks.

Provided a computational method for therapy optimization based on control theory.

Abstract

The effects of molecularly targeted drug perturbations on cellular activities and fates are difficult to predict using intuition alone because of the complex behaviors of cellular regulatory networks. An approach to overcoming this problem is to develop mathematical models for predicting drug effects. Such an approach beckons for co-development of computational methods for extracting insights useful for guiding therapy selection and optimizing drug scheduling. Here, we present and evaluate a generalizable strategy for identifying drug dosing schedules that minimize the amount of drug needed to achieve sustained suppression or elevation of an important cellular activity/process, the recycling of cytoplasmic contents through (macro)autophagy. Therapeutic targeting of autophagy is currently being evaluated in diverse clinical trials but without the benefit of a control engineering perspective. Using a nonlinear ordinary differential equation (ODE) model that accounts for activating and inhibiting influences among protein and lipid kinases that regulate autophagy (MTORC1, ULK1, AMPK and VPS34) and methods guaranteed to find locally optimal control strategies, we find optimal drug dosing schedules (open-loop controllers) for each of six classes of drugs and drug pairs. Our approach is generalizable to designing monotherapy and multi therapy drug schedules that affect different cell signaling networks of interest.