Related Experiment Video
Updated: Jan 29, 2026
Author Spotlight: A Selective Luciferase-Based Assay for Monitoring ATG4B 27 Activity in Cells
Published on: June 30, 2023
Prediction of Optimal Drug Schedules for Controlling Autophagy
Afroza Shirin1, Isaac S Klickstein1, Song Feng2
1Mechanical Engineering Department, University of New Mexico, Albuquerque, NM, 87131, USA.
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.
Insights
Mathematical models predict drug effects on cellular processes like autophagy. This study identifies optimal drug schedules to minimize dosage while maintaining therapeutic effects, aiding therapy selection and drug scheduling.
Area of Science:
- Systems Biology
- Pharmacology
- Control Engineering
Background:
- Cellular regulatory networks exhibit complex behaviors, making drug effects difficult to predict intuitively.
- Mathematical modeling offers a promising approach for predicting drug effects and guiding therapeutic strategies.
Purpose of the Study:
- To develop and evaluate a generalizable strategy for identifying optimal drug dosing schedules.
- To minimize drug dosage required for sustained modulation of cellular processes, specifically (macro)autophagy.
- To apply a control engineering perspective to therapeutic autophagy targeting.
Main Methods:
- Utilized a nonlinear ordinary differential equation (ODE) model of autophagy-regulating kinases (MTORC1, ULK1, AMPK, VPS34).
- Employed methods for finding locally optimal control strategies to determine dosing schedules.
- Evaluated optimal open-loop controllers for six classes of drugs and drug pairs.
Main Results:
- Identified optimal drug dosing schedules for monotherapy and combination therapy.
- Demonstrated a strategy to minimize drug amounts for sustained suppression or elevation of autophagy.
- The approach is generalizable to other cell signaling networks.
Conclusions:
- Optimal control strategies can effectively guide drug scheduling for targeted cellular processes.
- This computational approach aids in optimizing therapeutic interventions by minimizing drug dosage.
- The findings support the integration of control engineering in drug development and clinical application.
Related Concept Videos
01:24Reinforcement Schedules
Once a behavior is learned,...
01:27Autophagy
An autophagic pathway consists of a series of signaling events activated in response to diverse stress and physiological conditions such as food deprivation,...
02:27Predicting Molecular Geometry
01:03Drug Control Governance: Regulatory Bodies and Their Impact
01:03Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
00:48Optimal Foraging