Prediction of Optimal Drug Schedules for Controlling Autophagy View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Afroza Shirin, Isaac S. Klickstein, Song Feng, Yen Ting Lin, William S. Hlavacek, Francesco Sorrentino

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. More... »

PAGES

1428

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-019-38763-9

    DOI

    http://dx.doi.org/10.1038/s41598-019-38763-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1111916048

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30723233


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