A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions.

ABSTRACT: Drug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of pathogen metabolic heterogeneity, changes in the growth environment, drug treatment order, and time interval. To address these limitations, we present a comprehensive approach that uses genome-scale metabolic modeling and machine learning to guide combination therapy design. Our mechanistic approach (a) accommodates diverse data types, (b) accounts for time- and order-specific interactions, and (c) accurately predicts drug interactions in various growth conditions and their robustness to pathogen metabolic heterogeneity. Our approach achieved high accurac

SUBMITTER: Chung CH 

PROVIDER: S-EPMC9396445 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature