Using Machine Learning to Design Effective Antimicrobial Dosing Regimens.

Resistant bacterial infections remain a major clinical challenge, often necessitating combination therapy, namely use of two or more antibiotics with different mechanisms of action. However, the systematic design of such therapies is still lacking. This study evaluates machine learning (ML) models as alternatives to heuristic approaches within a previously developed framework for predicting the efficacy of antibiotic combinations under clinically relevant pharmacokinetic conditions. That framework uses longitudinal data on bacterial load, captured through optical density measurements during time-kill assays, and fits a mathematical model to the data. The outcome of this exercise is a set of values for the kill rate induced on the most resistant (least susceptible) bacteria of a bacterial population exposed to antibiotics. The contribution of this work is in feeding these kill rate data points to eight different machine learning modeling methods and comparing predictions both to prior experimental test outcomes and results from heuristic modeling. The study focused on the bacterial pathogen