Using machine learning to optimize antibiotic combinations: dosing strategies for meropenem and polymyxin B against carbapenem-resistant Acinetobacter baumannii.

ABSTRACT:

Objectives

Increased rates of carbapenem-resistant strains of Acinetobacter baumannii have forced clinicians to rely upon last-line agents, such as the polymyxins, or empirical, unoptimized combination therapy. Therefore, the objectives of this study were: (a) to evaluate the in vitro pharmacodynamics of meropenem and polymyxin B (PMB) combinations against A. baumannii; (b) to utilize a mechanism-based mathematical model to quantify bacterial killing; and (c) to develop a genetic algorithm (GA) to define optimal dosing strategies for meropenem and PMB.

Methods

A. baumannii (N16870; MIC_meropenem = 16 mg/L, MIC_PMB = 0.5 mg/L) was studied in the hollow-fibre infection model (initial inoculum 10⁸ cfu/mL) over 14 days against meropenem and PMB com

SUBMITTER: Smith NM 

PROVIDER: S-EPMC7587610 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature