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

N. M. Smith, J. R. Lenhard, K. R. Boissonneault, C.B. Landersdorfer, J. B. Bulitta, P. N. Holden, A. Forrest, R.L. Nation, J. Li, B. T. Tsuji

Research output: Contribution to journalArticleResearchpeer-review


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; MICmeropenem = 16 mg/L, MICPMB = 0.5 mg/L) was studied in the hollow-fibre infection model (initial inoculum 108 cfu/mL) over 14 days against meropenem and PMB combinations. A mechanism-based model of the data and population pharmacokinetics of each drug were used to develop a GA to define the optimal regimen parameters. Results: Monotherapies resulted in regrowth to ~1010 cfu/mL by 24 h, while combination regimens employing high-intensity PMB exposure achieved complete bacterial eradication (0 cfu/mL) by 336 h. The mechanism-based model demonstrated an SC50 (PMB concentration for 50% of maximum synergy on meropenem killing) of 0.0927 mg/L for PMB-susceptible subpopulations versus 3.40 mg/L for PMB-resistant subpopulations. The GA had a preference for meropenem regimens that improved the %T > MIC via longer infusion times and shorter dosing intervals. The GA predicted that treating 90% of simulated subjects harbouring a 108 cfu/mL starting inoculum to a point of 100 cfu/mL would require a regimen of meropenem 19.6 g/day 2 h prolonged infusion (2 hPI) q5h + PMB 5.17 mg/kg/day 2 hPI q6h (where the 0 h meropenem and PMB doses should be ‘loaded’ with 80.5% and 42.2% of the daily dose, respectively). Conclusion: This study provides a methodology leveraging in vitro experimental data, a mathematical pharmacodynamic model, and population pharmacokinetics provide a possible avenue to optimize treatment regimens beyond the use of the ‘traditional’ indices of antibiotic action.

Original languageEnglish
Number of pages7
JournalClinical Microbiology and Infection
Publication statusAccepted/In press - 12 Feb 2020


  • Acinetobacter baumannii
  • Antibiotic resistance
  • Combination therapy
  • Genetic algorithm
  • Machine learning
  • Mechanism-based model
  • Meropenem
  • Pharmacodynamics
  • Pharmacometrics
  • Polymyxin

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