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

Autor: Smith NM; Laboratory for Antimicrobial Pharmacodynamics, University at Buffalo, School of Pharmacy and Pharmaceutical Sciences, Buffalo, NY, USA; New York State Center of Excellence in Life Sciences and Bioinformatics, Buffalo, NY, USA., Lenhard JR; California Northstate University, College of Pharmacy, Elk Grove, CA, USA., Boissonneault KR; Laboratory for Antimicrobial Pharmacodynamics, University at Buffalo, School of Pharmacy and Pharmaceutical Sciences, Buffalo, NY, USA; New York State Center of Excellence in Life Sciences and Bioinformatics, Buffalo, NY, USA., Landersdorfer CB; Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia., Bulitta JB; Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida, Orlando, FL, USA., Holden PN; Laboratory for Antimicrobial Pharmacodynamics, University at Buffalo, School of Pharmacy and Pharmaceutical Sciences, Buffalo, NY, USA; New York State Center of Excellence in Life Sciences and Bioinformatics, Buffalo, NY, USA., Forrest A; School of Pharmacy, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, NC, USA., Nation RL; Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia., Li J; Monash Biomedicine Discovery Institute, Infection & Immunity Program and Department of Microbiology, Monash University, Clayton, Victoria, Australia., Tsuji BT; Laboratory for Antimicrobial Pharmacodynamics, University at Buffalo, School of Pharmacy and Pharmaceutical Sciences, Buffalo, NY, USA; New York State Center of Excellence in Life Sciences and Bioinformatics, Buffalo, NY, USA. Electronic address: btsuji@buffalo.edu.
Jazyk: angličtina
Zdroj: Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases [Clin Microbiol Infect] 2020 Sep; Vol. 26 (9), pp. 1207-1213. Date of Electronic Publication: 2020 Feb 12.
DOI: 10.1016/j.cmi.2020.02.004
Abstrakt: 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 10 8  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 ~10 10  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 SC 50 (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 10 8  cfu/mL starting inoculum to a point of 10 0  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.
(Copyright © 2020 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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