Evidence map›Paper›PMID 32061797›Full record

ArticleClinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases2020

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

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PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 2 pooled it
2.1field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 50 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 6 institutions in 2 countries.

N M SmithLaboratory 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.
J R LenhardCalifornia Northstate University, College of Pharmacy, Elk Grove, CA, USA.
K R BoissonneaultLaboratory 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.
C B LandersdorferDrug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia.
J B BulittaCenter for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida, Orlando, FL, USA.
P N HoldenLaboratory 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.
A ForrestSchool of Pharmacy, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, NC, USA.
R L NationDrug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia.
J LiMonash Biomedicine Discovery Institute, Infection & Immunity Program and Department of Microbiology, Monash University, Clayton, Victoria, Australia.
B T TsujiLaboratory 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.
University at Buffalo, State University of New York · USMonash University · AUAustralian Regenerative Medicine Institute · AUCalifornia Northstate University · USUniversity of Florida · USUniversity of North Carolina at Chapel Hill · US

Funding

Novel PK/PD Strategies for Polymyxin Combinations against Gram-negative SuperbugsR01AI111990 · NIAID · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI LI, JIAN, TSUJI, BRIAN · 2014 to 2018
$4.4M
Novel Strategies for Antibiotic Combinations to Combat Gram-negative SuperbugsR01AI148560 · NIAID · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI BULITTA, JURGEN BERND, TSUJI, BRIAN · 2020 to 2024
$3.9M
NIAID NIH HHS R01 AI111990NIAID NIH HHS R01 AI148560
6 · The paper itself

Abstract

objectivesIncreased 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.

methodsA. baumannii (N16870; MIC

resultsMonotherapies resulted in regrowth to ~10

conclusionThis 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.

Indexed as

Machine LearningAcinetobacter baumanniiAcinetobacter InfectionsAnti-Bacterial AgentsCarbapenemsDrug Resistance, BacterialDrug Therapy, CombinationHumansMeropenemMicrobial Sensitivity TestsPolymyxin BAnti-Bacterial AgentsCarbapenemsMeropenemPolymyxin BAcinetobacter baumanniiAntibiotic resistanceCombination therapyGenetic algorithmMachine learningMechanism-based modelMeropenemPharmacodynamicsPharmacometricsPolymyxin

Identifiers

PMID32061797
PMCPMC7587610
OpenAlexW3005744059

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.