Mathematical modelling of synergistic triple-antibiotic therapy against carbapenem-resistant Acinetobacter baumannii at clinically achievable concentrations

<b>Introduction.</b> Carbapenem-resistant <i>Acinetobacter baumannii</i> (CRAB) represents a critical healthcare threat with limited treatment options, particularly challenging in Southeast Asia where resistance rates exceed 65%.<b>Hypothesis/Gap Statement.</b> Current synergy testing methods are labour-intensive and poorly standardized, limiting their clinical application. We hypothesize that synergistic interactions between antibiotics follow predictable mathematical patterns derivable from separate MIC determinations for each antibiotic.<b>Aim.</b> To evaluate the efficacy of colistin-meropenem-ampicillin/sulbactam combinations at standard dosages and develop a predictive mathematical model for synergistic interactions against CRAB.<b>Methodology.</b> A cross-sectional study was conducted using 61 CRAB clinical isolates from a Vietnamese tertiary hospital. MICs were determined using broth microdilution, and synergy was assessed via checkerboard assays. Mathematical models were developed to predict fractional inhibitory concentration (FIC) values from separate MIC determinations for each antibiotic.<b>Results.</b> All isolates demonstrated high-level meropenem resistance (MICs 32-≥128 µg ml<sup>-1</sup>) and ampicillin/sulbactam resistance (98.4% with MICs≥64/32 µg ml<sup>-1</sup>) but remained intermediate to colistin (MICs 0.0625-0.25 µg ml<sup>-1</sup>). The triple combination achieved 100% synergy at standard ampicillin/sulbactam doses (8/4 µg ml<sup>-1</sup>). Our log-transformed power model accurately predicted synergistic interactions (<i>R</i>²=0.928) using the equation log(FIC) = -2.52+(-1.02) × 1/√Mero+0.43×1/√Col+4.32×1/√As.<b>Conclusion.</b> The triple combination achieves universal synergy at standard dosages, potentially reducing toxicity risks. Our mathematical model enables rapid prediction of effective combinations from routine susceptibility tests, offering a transformative approach to optimizing therapy against multidrug-resistant pathogens.

Authors

Institutions

Publication Details

Journal
Journal of Medical Microbiology
Published
2025-11-05
DOI
https://doi.org/10.1099/jmm.0.002086
Primary Topic
Antibiotic Resistance in Bacteria
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mathematical modelling of synergistic triple-antibiotic therapy against carbapenem-resistant Acinetobacter baumannii at clinically achievable concentrations

Trần Thị Ngọc Lắm, Huỳnh Minh Tuấn, Loan Luong, My Dung Jusselme et al.
Journal of Medical Microbiology
Antibiotic Resistance in Bacteria
article

Mathematical modelling of synergistic triple-antibiotic therapy against carbapenem-resistant Acinetobacter baumannii at clinically achievable concentrations

Trần Thị Ngọc Lắm, Huỳnh Minh Tuấn, Loan Luong, My Dung Jusselme, Ngan Truong Thi Pham
article en

Abstract

<b>Introduction.</b> Carbapenem-resistant <i>Acinetobacter baumannii</i> (CRAB) represents a critical healthcare threat with limited treatment options, particularly challenging in Southeast Asia where resistance rates exceed 65%.<b>Hypothesis/Gap Statement.</b> Current synergy testing methods are labour-intensive and poorly standardized, limiting their clinical application. We hypothesize that synergistic interactions between antibiotics follow predictable mathematical patterns derivable from separate MIC determinations for each antibiotic.<b>Aim.</b> To evaluate the efficacy of colistin-meropenem-ampicillin/sulbactam combinations at standard dosages and develop a predictive mathematical model for synergistic interactions against CRAB.<b>Methodology.</b> A cross-sectional study was conducted using 61 CRAB clinical isolates from a Vietnamese tertiary hospital. MICs were determined using broth microdilution, and synergy was assessed via checkerboard assays. Mathematical models were developed to predict fractional inhibitory concentration (FIC) values from separate MIC determinations for each antibiotic.<b>Results.</b> All isolates demonstrated high-level meropenem resistance (MICs 32-≥128 µg ml<sup>-1</sup>) and ampicillin/sulbactam resistance (98.4% with MICs≥64/32 µg ml<sup>-1</sup>) but remained intermediate to colistin (MICs 0.0625-0.25 µg ml<sup>-1</sup>). The triple combination achieved 100% synergy at standard ampicillin/sulbactam doses (8/4 µg ml<sup>-1</sup>). Our log-transformed power model accurately predicted synergistic interactions (<i>R</i>²=0.928) using the equation log(FIC) = -2.52+(-1.02) × 1/√Mero+0.43×1/√Col+4.32×1/√As.<b>Conclusion.</b> The triple combination achieves universal synergy at standard dosages, potentially reducing toxicity risks. Our mathematical model enables rapid prediction of effective combinations from routine susceptibility tests, offering a transformative approach to optimizing therapy against multidrug-resistant pathogens.

Journal of Medical MicrobiologyVol. 74(11)
École nationale des ponts et chaussées (FR), Université Paris-Est Créteil (FR), University of Medicine and Pharmacy at Ho Chi Minh City (VN), University Medical Center HCMC (VN), Hong Bang International University (VN), Van Lang University (VN), Laboratoire Eau, Environnement et Systèmes Urbains (FR)
Openalex Percentile: Top 72%
Antibiotic Resistance in Bacteria
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.