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

Introduction. Carbapenem-resistant Acinetobacter baumannii (CRAB) represents a critical healthcare threat with limited treatment options, particularly challenging in Southeast Asia where resistance rates exceed 65%.Hypothesis/Gap Statement. 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.Aim. To evaluate the efficacy of colistin-meropenem-ampicillin/sulbactam combinations at standard dosages and develop a predictive mathematical model for synergistic interactions against CRAB.Methodology. 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.Results. All isolates demonstrated high-level meropenem resistance (MICs 32-≥128 µg ml-1) and ampicillin/sulbactam resistance (98.4% with MICs≥64/32 µg ml-1) but remained intermediate to colistin (MICs 0.0625-0.25 µg ml-1). The triple combination achieved 100% synergy at standard ampicillin/sulbactam doses (8/4 µg ml-1). Our log-transformed power model accurately predicted synergistic interactions (R²=0.928) using the equation log(FIC) = -2.52+(-1.02) × 1/√Mero+0.43×1/√Col+4.32×1/√As.Conclusion. 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.