Terrill AE; Tait JR; Rogers KE; Lee WL; López-Causapé C; Wang X; Song J; Nation RL; Oliver A; Landersdorfer CB

Abstract: This study aimed to investigate the effects of different bacterial resistance mechanisms on the response of isogenic strains of Pseudomonas aeruginosa to meropenem and ciprofloxacin, in monotherapy and combination. Seven isogenic P. aeruginosa strains were used: the PAO1 wild-type reference parent strain, PAΔAD (AmpC overexpression), PAOD1 (OprD porin loss), PAΔmexR (MexAB-OprM overexpression), and strains with two of these mutations (PAΔDMxR, PAOD1MxR, PAOD1ΔD). Each strain was exposed to constant meropenem and/or ciprofloxacin concentrations over 72 h. Pharmacokinetic/pharmacodynamic indices, i.e. the percentage of time the free meropenem concentration exceeded the MIC of the pathogen (%fT˃MIC) and the ratio of the area under the free ciprofloxacin concentration-time curve over 24 h to MIC (fAUC/MIC), were calculated. A novel mechanism-based mathematical model was developed to describe the bacterial count profiles over time. The antibiotic exposures in monotherapy required to suppress regrowth varied between isogenic strains, even when strains had the same MIC; highlighting limitations of relying solely on MIC to inform dosing. Combination therapies, which cannot be predicted by pharmacokinetic/pharmacodynamic indices, were both synergistic and bactericidal at 72 h in five of the seven strains. All viable counts across all strains with monotherapies, combinations, and controls (n = 292 curves) were simultaneously modelled. The model accounted for different resistance mechanisms present across strains, while keeping all drug effect parameters the same between strains. The responses of strains with two resistance mutations were well described by the model developed from strains with a single mutation. Overall, the mechanism-based mathematical model was predictive for >96% of treatments. International journal of antimicrobial agents: 2025 doi: 10.1016/j.ijantimicag.2025.107528 https://pubmed.ncbi.nlm.nih.gov/40320217