Effects of different resistance mechanisms on bacterial response to meropenem, ciprofloxacin, and their combination described by next-generation mechanism-based modelling
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Alice E Terrill1, Jessica R Tait1, Kate E Rogers1
1Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia.
Objective:
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.
Methods:
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.
Results:
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.
Conclusions:
Overall, the mechanism-based mathematical model was predictive for >96% of treatments.
Insights
A novel mathematical model accurately predicts Pseudomonas aeruginosa response to antibiotics, even with resistance mechanisms. Combination therapies show synergistic effects, outperforming predictions based solely on MIC values.
Area of Science:
- Microbiology
- Pharmacology
- Mathematical Biology
Background:
- Bacterial resistance mechanisms like AmpC overexpression, OprD porin loss, and MexAB-OprM efflux pump overexpression impact antibiotic efficacy.
- Isogenic strains of Pseudomonas aeruginosa with varying resistance profiles were utilized to study treatment responses.
Purpose of the Study:
- To investigate the impact of distinct bacterial resistance mechanisms on Pseudomonas aeruginosa's response to meropenem and ciprofloxacin.
- To evaluate monotherapy and combination antibiotic treatments against isogenic P. aeruginosa strains.
- To develop and validate a mechanism-based mathematical model for predicting bacterial growth dynamics.
Main Methods:
- Seven isogenic P. aeruginosa strains, including wild-type and strains with specific resistance mutations, were exposed to meropenem and ciprofloxacin.
- Pharmacokinetic/pharmacodynamic indices (%fT>MIC and fAUC/MIC) were calculated for meropenem and ciprofloxacin.
- A mechanism-based mathematical model was developed to describe bacterial count profiles over 72 hours.
Main Results:
- Antibiotic exposures needed for regrowth suppression varied among strains, even with identical Minimum Inhibitory Concentrations (MICs).
- Combination therapies demonstrated synergistic and bactericidal effects in most strains, exceeding predictions from pharmacokinetic/pharmacodynamic indices alone.
- The developed mathematical model accurately described bacterial counts across monotherapies and combinations, accounting for diverse resistance mechanisms.
Conclusions:
- A mechanism-based mathematical model effectively predicted treatment outcomes for over 96% of tested conditions.
- The model's ability to account for multiple resistance mechanisms enhances its utility in predicting antibiotic efficacy.
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