Predicting mutant selection in competition experiments with ciprofloxacin-exposed Escherichia coli

Area of Science:

• Microbiology
• Pharmacology
• Mathematical Biology

Background:

• Predicting the competition between antibiotic-susceptible wild-type (WT) and resistant mutant (MT) bacteria is crucial for understanding antibiotic resistance emergence.
• Pharmacokinetic/pharmacodynamic (PK/PD) models are valuable tools for optimizing antibiotic treatment regimens by predicting bacterial response to drug concentrations.

Purpose of the Study:

• To evaluate a mechanism-based PK/PD model's ability to predict in vitro competition between *Escherichia coli* (E. coli) WT and resistant mutants under ciprofloxacin exposure.
• To assess the model's predictive performance across different experimental settings, including static and dynamic time-kill assays and serial passage experiments.

Main Methods:

• Utilized a previously developed mechanism-based PK/PD model.
• Conducted in vitro experiments with *E. coli* WT and three resistant mutants exposed to ciprofloxacin at various concentrations.
• Performed static and dynamic time-kill experiments (measuring CFU/mL up to 24h) and serial passage experiments (measuring strain ratios via flow cytometry).

Main Results:

• The PK/PD model reasonably predicted bacterial growth and killing in most static and dynamic time-kill experiments without parameter re-estimation.
• An adequate fit for 6-day serial passage experiments was achieved after re-estimating growth rates.
• No significant bacterial interaction affecting growth was observed between strains.

Conclusions:

• The study demonstrates the predictive capacity of the PK/PD model for bacterial competition under antibiotic pressure.
• The findings support the application of PK/PD modeling for predicting bacterial kill and resistance selection in diverse clinical and experimental settings.