Mechanism-Based Pharmacodynamic Models of Fluoroquinolone Resistance in Staphylococcus aureus

Abstract

) pharmacokinetic profiles in an in vitro system indicated that the subpopulation-specific estimated maximal killing rate constants were similar for both agents, suggesting a common mechanism of action. We propose two novel pharmacodynamic models that assign mechanisms of action to fluoroquinolones (growth inhibition or death stimulation) and compare the abilities of these models and two other maximum effect models (net effect and MIC based) to describe and predict the changes in the population dynamics observed during our previous in vitro system experiments with ciprofloxacin. A high correlation between predicted and observed viable counts was observed for all models, but the best fits, as assessed by diagnostic tests, and the most precise parameter estimates were obtained with the growth inhibition and net effect models. All models, except the death stimulation model, correctly predicted that resistant subpopulations would not emerge when a high-density culture was exposed to a high initial concentration designed to rapidly eradicate low-level-resistant grlA mutants. Additional experiments are necessary to elucidate which of the proposed mechanistic models best characterizes the antibacterial effects of fluoroquinolone antimicrobial agents.Pharmacodynamic models may prove to be efficient and powerful tools for optimizing antimicrobial dosing to prevent the emergence of resistance if they can accurately predict changes in susceptible and resistant bacterial subpopulations over time as a function of antimicrobial concentration. Such models could potentially be used to narrow the range of in vitro, animal, or clinical trials required for drug development and approval.We previously evaluated the abilities of three maximum effect (E max ) models of increasing complexity, designed for two subpopulations of bacteria with different susceptibilities, to describe and predict the evolution of resistance to ciprofloxacin in Staphylococcus aureus by using pharmacokinetic, viable count, subpopulation, and resistance mechanism data obtained from in vitro system experiments (5). A two-population model with unique growth and killing rate constants for the ciprofloxacin-susceptible and -resistant subpopulations best described the initial killing and subsequent regrowth patterns observed. The model correctly characterized the enrichment of subpopulations with low-level resistance in the parent cultures and confirmed the importance of resistant variants to the emergence of resistance by successfully predicting that resistant subpopulations would not emerge when a low-density culture, with a low probability of mutants, was exposed to a clinical dosing regimen or when a high-density culture, with a higher probability of mutants, was exposed to a transient high initial concentration designed to rapidly eradicate low-levelresistant grlA mutants (5).In subsequent in vitro system experiments in which the same S. aureus strains were exposed to ciprofloxacin, gatifloxacin, and garenoxacin (J. J. Campion and M. E. Evans, Abstr. 11th Int....

Development and Qualification of a Pharmacodynamic Model for the Pronounced Inoculum Effect of Ceftazidime against Pseudomonas aeruginosa

“…At high drug concentrations, the maximum rate constant of bacterial loss is the first-order natural death rate constant k d . This model has been previously described by other authors (11,15,26,44):…”

Semimechanistic Pharmacokinetic-Pharmacodynamic Model with Adaptation Development for Time-Kill Experiments of Ciprofloxacin against Pseudomonas aeruginosa

“…They were first used in oncology and were adapted more recently to antimicrobial activity by differentiating susceptible and resistant subpopulations (14). The growth of the resistant subpopulation during exposition to the antimicrobial agent explains the observed regrowth and thus these models may be interpreted as models of development of resistance (7,15,25). With adaptation models on the other hand, the susceptibility of the bacteria to the antimicrobial agents is modeled to decrease with time and/or drug concentration.…”

“…Analysis of time-kill curves using PK-PD models could allow better characterization of the dynamics of the drug effect with time and concentration compared to simply using the MIC, as demonstrated on several occasions (7,(24)(25)(26). However such in vitro PK-PD experiments have generally been performed using devices allowing exposure of bacteria to variable antibiotic concentrations with time.…”

“…However, although very popular, these approaches present several limits when attempting to select the best dosing regimens, especially when bacterial regrowth is observed after an initial decay, as occasionally observed (7,24,25). A more complex but also more powerful way to assess the efficacy of antimicrobial agents is to use PK-PD modeling approaches and to characterize drug effects over the whole period of time (23).Analysis of time-kill curves using PK-PD models could allow better characterization of the dynamics of the drug effect with time and concentration compared to simply using the MIC, as demonstrated on several occasions (7,(24)(25)(26). However such in vitro PK-PD experiments have generally been performed using devices allowing exposure of bacteria to variable antibiotic concentrations with time.…”

“…Time-kill studies are commonly used to address these issues by exploring the changes of bacteria counts in the presence of different concentrations of antibiotics in vitro, and pharmacokinetic-pharmacodynamic (PK-PD) indexes derived from the MIC are used to quantify the activity of antibiotics against bacteria (22). However, although very popular, these approaches present several limits when attempting to select the best dosing regimens, especially when bacterial regrowth is observed after an initial decay, as occasionally observed (7,24,25). A more complex but also more powerful way to assess the efficacy of antimicrobial agents is to use PK-PD modeling approaches and to characterize drug effects over the whole period of time (23).…”