title: Distinguishing Antimicrobial Models with Different Resistance Mechanisms via Population Pharmacodynamic Modeling.
abstract: Semi-mechanistic pharmacokinetic-pharmacodynamic (PK-PD) modeling is increasingly used for antimicrobial drug development and optimization of dosage regimens, but systematic simulation-estimation studies to distinguish between competing PD models are lacking. This study compared the ability of static and dynamic in vitro infection models to distinguish between models with different resistance mechanisms and support accurate and precise parameter estimation. Monte Carlo simulations (MCS) were performed for models with one susceptible bacterial population without (M1) or with a resting stage (M2), a one population model with adaptive resistance (M5), models with pre-existing susceptible and resistant populations without (M3) or with (M4) inter-conversion, and a model with two pre-existing populations with adaptive resistance (M6). For each model, 200 datasets of the total bacterial population were simulated over 24h using static antibiotic concentrations (256-fold concentration range) or over 48h under dynamic conditions (dosing every 12h; elimination half-life: 1h). Twelve-hundred random datasets (each containing 20 curves for static or four curves for dynamic conditions) were generated by bootstrapping. Each dataset was estimated by all six models via population PD modeling to compare bias and precision. For M1 and M3, most parameter estimates were unbiased (<10%) and had good imprecision (<30%). However, parameters for adaptive resistance and inter-conversion for M2, M4, M5 and M6 had poor bias and large imprecision under static and dynamic conditions. For datasets that only contained viable counts of the total population, common statistical criteria and diagnostic plots did not support sound identification of the true resistance mechanism. Therefore, it seems advisable to quantify resistant bacteria and characterize their MICs and resistance mechanisms to support extended simulations and translate from in vitro experiments to animal infection models and ultimately patients.
authors.affiliation: INSERM U1070, Poitiers, France.
authors.forename: Matthieu
authors.initials: M
authors.lastname: Jacobs
authors.affiliation: INSERM U1070, Poitiers, France.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Grégoire
authors.affiliation: INSERM U1070, Poitiers, France.
authors.forename: William
authors.initials: W
authors.lastname: Couet
authors.affiliation: Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, Florida, United States of America.
authors.forename: Jurgen B
authors.initials: JB
authors.lastname: Bulitta
mesh_terms: D000900:Anti-Bacterial Agents / Q000008:administration & dosage* / Q000493:pharmacokinetics*; D001419:Bacteria / Q000187:drug effects* / Q000378:metabolism*; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D024881:Drug Resistance, Bacterial / Q000187:drug effects / Q000502:physiology*; D008954:Models, Biological; D015233:Models, Statistical; D010599:Pharmacokinetics
chemical_list: D000900:Anti-Bacterial Agents