How preclinical infection models help define antibiotic doses in the clinic.

ABSTRACT Appropriate dosing of antibiotics is key in the treatment of bacterial infections to ensure clinical efficacy while avoiding toxic drug concentrations and emergence of resistance as much as possible. Because sufficient clinical evidence for specific patient populations, infection types and pathogens is difficult to collect, market authorization, dosing strategies and recommendations often rely on data obtained from in vitro and animal experiments. This review aims to provide an overview of commonly used preclinical infection models, including their strengths and limitations. In vitro, static and dynamic time-kill experiments remain the most frequently used methods for assessing pharmacokinetic/pharmacodynamic (PK/PD) associations. Limitations of the in vitro models include the inability to account for the effects of immune system, as well as uncertainties in clinically relevant bacterial concentrations, growth conditions and the implications of emerging resistant bacterial populations during experiments. Animal experiments, most commonly murine lung and thigh infections models, are considered a necessary link between the in vitro data and the clinical situation. However, differences in pathophysiology, immunology and PK between species still exist. Mathematical modeling, whereby preclinical data are integrated with human population PK, can facilitate translation of preclinical data to the patient's clinical situation. Moreover, PK/PD modeling and simulations can help in the design of clinical trials aiming to establish optimal dosing regimens to improve patient outcomes.