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

• In vitro studies are essential for the evaluation of new antibiotic treatments. • Animal models are an important link between in vitro data and clinical testing. • Market authorization and dosing guidelines rely on preclinical pharmacokinetic/pharmacodynamic (PK/PD) studies. • Mathematical modeling can optimize study design and translation of preclinical data. • More clinical trials for optimal dosing are warranted but are difficult to perform. Appropriate dosing of antibiotics is key in the treatment of bacterial infections to ensure clinical efficacy while avoiding toxic drug concentrations and minimizing emergence of resistance. As collection of sufficient clinical evidence is difficult for specific patient populations, infection types and pathogens, market authorization, dosing strategies and recommendations often rely on data obtained from in vitro and animal experiments. The aim of this review is to provide an overview of commonly used preclinical infection models, including their strengths and limitations. In vitro, static and dynamic time-kill experiments are the most frequently used methods for assessing pharmacokinetic/pharmacodynamic (PK/PD) associations. Limitations of in vitro models include the inability to account for the effects of the immune system, and 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 in vitro data and the clinical situation. However, there are differences in pathophysiology, immunology, and PK between species. Mathematical modeling in which 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. [ABSTRACT FROM AUTHOR]