Pharmacokinetic and pharmacodynamic modeling of antibiotics and resistance

Giving the right antibiotic agent at the right dosing regimen has become a very crucial issue for the treatment of infections. The main aim of this study is to develop pharmacokinetic-pharmacodynamic (PK/PD) models to describe and predict the antimicrobial effect and bacterial resistance to minimize trial and error in dose optimization and drug development. Three projects were performed focusing on moxifloxacin, ceftriaxone and piperacillin/tazobactam combinations, respectively.; In the moxifloxacin project, the in vitro bacterial kill curve experiment was used to evaluate the antiinfective activity and an indirect response sigmoid Emax model was used to fit the obtained data. The PD model was then integrated with a population human PK model by linking the antfnfective activity with the free moxifloxacin tissue concentration. Monte Carlo simulations were performed to find the MIC breakpoints and to evaluate the antimicrobial effects against Streptococcus pneumoniae of two standard dosing regimens. The findings provided evidence that moxifloxacin may be qualified as a rational choice for the treatment of infections caused by Streptococcus pneumoniae.; Similar PK/PD approaches were applied in the ceftriaxone project and provided evidence of the validity of using the once daily administration.; In the piperacillin-tazobactam combinations project, PK/PD modeling clearly demonstrated the important impact of pharmacokinetics of tazobactam on the antimicrobial effect of the combination therapy and could provide rational basis for optimization of the dose ratio in the piperacillin-tazobactam combinations.; In the moxifloxacin and ceftriaxone projects, the patterns and models for the bacteria resistance development were also explored. The results suggested that the inadequate antibiotic concentration in favor of the enrichment in low-level resistant populations should be avoided.; in summary, this study demonstrated that PK/PD modeling and simulation can be used to improve antibiotic development and clinical application by providing a paradigm for the selection of antibiotics and their dosing regimens to maximize the antimicrobial effect and minimize the development of bacterial resistance.