Study Design and Dose Regimen Evaluation of Antibiotics based on Pharmacokinetic and Pharmacodynamic Modelling

• Main Author: Kristoffersson, Anders
• Format: Doctoral Thesis
• Language: English
• Published: Uppsala universitet, Institutionen för farmaceutisk biovetenskap 2015
• Subjects: pharmacometric; optimal design; pharmacokinetics; pharmacodynamics; PKPD; resistance; antibiotics; modeling; time-kill curve; colistin; meropenem; ciprofloxacin; non-linear mixed effects models; bayesian
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-264798; http://nbn-resolving.de/urn:isbn:978-91-554-9381-3

Current excessive use and abuse of antibiotics has resulted in increasing bacterial resistance to common treatment options which is threatening to deprive us of a pillar of modern medicine. In this work methods to optimize the use of existing antibiotics and to help development of new antibiotics were developed and applied. Semi-mechanistic pharmacokinetic-pharmacodynamic (PKPD) models were developed to describe the time course of the dynamic effect and interaction of combinations of antibiotics. The models were applied to illustrate that colistin combined with a high dose of meropenem may overcome meropenem-resistant P. aeruginosa infections. The results from an in vivo dose finding study of meropenem was successfully predicted by the meropenem PKPD model in combination with a murine PK model, which supports model based dosage selection. However, the traditional PK/PD index based dose selection was predicted to have poor extrapolation properties from pre-clinical to clinical settings, and across patient populations. The precision of the model parameters, and hence the model predictions, is dependent on the experimental design. A limited study design is dictated by cost and, for in vivo studies, ethical reasons. In this work optimal design (OD) was demonstrated to be able to reduce the experimental effort in time-kill curve experiments and was utilized to suggest the experimental design for identification and estimation of an interaction between antibiotics. OD methods to handle inter occasion variability (IOV) in optimization of individual PK parameter estimates were proposed. The strategy was applied in the design of a sparse sampling schedule that aim to estimate individual exposures of colistin in a multi-centre clinical study. Plasma concentration samples from the first 100 patients have been analysed and indicate that the performance of the design is close to the predicted. The methods described in this thesis holds promise to facilitate the development of new antibiotics and to improve the use of existing antibiotics.