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A PKPD model characterizing the combined effects of colistin and ciprofloxacin on MG1655 wild type and a clinical isolate of E. coli
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)ORCID iD: 0000-0003-3166-9981
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
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(English)Manuscript (preprint) (Other academic)
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-264795OAI: oai:DiVA.org:uu-264795DiVA: diva2:861612
Available from: 2015-10-19 Created: 2015-10-19 Last updated: 2016-05-12
In thesis
1. Study Design and Dose Regimen Evaluation of Antibiotics based on Pharmacokinetic and Pharmacodynamic Modelling
Open this publication in new window or tab >>Study Design and Dose Regimen Evaluation of Antibiotics based on Pharmacokinetic and Pharmacodynamic Modelling
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2015. 85 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 206
Keyword
pharmacometric, optimal design, pharmacokinetics, pharmacodynamics, PKPD, resistance, antibiotics, modeling, time-kill curve, colistin, meropenem, ciprofloxacin, non-linear mixed effects models, bayesian
National Category
Medical and Health Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-264798 (URN)978-91-554-9381-3 (ISBN)
Public defence
2015-12-04, B22, Biomedicinskt centrum (BMC), Husargatan 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2015-11-11 Created: 2015-10-19 Last updated: 2015-11-13
2. Pharmacokinetic-Pharmacodynamic modeling and prediction of antibiotic effects
Open this publication in new window or tab >>Pharmacokinetic-Pharmacodynamic modeling and prediction of antibiotic effects
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Problems of emerging antibiotic resistance are becoming a serious threat worldwide, and at the same time, the interest to develop new antimicrobials has declined. There is consequently a need for efficient methods to develop new treatments that minimize the risk of resistance development and that are effective on infections caused by resistant strains. Based on in silico mathematical models, describing the time course of exposure (Pharmacokinetics, PK) and effect (Pharmacodynamics, PD) of a drug, information can be collected and the outcome of various exposures may be predicted. A general model structure, that characterizes the most important features of the system, has advantages as it can be used for different situations. The aim of this thesis was to develop Pharmacokinetic-Pharmacodynamic (PKPD) models describing the bacterial growth and killing after mono- and combination exposures to antibiotics and to explore the predictive ability of PKPD-models across preclinical experimental systems. Models were evaluated on data from other experimental settings, including prediction into animals. A PKPD model characterizing the growth and killing for a range of E. coli bacteria strains, with different MICs, as well as emergence of resistance, was developed.  The PKPD model was able to predict results from different experimental conditions including high start inoculum experiments, a range of laboratory and clinical strains as well as experiments where wild-type and mutant bacteria are competing at different drug concentrations. A PKPD model, developed based on in vitro data, was also illustrated to have the capability to replicate the data from an in vivo study. This thesis illustrates the potential of PKPD models to characterize in vitro data and their usage for predictions of different types of experiments. The thesis supports the use of PKPD models to facilitate development of new drugs and to improve the use of existing antibiotics.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 56 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 215
Keyword
Pharmacometrics, Pharmacokinetics, Pharmacodynamics, PKPD modeling, ciprofloxacin, colistin, E. coli, antibiotics, time-kill experiments
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-282604 (URN)978-91-554-9550-3 (ISBN)
Public defence
2016-05-28, A1:111a, BMC, Husargatan 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2016-05-04 Created: 2016-04-05 Last updated: 2016-05-12

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Khan, David D.Kristoffersson, Anders N.

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