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Are PKPD models predictive across bacterial densities and strains - external evaluation of a PKPD model describing longitudinal in vitro data.
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.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
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
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-282278OAI: oai:DiVA.org:uu-282278DiVA: diva2:917235
Available from: 2016-04-05 Created: 2016-04-04 Last updated: 2016-05-12
In thesis
1. 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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