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A mechanism-based pharmacokinetic/pharmacodynamic model allows prediction of antibiotic killing from MIC values for WT and mutants
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 Sciences, Infectious Diseases.
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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2015 (English)In: Journal of Antimicrobial Chemotherapy, ISSN 0305-7453, E-ISSN 1460-2091, Vol. 70, no 11, 3051-3060 p.Article in journal (Refereed) PublishedText
Abstract [en]

Objectives: In silico pharmacokinetic/pharmacodynamic (PK/PD) models can be developed based on data from in vitro time-kill experiments and can provide valuable information to guide dosing of antibiotics. The aim was to develop a mechanism-based in silico model that can describe in vitro time-kill experiments of Escherichia coli MG1655 WT and six isogenic mutants exposed to ciprofloxacin and to identify relationships that may be used to simplify future characterizations in a similar setting. Methods: In this study, we developed a mechanism-based PK/PD model describing killing kinetics for E. coli following exposure to ciprofloxacin. WT and six well-characterized mutants, with one to four clinically relevant resistance mutations each, were exposed to a wide range of static ciprofloxacin concentrations. Results: The developed model includes susceptible growing bacteria, less susceptible (pre-existing resistant) growing bacteria, non-susceptible non-growing bacteria and non-colony-forming non-growing bacteria. The non-colony-forming state was likely due to formation of filaments and was needed to describe data close to the MIC. A common model structure with different potency for bacterial killing (EC50) for each strain successfully characterized the time-kill curves for both WT and the six E. coli mutants. Conclusions: The model-derived mutant-specific EC50 estimates were highly correlated (r(2) = 0.99) with the experimentally determined MICs, implying that the in vitro time-kill profile of a mutant strain is reasonably well predictable by the MIC alone based on the model.

Place, publisher, year, edition, pages
2015. Vol. 70, no 11, 3051-3060 p.
National Category
Infectious Medicine Microbiology in the medical area
URN: urn:nbn:se:uu:diva-276896DOI: 10.1093/jac/dkv233ISI: 000368245500017PubMedID: 26349518OAI: oai:DiVA.org:uu-276896DiVA: diva2:903671
Swedish Foundation for Strategic Research
Available from: 2016-02-16 Created: 2016-02-16 Last updated: 2016-05-12Bibliographically approved
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.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 215
Pharmacometrics, Pharmacokinetics, Pharmacodynamics, PKPD modeling, ciprofloxacin, colistin, E. coli, antibiotics, time-kill experiments
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
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)
Available from: 2016-05-04 Created: 2016-04-05 Last updated: 2016-05-12

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Khan, David D.Lagerbäck, PernillaCao, ShaLustig, UlrikaNielsen, Elisabet I.Cars, OttoHughes, DiarmaidAndersson, Dan I.Friberg, Lena E.
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Department of Pharmaceutical BiosciencesInfectious DiseasesDepartment of Medical Biochemistry and Microbiology
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Journal of Antimicrobial Chemotherapy
Infectious MedicineMicrobiology in the medical area

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