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Pharmacokinetic-Pharmacodynamic modeling and prediction of antibiotic effects
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
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 [en]
Pharmacometrics, Pharmacokinetics, Pharmacodynamics, PKPD modeling, ciprofloxacin, colistin, E. coli, antibiotics, time-kill experiments
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
URN: urn:nbn:se:uu:diva-282604ISBN: 978-91-554-9550-3 (print)OAI: oai:DiVA.org:uu-282604DiVA: diva2:917780
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
List of papers
1. A mechanism-based pharmacokinetic/pharmacodynamic model allows prediction of antibiotic killing from MIC values for WT and mutants
Open this publication in new window or tab >>A mechanism-based pharmacokinetic/pharmacodynamic model allows prediction of antibiotic killing from MIC values for WT and mutants
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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) Published
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.

National Category
Infectious Medicine Microbiology in the medical area
Identifiers
urn:nbn:se:uu:diva-276896 (URN)10.1093/jac/dkv233 (DOI)000368245500017 ()26349518 (PubMedID)
Funder
Swedish Foundation for Strategic Research
Available from: 2016-02-16 Created: 2016-02-16 Last updated: 2017-11-30Bibliographically approved
2. Are PKPD models predictive across bacterial densities and strains - external evaluation of a PKPD model describing longitudinal in vitro data.
Open this publication in new window or tab >>Are PKPD models predictive across bacterial densities and strains - external evaluation of a PKPD model describing longitudinal in vitro data.
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(English)Manuscript (preprint) (Other academic)
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-282278 (URN)
Available from: 2016-04-05 Created: 2016-04-04 Last updated: 2016-05-12
3. Predicting mutant selection in competition experiments
Open this publication in new window or tab >>Predicting mutant selection in competition experiments
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(English)Manuscript (preprint) (Other academic)
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-282279 (URN)
Available from: 2016-04-05 Created: 2016-04-04 Last updated: 2016-05-12
4. A PKPD model characterizing the combined effects of colistin and ciprofloxacin on MG1655 wild type and a clinical isolate of E. coli
Open this publication in new window or tab >>A PKPD model characterizing the combined effects of colistin and ciprofloxacin on MG1655 wild type and a clinical isolate of E. coli
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(English)Manuscript (preprint) (Other academic)
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-264795 (URN)
Available from: 2015-10-19 Created: 2015-10-19 Last updated: 2016-05-12
5. A pharmacokinetic-pharmacodynamic (PKPD) model based on in vitro time-kill data predicts the in vivo PK/PD index of colistin
Open this publication in new window or tab >>A pharmacokinetic-pharmacodynamic (PKPD) model based on in vitro time-kill data predicts the in vivo PK/PD index of colistin
2016 (English)In: Journal of Antimicrobial Chemotherapy, ISSN 0305-7453, E-ISSN 1460-2091, Vol. 71, no 7, 1881-1884 p.Article in journal (Refereed) Published
Abstract [en]

Objectives: For antibiotics, extensive animal PKPD studies are often performed to evaluate the PK/PD driver for subsequent use when recommending dosing regimens. The aim of this work was to evaluate a PKPD model, developed based on in vitro time-kill data for colistin, in predicting the relationships between PK/PD indices and the bacterial killing previously observed in mice. Methods: An in silico PKPD model for Pseudomonas aeruginosa exposed to colistin was previously developed based on static in vitro time-kill data. The model was here applied to perform an in silico replication of an in vivo study where the effect of colistin on P. aeruginosa was studied in the thigh infection model. Concentration-time profiles of unbound colistin were predicted and used as input to drive the bacterial killing in the PKPD model. The predicted bacterial count at 24 h was related to each of the PK/PD indices and the results were compared with reported observations in vivo. Results: The model was found to adequately predict in vivo results from mice; both in terms of which PK/PD index best correlates to effect (fAUC/MIC) as well as the magnitude needed for a 2 log kill. The fAUC/MIC needed to achieve a 2 log reduction in bacterial counts after 24 h was here predicted to be 9 compared with 31 previously reported in vivo. Conclusions: This study provides further support that PKPD models based on longitudinal data can be a useful tool to make drug development more efficient within the infectious diseases area.

National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-282276 (URN)10.1093/jac/dkw057 (DOI)000383246000017 ()26983860 (PubMedID)
Funder
Swedish Foundation for Strategic Research
Available from: 2016-04-05 Created: 2016-04-04 Last updated: 2017-11-30Bibliographically approved

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