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Modeling the Mechanism of Postantibiotic Effect and Determining Implications for Dosing Regimens
Stockholm University, Faculty of Science, Department of Mathematics.
2009 (English)In: Journal of Mathematical Biology, ISSN 0303-6812, E-ISSN 1432-1416, Vol. 59, no 5, 1416-1432 p.Article in journal (Refereed) Published
Abstract [en]

A stochastic model is proposed to explain one possible underlying mechanism of the postantibiotic effect (PAE). This phenomenon, of continued inhibition of bacterial growth after removal of the antibiotic drug, is of high relevance in the context of optimizing dosing regimens. One clinical implication of long PAE lies in the possibility of increasing intervals between drug administrations. The model describes the dynamics of synthesis, saturation and removal of penicillin binding proteins (PBPs). High fractions of saturated PBPs are in the model associated with a lower growth capacity of bacteria. An analytical solution for the bivariate probability of saturated and unsaturated PBPs is used as a basis to explore optimal antibiotic dosing regimens. Our finding that longer PAEs do not necessarily promote for increased intervals between doses, might help for our understanding of data provided from earlier PAE studies and for the determination of the clinical relevance of PAE in future studies.

Place, publisher, year, edition, pages
2009. Vol. 59, no 5, 1416-1432 p.
Keyword [en]
Antibiotic resistance - Kolmogorov equations - Penicillin binding proteins - Postantibiotic effect
National Category
Mathematics Other Mathematics
URN: urn:nbn:se:su:diva-25711DOI: 10.1007/s00285-009-0249-8ISI: 000269058000007OAI: diva2:200334
Part of urn:nbn:se:su:diva-8477Available from: 2009-02-05 Created: 2009-01-27 Last updated: 2010-07-19Bibliographically approved
In thesis
1. From Penicillin Binding Proteins to Community Interventions: Mathematical and Statistical Models Related to Antibiotic Resistance
Open this publication in new window or tab >>From Penicillin Binding Proteins to Community Interventions: Mathematical and Statistical Models Related to Antibiotic Resistance
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Antibiotic resistance has become a major public health concern and mathematical models are important analytical tools for the understanding, evaluation and prediction of the resistance problem and related control strategies.

The risk of emerging antibiotic resistance and selection has rarely been a concern in the design of antibiotic drug dosing regimens. In the first paper, a selection of antibiotic resistant subpopulations for different antibiotic dosing regimens was studied in vitro. The demonstrated complex relationship was influenced by both the rise of new mutants and a postantibiotic effect (PAE) (continued inhibition of bacterial growth after removal of the antibiotic drug). By constructing a mathematical model that incorporated biologically relevant parameters, we were able to assess the risks of resistance development under different dosing strategies.

In the second paper, the model for PAEs is further developed to determine the implications for different dosing regimens. The result challenges the conventional notion that long PAEs promote extended drug dosing intervals and it allows new hypotheses to be tested experimentally based on the findings from the theoretical framework.

Since PAE experiments often are time-consuming and laborious, very few studies have been reporting variation for this phenomenon. In the third paper, an extension to capture the stochastic behavior of bacterial population growth under drug exposure is made. The stochastic nature of the model is also an important complement to the existing deterministic models on drug dose drug effect relationships.

The last paper describes a controlled clinical intervention study aiming at determining whether the frequency of trimethoprim resistance in E. coli can be decreased by a sudden and drastic reduction in trimethoprim use. In addition to evaluating the intervention effect, the model, given estimated parameters, is also used for predicting other interesting outcomes.

Place, publisher, year, edition, pages
Stockholm: Matematiska institutionen, 2009. 132 p.
antibiotic resistance, mathematical modeling, pharmacokinetics, pharmacodynamics, penicillin binding protein, postantibiotic effect, reversibility, selection, community intervention
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
urn:nbn:se:su:diva-8477 (URN)978-91-7155-809-1 (ISBN)
Public defence
2009-02-27, sal 14, hus 5, Kräftriket, Stockholm, 13:00
Available from: 2009-02-05 Created: 2009-01-27Bibliographically approved

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