ArticleAIChE journal. American Institute of Chemical Engineers2015
Modeling Heterogeneous Bacterial Populations Exposed to Antibiotics: The Logistic-Dynamics Case.
Article in AIChE journal. American Institute of Chemical Engineers, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Send more data: a systematic review of mathematical models of antimicrobial resistance.Antimicrobial resistance and infection control · 2018Pooled it
- Rapid Design of Combination Antimicrobial Therapy againstComputers & chemical engineering · 2025Article
- Rapid In Vitro Assessment of Antimicrobial Drug Effect Bridging Clinically Relevant Pharmacokinetics: A Comprehensive Methodology.Pharmaceutics · 2023Article
- Discerning in vitro pharmacodynamics from OD measurements: A model-based approach.Computers & chemical engineering · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
In typical in vitro tests for clinical use or development of antibiotics, samples from a bacterial population are exposed to an antibiotic at various concentrations. The resulting data can then be used to build a mathematical model suitable for dosing regimen design or for further development. For bacterial populations that include resistant subpopulations-an issue that has reached alarming proportions-building such a model is challenging. In prior work, we developed a related modeling framework for such heterogeneous bacterial populations following linear dynamics when exposed to an antibiotic. We extend this framework to the case of logistic dynamics, common among strongly resistant bacterial strains. Explicit formulas are developed that can be easily used in parameter estimation and subsequent dosing regimen design under realistic pharmacokinetic conditions. A case study using experimental data from the effect of an antibiotic on a gram-negative bacterial population exemplifies the usefulness of the proposed approach.
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