ArticlePLoS computational biology2017
Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 26 citations in OpenAlex.
- A Semimechanistic Model of the Bactericidal Activity of High-Dose Isoniazid against Multidrug-Resistant Tuberculosis: Results from a Randomized Clinical Trial.American journal of respiratory and critical care medicine · 2021Trial
- Drug Design and Delivery for Intracellular Bacteria: Emerging Paradigms.Drug development research · 2025Review
- Residence Times for Femtomolar and Picomolar Inhibitors of MTANs.Biochemistry · 2023Article
- vCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding.BMC bioinformatics · 2022Article
- Current Approaches of Building Mechanistic Pharmacodynamic Drug-Target Binding Models.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Understanding tolerance to cell wall-active antibiotics.Annals of the New York Academy of Sciences · 2021Review
- Article
- Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones.PLoS computational biology · 2020Article
- Multi-scale modeling of drug binding kinetics to predict drug efficacy.Cellular and molecular life sciences : CMLS · 2020Review
- Updated Italian recommendations for the diagnosis, treatment and follow-up of the first febrile urinary tract infection in young children.Acta paediatrica (Oslo, Norway : 1992) · 2020Review
- Estimating treatment prolongation for persistent infections.Pathogens and disease · 2018Article
- Predicting the dynamics of bacterial growth inhibition by ribosome-targeting antibiotics.Physical biology · 2017Article
- Understanding and overcoming antibiotic resistance.PLoS biology · 2017Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying optimal dosing of antibiotics has proven challenging-some antibiotics are most effective when they are administered periodically at high doses, while others work best when minimizing concentration fluctuations. Mechanistic explanations for why antibiotics differ in their optimal dosing are lacking, limiting our ability to predict optimal therapy and leading to long and costly experiments. We use mathematical models that describe both bacterial growth and intracellular antibiotic-target binding to investigate the effects of fluctuating antibiotic concentrations on individual bacterial cells and bacterial populations. We show that physicochemical parameters, e.g. the rate of drug transmembrane diffusion and the antibiotic-target complex half-life are sufficient to explain which treatment strategy is most effective. If the drug-target complex dissociates rapidly, the antibiotic must be kept constantly at a concentration that prevents bacterial replication. If antibiotics cross bacterial cell envelopes slowly to reach their target, there is a delay in the onset of action that may be reduced by increasing initial antibiotic concentration. Finally, slow drug-target dissociation and slow diffusion out of cells act to prolong antibiotic effects, thereby allowing for less frequent dosing. Our model can be used as a tool in the rational design of treatment for bacterial infections. It is easily adaptable to other biological systems, e.g. HIV, malaria and cancer, where the effects of physiological fluctuations of drug concentration are also poorly understood.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.