title: Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies.
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.
authors.affiliation: Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.|Centre for Molecular Medicine Norway, Nordic EMBL Partnership, Oslo, Norway.|Department of Pharmacy, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway.
authors.forename: Pia
authors.initials: P
authors.lastname: Abel Zur Wiesch
authors.affiliation: Department of Pharmacy, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway.
authors.forename: Fabrizio
authors.identifier: 0000-0002-3738-3469
authors.initials: F
authors.lastname: Clarelli
authors.affiliation: Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.
authors.forename: Ted
authors.initials: T
authors.lastname: Cohen
mesh_terms: D000900:Anti-Bacterial Agents / Q000008:administration & dosage / Q000493:pharmacokinetics / Q000494:pharmacology / Q000627:therapeutic use; D001424:Bacterial Infections / Q000188:drug therapy* / Q000382:microbiology*; D018407:Bacterial Physiological Phenomena / Q000187:drug effects; D019295:Computational Biology; D024881:Drug Resistance, Bacterial; D006801:Humans; D007700:Kinetics; D008826:Microbial Sensitivity Tests; D008954:Models, Biological
chemical_list: D000900:Anti-Bacterial Agents