Genetic algorithms as a tool for dosing guideline optimisation : application to intermittent infusion dosing for vancomycin in adults
Colin, Pieter J. and Elveld, Douglas J. and Thomson, Alison H. (2020) Genetic algorithms as a tool for dosing guideline optimisation : application to intermittent infusion dosing for vancomycin in adults. CPT Pharmacometrics and Systems Pharmacology, 9 (5). pp. 294-302. ISSN 2163-8306 (https://doi.org/10.1002/psp4.12512)
Preview |
Text.
Filename: Colin_etal_CPT_PSP_2020_Genetic_algorithms_as_a_tool_for_dosing_guideline_optimisation.pdf
Final Published Version License: Download (432kB)| Preview |
Abstract
This paper demonstrates the use of a genetic algorithm (GA) for the optimization of a dosing guideline. GAs are well-suited to derive combinations of doses and dosing intervals that go into a dosing guideline when the number of possible combinations rule out the calculation of all possible outcomes. GAs also allow for different constraints to be imposed on the optimization process to safeguard the clinical feasibility of the dosing guideline. In this work, we demonstrate the use of a GA for the optimization of intermittent vancomycin administration in adult patients. Constraints were placed on the dose strengths, the length of the dosing intervals, and the maximum infusion rate. In addition, flexibility with respect to the timing of the first maintenance dose was included in the optimization process. The GA-based optimal solution is compared with the Scottish Antimicrobial Prescribing Group vancomycin guideline.
ORCID iDs
Colin, Pieter J., Elveld, Douglas J. and Thomson, Alison H. ORCID: https://orcid.org/0000-0002-2354-6116;-
-
Item type: Article ID code: 72452 Dates: DateEvent31 May 2020Published8 May 2020Published Online5 April 2020Accepted14 January 2020SubmittedSubjects: Medicine > Therapeutics. Pharmacology Department: Faculty of Science > Strathclyde Institute of Pharmacy and Biomedical Sciences Depositing user: Pure Administrator Date deposited: 21 May 2020 15:12 Last modified: 28 Nov 2024 01:20 URI: https://strathprints.strath.ac.uk/id/eprint/72452