{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T05:28:56Z","timestamp":1769318936347,"version":"3.49.0"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>Recent guidelines for vancomycin have incorporated the use of Bayesian forecasting, reinforcing the need to inform students in pharmacy and clinical pharmacology of its use in therapeutic drug monitoring. The goal was to devise a PharmD research project that could demonstrate to students through simulation and data generation the utility of the Bayesian approach in estimating the pharmacokinetics of gentamicin and vancomycin.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>A series of steps were devised using Microsoft Excel to simulate patient data based on study-derived means and variances, pharmacokinetic modelling, random selection of sparse blood samples, introduce random error into the selected concentrations based on assay variability measure, and finally, inputting of the information into an add-in computer program to find the pharmacokinetic estimates using Bayesian forecasting.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Key findings<\/jats:title>\n                  <jats:p>Excellent correlations were seen between Bayesian estimates and true clearances. Lower assay variability tended to provide better estimates than larger assay variability for gentamicin, and for vancomycin, selecting a sample during the distribution phase and near the trough values tended to provide estimates with less bias and greater precision.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>The approach used was able to demonstrate all aspects involved in Bayesian forecasting, and the results supported its use for these antibiotics.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jpp\/rgad068","type":"journal-article","created":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T22:35:33Z","timestamp":1689978933000},"page":"1378-1387","source":"Crossref","is-referenced-by-count":3,"title":["Use of a common spreadsheet program to demonstrate the ability of Bayesian forecasting to estimate the pharmacokinetic parameters of antibiotics"],"prefix":"10.1093","volume":"75","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5024-2268","authenticated-orcid":false,"given":"Dion R","family":"Brocks","sequence":"first","affiliation":[{"name":"Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta , Edmonton, Alberta ,","place":["Canada"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta , Edmonton, Alberta ,","place":["Canada"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"2024103012281647300_CIT0001","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1128\/AAC.49.1.302-308.2005","article-title":"Pharmacodynamics of cefepime alone and in combination with various antimicrobials against methicillin-resistant Staphylococcus aureus in an in vitro 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