R Bruno, A Iliadis, R Favre, N Lena, A M Imbert, J P Cano
{"title":"Dosage predictions in high-dose methotrexate infusions. Part 2: Bayesian estimation of methotrexate clearance.","authors":"R Bruno, A Iliadis, R Favre, N Lena, A M Imbert, J P Cano","doi":"10.1089/cdd.1985.2.277","DOIUrl":null,"url":null,"abstract":"<p><p>Population pharmacokinetics of methotrexate (MTX) was evaluated from intravenous test-dose (TD) data (n = 20 corresponding to 174 measured samples). Bayesian prediction of MTX clearance from TD experiments combining population data with measured levels (at times 0.5 and 6 h) was found to be feasible in routine situations with good performance (root mean squared error : rmse (precision) = 1.14 1.h-1 (11.2%) and mean error : me (bias) = 0.06 1.h-1 (NS) relatively to weighted least-square estimates, n = 50). The precision of Bayesian prediction was comparable to that of the model independent which is used in routine practice and involves 9 measured levels over 30 h, (rmse = 1.35 1.h-1 (10.9%), n = 50). However, the routine method presented a significative bias (me = -0.81 1.h-1, n = 50).</p>","PeriodicalId":77686,"journal":{"name":"Cancer drug delivery","volume":"2 4","pages":"277-83"},"PeriodicalIF":0.0000,"publicationDate":"1985-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1089/cdd.1985.2.277","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer drug delivery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1089/cdd.1985.2.277","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18
Abstract
Population pharmacokinetics of methotrexate (MTX) was evaluated from intravenous test-dose (TD) data (n = 20 corresponding to 174 measured samples). Bayesian prediction of MTX clearance from TD experiments combining population data with measured levels (at times 0.5 and 6 h) was found to be feasible in routine situations with good performance (root mean squared error : rmse (precision) = 1.14 1.h-1 (11.2%) and mean error : me (bias) = 0.06 1.h-1 (NS) relatively to weighted least-square estimates, n = 50). The precision of Bayesian prediction was comparable to that of the model independent which is used in routine practice and involves 9 measured levels over 30 h, (rmse = 1.35 1.h-1 (10.9%), n = 50). However, the routine method presented a significative bias (me = -0.81 1.h-1, n = 50).