title: Progress in strategies for dosage regimen individualization.
abstract: Several findings suggest that patient outcome would be improved with individualized doses. The aim of this paper is to describe major approaches, methods and underlying basic foundations implemented, in clinical practice, for dosage individualization. Also we propose a new method codified by kinetic nomograms as reliable alternative to traditional Bayesian methods. Clinical and simulation data were reported to evaluate performances of the proposed methods. Real examples of therapeutic drug monitoring were selected. Bayesian methods were used to individualize high-dose methotrexate rate infusion and amikacin dosage regimen, and kinetic nomograms to adjust sirolimus doses. 1) Using only few measurements, Bayesian method resulted in accurate estimates of individual pharmacokinetic parameters of high dose methotrexate infusion. Targeting a pre-defined end-of-infusion level, infusion rate was individualized according to the previously obtained pharmacokinetic parameters. 2) With the same reasoning, individual pharmacokinetic parameters of amikacin were obtained by Bayesian estimation using three individual samples. Subsequent dosage adjustment allowed achievement of therapeutic goals at steady state. 3) Without computing individual pharmacokinetic parameters, nor using pharmacokinetic software, kinetic nomograms steered individual sirolimus blood levels within its therapeutic window with only two samples and in the first week after starting treatment. This contribution relates traditional Bayesian methods developed in 80's but not yet fully integrated in clinical context because of their complexity. The contribution focuses on recent developments based on population approaches, rendering the dosage adjustment methodology a simple and quick bedside application.
authors.affiliation: Aix-Marseille Univ, UMR MD3, 13385 Marseille, France.
authors.forename: Hafedh
authors.initials: H
authors.lastname: Marouani
authors.forename: Christian
authors.initials: C
authors.lastname: Woloch
authors.forename: Stephan
authors.initials: S
authors.lastname: Benay
authors.forename: Nicolas
authors.initials: N
authors.lastname: Frances
authors.forename: Athanassios
authors.initials: A
authors.lastname: Iliadis
mesh_terms: D000583:Amikacin / Q000008:administration & dosage / Q000009:adverse effects / Q000493:pharmacokinetics; D001499:Bayes Theorem; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D016903:Drug Monitoring / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D008727:Methotrexate / Q000008:administration & dosage / Q000009:adverse effects / Q000493:pharmacokinetics; D008954:Models, Biological; D015233:Models, Statistical; D049451:Nomograms; D057285:Precision Medicine / Q000379:methods* / Q000706:statistics & numerical data; D020123:Sirolimus / Q000008:administration & dosage / Q000009:adverse effects / Q000493:pharmacokinetics
chemical_list: D000583:Amikacin; D020123:Sirolimus; D008727:Methotrexate