Dosage exploration of meloxicam according to CYP2C9 genetic polymorphisms based on a population pharmacokinetic‐pharmacodynamic model

Abstract

Background Meloxicam, used for treating inflammatory diseases, shows large differences in metabolism according to CYP2C9 genetic polymorphisms; however, there are few studies on dose regimen setting based on quantitative predictions. Objective The aim of this study was to determine the appropriate meloxicam dose regimen for each genotype through population pharmacokinetic‐pharmacodynamic modeling of meloxicam by considering CYP2C9 genetic polymorphisms. Methods For modeling, previously reported pharmacokinetic (plasma concentration)‐pharmacodynamic (inhibition of thromboxane B2 generation) data of meloxicam were collected for CYP2C9 genetic polymorphisms (n = 43). And these data were mainly used in the modeling process. Through simulations of the established models, steady‐state pharmacokinetic‐pharmacodynamic profiles were obtained according to meloxicam multiple exposures for each CYP2C9 genotype, and predictions were made based on dose regimen changes. Results Genetic polymorphisms of CYP2C9 were identified as key covariates that significantly affected pharmacokinetic variability of meloxicam between individuals. The developed meloxicam population pharmacokinetic‐pharmacodynamic model predicted pharmacokinetic results of the 7.5 mg meloxicam administration groups (n = 26) for CYP2C9*1/*1 and *1/*3 as an external validation. The results of model simulation revealed that the differences were 2.39–5.42 times for steady‐state mean plasma concentrations and 1.21–1.71 times for the degree of inhibition of thromboxane B2 generation following multiple exposures for CYP2C9*1/*1 versus *1/*13, *1/*3, and *3/*3. This suggested that thromboxane B2 inhibition following increased plasma exposure to meloxicam differed significantly according to CYP2C9 genetic polymorphisms. The dose of meloxicam in CYP2C9*1/*13, *1/*3, and *3/*3 was randomly adjusted to 1.6–15 mg to approximate the mean thromboxane B2 inhibition for CYP2C9*1/*1 at steady state, the dose intervals varied from 24 h to 48 h. Conclusions The results suggested that clinical dose adjustment of meloxicam would be necessary to account for CYP2C9 genetic polymorphisms and reduce side effects. This study suggests a clearer direction for setting up clinical therapy based on personalized medicine and quantitative predictions for meloxicam.

Is Gender an Important Factor in the Precision Medicine Approach to Levocetirizine?

“…This relationship could be extended and translated into a pharmacodynamic model, allowing for the quantification of the time–drug effect after exposure to levocetirizine. This was structurally consistent with the attempted areas in previous pharmacokinetic and pharmacodynamic co-linkage models [ 13 , 14 , 15 , 16 ].…”

“…Nevertheless, this study had great significance in that it was able to quantitatively explore the pharmacokinetic and pharmacodynamic characteristics of levocetirizine and their effects across genders, which had not been clearly identified previously. In addition, it is important to accelerate precision medicine and present information related to drug efficacy and safety by discovering new covariates that can effectively explain the diversity of levocetirizine pharmacometrics within the population [ 14 , 21 ]. In particular, the fact that the gender factor was an effective covariate in the distribution of levocetirizine to peripheral tissues was a new and progressive discovery.…”

“…Additionally, the established pharmacodynamic model structure was extended and applied to the population pharmacokinetic model of levocetirizine developed in this study. The co-linkage analysis technique of the internal pharmacokinetic dataset-based model and the external pharmacodynamic dataset-based model of levocetirizine based on rational judgment, which was expanded and applied in this study, was similar to the methodological part attempted in past reports [ 13 , 14 ]. Since the pharmacokinetic and pharmacodynamic patterns of the reported data [ 12 ] were sufficiently model-fitted at a reasonable level, no major problems were foreseen in the attempt to expand the pharmacodynamic modeling in this study if significant correlation differences between pharmacokinetics and pharmacodynamics did not occur according to age factors.…”

Modeling population pharmacokinetics of morniflumate in healthy Korean men: extending pharmacometrics analysis to niflumic acid, its major active metabolite

“…The population pharmacokinetic model of morni umate was thoroughly evaluated and validated both visually and numerically using Phoenix NLME and the R programming language (R Core Team). The model was evaluated using widely applicable tools for population-scale model validation (Jeong et al, 2021, Jeong et al, 2022, Jang et al, 2023c, including GOF (including distribution of residuals), visual predictive check (VPC), bootstrapping, and normalized prediction distribution error (NPDE). The approaches for each validation tool are presented in Supplementary Information 3.…”