霉酚酸
医学
药代动力学
狼疮性肾炎
治疗药物监测
协变量
人口
霉酚酸酯
曲线下面积
统计
数学
药理学
内科学
移植
疾病
环境卫生
作者
Kévin Koloskoff,Ritika Panwar,Manish Rathi,Sumith K. Mathew,Aman Sharma,Pierre Marquet,Sylvain Bénito,Jean‐Baptiste Woillard,Smita Pattanaik
出处
期刊:Therapeutic Drug Monitoring
[Ovid Technologies (Wolters Kluwer)]
日期:2024-05-09
卷期号:46 (5): 567-574
标识
DOI:10.1097/ftd.0000000000001213
摘要
Background: Mycophenolic acid is widely used to treat lupus nephritis (LN). However, it exhibits complex pharmacokinetics with large interindividual variability. This study aimed to develop a population pharmacokinetic (popPK) model and a 3-sample limited sampling strategy (LSS) to optimize therapeutic drug monitoring in Indian patients with LN. Methods: Five blood samples from each LN patient treated with mycophenolic acid were collected at steady-state predose and 1, 2, 4, and 6 hours postdose. Demographic parameters were tested as covariates to explain interindividual variability. PopPK analysis was performed using Monolix and the stochastic approximation expectation-maximization algorithm. An LSS was derived from 500 simulated pharmacokinetic (PK) profiles using maximum a posteriori Bayesian estimation to estimate individual PK parameters and area under the curve (AUC). The LSS-calculated AUC was compared with the AUC calculated using the trapezoidal rule and all the simulated samples. Results: A total of 51 patients were included in this study. Based on the 245 mycophenolic acid concentrations, a 1-compartmental model with double absorption using gamma distributions best fitted the data. None of the covariates improved the model significantly. The model was internally validated using diagnostic plots, prediction-corrected visual predictive checks, and bootstrapping. The best LSS included samples at 1, 2, and 4 hours postdose and exhibited good performances in an external dataset (root mean squared error, 21.9%; mean bias, −4.20%). Conclusions: The popPK model developed in this study adequately estimated the PK of mycophenolic acid in adult Indian patients with LN. This simple LSS can optimize TDM based on the AUC in routine practice.
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