Moataz E Mohamed, Abdelrahman Saqr, Mahmoud Al-Kofahi, Guillaume Onyeaghala, Rory P Remmel, Christopher Staley, Casey R Dorr, Levi Teigen, Weihua Guan, Henry Madden, Julia Munoz, Duy Vo, Bryan Sanchez, Rasha El-Rifai, William S Oetting, Arthur J Matas, Ajay K Israni, Pamala A Jacobson
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This study is the first to evaluate LSSs models performance in the context of EHR.</p><p><strong>Methods: </strong>Adult kidney transplant recipients (n = 84) receiving oral mycophenolate mofetil underwent intensive MPA pharmacokinetic sampling. MPA AUC 0-12hr and EHR were determined. Published MPA LSSs in kidney transplant recipients receiving tacrolimus were evaluated for their predictive performance in estimating AUC 0-12hr in our full cohort and separately in individuals with high and low EHR.</p><p><strong>Results: </strong>None of the evaluated LSS models (n = 12) showed good precision or accuracy in predicting MPA AUC 0-12hr in the full cohort. In the high EHR group, models with late timepoints had better accuracy but low precision, except for 1 model with late timepoints at 6 and 10 hours postdose, which had marginally acceptable precision. For all models, the good guess of predicted AUC 0-12hr (±15% of observed AUC 0-12hr ) was highly variable (range, full cohort = 19%-61.9%; high EHR = 4.5%-65.9%; low EHR = 27.5%-62.5%).</p><p><strong>Conclusions: </strong>The predictive performance of the LSS models varied according to EHR status. Timepoints ≥5 hours postdose in LSS models are essential to capture EHR. 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引用次数: 0
摘要
背景:由于难以测量曲线下面积(AUC),霉酚酸(MPA)的治疗药物监测具有挑战性。目前已开发出用于 MPA 治疗药物监测的有限采样策略 (LSS),但有可能出现无法接受的结果。作者假设 LSS 的预测性能不佳是由于 MPA 肠肝再循环 (EHR) 的变异性造成的。本研究首次评估了 EHR 背景下的 LSSs 模型性能:方法:接受口服霉酚酸酯治疗的成年肾移植受者(n = 84)接受了密集的 MPA 药代动力学采样。测定了 MPA AUC0-12hr 和 EHR。对已发表的肾移植受者接受他克莫司治疗时的MPA LSS进行了评估,以确定其在估计整个队列的AUC0-12hr时的预测性能,并分别评估了高EHR和低EHR个体的预测性能:结果:所评估的 LSS 模型(n = 12)在预测整个队列中的 MPA AUC0-12hr 时均未显示出良好的精确性或准确性。在高 EHR 组中,时间点较晚的模型准确度较高,但精确度较低,只有一个模型的时间点较晚,分别在服药后 6 小时和 10 小时,其精确度尚可接受。对于所有模型,预测 AUC0-12hr 的良好猜测值(观察到的 AUC0-12hr 的 ±15%)变化很大(范围,全队列 = 19%-61.9%;高 EHR = 4.5%-65.9%;低 EHR = 27.5%-62.5%):结论:LSS 模型的预测性能因 EHR 状态而异。LSS模型中用药后≥5小时的时间点对于捕捉EHR至关重要。要准确确定 MPA 暴露,需要在开发过程中纳入 EHR 的模型和策略。
Limited Sampling Strategies Fail to Accurately Predict Mycophenolic Acid Area Under the Curve in Kidney Transplant Recipients and the Impact of Enterohepatic Recirculation.
Background: Therapeutic drug monitoring for mycophenolic acid (MPA) is challenging due to difficulties in measuring the area under the curve (AUC). Limited sampling strategies (LSSs) have been developed for MPA therapeutic drug monitoring but come with risk of unacceptable performance. The authors hypothesized that the poor predictive performance of LSSs were due to the variability in MPA enterohepatic recirculation (EHR). This study is the first to evaluate LSSs models performance in the context of EHR.
Methods: Adult kidney transplant recipients (n = 84) receiving oral mycophenolate mofetil underwent intensive MPA pharmacokinetic sampling. MPA AUC 0-12hr and EHR were determined. Published MPA LSSs in kidney transplant recipients receiving tacrolimus were evaluated for their predictive performance in estimating AUC 0-12hr in our full cohort and separately in individuals with high and low EHR.
Results: None of the evaluated LSS models (n = 12) showed good precision or accuracy in predicting MPA AUC 0-12hr in the full cohort. In the high EHR group, models with late timepoints had better accuracy but low precision, except for 1 model with late timepoints at 6 and 10 hours postdose, which had marginally acceptable precision. For all models, the good guess of predicted AUC 0-12hr (±15% of observed AUC 0-12hr ) was highly variable (range, full cohort = 19%-61.9%; high EHR = 4.5%-65.9%; low EHR = 27.5%-62.5%).
Conclusions: The predictive performance of the LSS models varied according to EHR status. Timepoints ≥5 hours postdose in LSS models are essential to capture EHR. Models and strategies that incorporate EHR during development are required to accurately ascertain MPA exposure.
期刊介绍:
Therapeutic Drug Monitoring is a peer-reviewed, multidisciplinary journal directed to an audience of pharmacologists, clinical chemists, laboratorians, pharmacists, drug researchers and toxicologists. It fosters the exchange of knowledge among the various disciplines–clinical pharmacology, pathology, toxicology, analytical chemistry–that share a common interest in Therapeutic Drug Monitoring. The journal presents studies detailing the various factors that affect the rate and extent drugs are absorbed, metabolized, and excreted. Regular features include review articles on specific classes of drugs, original articles, case reports, technical notes, and continuing education articles.