评估外推方法以预测谷浓度，以指导口服抗癌药物的治疗药物监测。
Evaluation of Extrapolation Methods to Predict Trough Concentrations to Guide Therapeutic Drug Monitoring of Oral Anticancer Drugs.
机构信息
Department of Pharmacy and Pharmacology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek, Amsterdam.
Department of Pharmaceutical Sciences, Utrecht University, Utrecht; and.
出版信息
Ther Drug Monit. 2020 Aug;42(4):532-539. doi: 10.1097/FTD.0000000000000767.
BACKGROUND
For oral anticancer drugs, trough concentration (Cmin) is usually used as a target in therapeutic drug monitoring (TDM). Recording of Cmin is highly challenging in outpatients, in whom there is typically a variability in sample collection time after dosing. Various methods are used to estimate Cmin from the collected samples. This simulation study aimed to evaluate the performance of 3 different methods in estimating the Cmin of 4 oral anticancer drugs for which TDM is regularly performed.
METHODS
Plasma concentrations of abiraterone, dabrafenib, imatinib, and pazopanib at a random time (Ct,sim) and at the end of the dosing interval (Cmin,sim) were simulated from population pharmacokinetic models including 1000 patients, and the values were converted into simulated observed concentrations (Ct,sim,obs and Cmin,sim,obs) by adding a residual error. From Ct, sim,obs, Cmin was predicted (Cmin,pred) by the Bayesian estimation (method 1), taking the ratio of the Ct,sim,obs and typical population concentration and multiplying this ratio with the typical population value of Cmin,sim (method 2), and log-linear extrapolation (method 3). Target attainment was assessed by comparing Cmin,pred with the proposed pharmacokinetic targets related to efficacy and calculating the positive predictive and negative predictive values.
RESULTS
The mean relative prediction error and root mean squared relative prediction error results showed that method 3 was out-performed by method 1 and 2. Target attainment was adequately predicted by all 3 methods (the respective positive predictive value of method 1, 2, and 3 was 92.1%, 92.5%, and 93.1% for abiraterone; 87.3%, 86.9%, and 99.1% for dabrafenib; 79.3%, 79.3%, and 75.9% for imatinib; and 72.5%, 73.5%, and 67.6% for pazopanib), indicating that dose adjustments were correctly predicted.
CONCLUSIONS
Both method 1 and 2 provided accurate and precise individual Cmin,pred values. However, method 2 was easier to implement than method 1 to guide individual dose adjustments in TDM programs.
背景
对于口服抗癌药物，治疗药物监测（TDM）通常以谷浓度（Cmin）作为目标。在门诊患者中，由于在给药后采集样本的时间存在很大的变异性，因此记录 Cmin 极具挑战性。目前使用各种方法从采集的样本中估算 Cmin。本模拟研究旨在评估 3 种不同方法估算 4 种常规 TDM 的口服抗癌药物 Cmin 的性能。
方法
从包括 1000 名患者的群体药代动力学模型中模拟阿比特龙、达布拉非尼、伊马替尼和帕唑帕尼的随机时间（Ct，sim）和给药间隔结束时（Cmin，sim）的血浆浓度，并通过添加残差将这些值转换为模拟观察浓度（Ct，sim，obs 和 Cmin，sim，obs）。从 Ct，sim，obs 预测 Cmin（Cmin，pred），采用贝叶斯估计（方法 1），将 Ct，sim，obs 与典型群体浓度的比值乘以典型群体 Cmin，sim 值（方法 2），对数线性外推（方法 3）。通过比较 Cmin，pred 与疗效相关的建议药代动力学目标来评估目标达成情况，并计算阳性预测值和阴性预测值。
结果
平均相对预测误差和均方根相对预测误差结果表明，方法 3 优于方法 1 和 2。所有 3 种方法均能很好地预测目标达成情况（方法 1、2 和 3 预测阿比特龙的阳性预测值分别为 92.1%、92.5%和 93.1%；达布拉非尼分别为 87.3%、86.9%和 99.1%；伊马替尼分别为 79.3%、79.3%和 75.9%；帕唑帕尼分别为 72.5%、73.5%和 67.6%），这表明剂量调整得到了正确预测。
结论
方法 1 和 2 均能提供准确、精密的个体 Cmin，pred 值。然而，方法 2 比方法 1 更易于实施，可用于指导 TDM 方案中的个体剂量调整。
Zotero 插件