School of Medicine, The University of Queensland.
School of Pharmacy, The University of Queensland.
Ther Drug Monit. 2021 Apr 1;43(2):238-246. doi: 10.1097/FTD.0000000000000814.
Bayesian forecasting-based limited sampling strategies (LSSs) for tacrolimus have not been evaluated for the prediction of subsequent tacrolimus exposure. This study examined the predictive performance of Bayesian forecasting programs/services for the estimation of future tacrolimus area under the curve (AUC) from 0 to 12 hours (AUC0-12) in kidney transplant recipients.
Tacrolimus concentrations were measured in 20 adult kidney transplant recipients, 1 month post-transplant, on 2 occasions one week apart. Twelve samples were taken predose and 13 samples were taken postdose at the specified times on the first and second sampling occasions, respectively. The predicted AUC0-12 (AUCpredicted) was estimated using Bayesian forecasting programs/services and data from both sampling occasions for each patient and compared with the fully measured AUC0-12 (AUCmeasured) calculated using the linear trapezoidal rule on the second sampling occasion. The bias (median percentage prediction error [MPPE]) and imprecision (median absolute prediction error [MAPE]) were determined.
Three programs/services were evaluated using different LSSs (C0; C0, C1, C3; C0, C1, C2, C4; and all available concentrations). MPPE and MAPE for the prediction of fully measured AUC0-12 were <15% for each program/service (with the exclusion of when only C0 was used), when using estimated AUC from data on the same (second) occasion. The MPPE and MAPE for the prediction of a future fully measured AUC0-12 were <15% for 2 programs/services (and for the third when participants who had a tacrolimus dose change between sampling days were excluded), when the occasion 1-AUCpredicted, using C0, C1, and C3, was compared with the occasion 2-AUCmeasured.
All 3 Bayesian forecasting programs/services evaluated had acceptable bias and imprecision for predicting a future AUC0-12, using tacrolimus concentrations at C0, C1, and C3, and could be used for the accurate prediction of tacrolimus exposure in adult kidney transplant recipients.
贝叶斯预测为基础的有限采样策略(LSS)在预测他克莫司的后续暴露方面尚未得到评估。本研究旨在评估贝叶斯预测方案/服务在预测肾移植受者 0 至 12 小时时的他克莫司下面积(AUC0-12)方面的预测性能。
在移植后 1 个月,对 20 例成年肾移植受者进行了 2 次采样,间隔 1 周。第一次采样时,在规定时间分别采集了 12 个点的预剂量样本和 13 个点的剂量后样本。分别使用每个患者两次采样的数据,通过贝叶斯预测方案/服务估算预测 AUC0-12(AUCpredicted),并与第二次采样时使用线性梯形规则计算的全测 AUC0-12(AUCmeasured)进行比较。确定了偏差(中位数预测误差百分比[MPPE])和不精确性(中位数绝对预测误差[MAPE])。
使用不同的 LSS(C0;C0、C1、C3;C0、C1、C2、C4;以及所有可用浓度)评估了 3 个方案/服务。当使用相同(第二次)采样时的数据估算 AUC 时,每个方案/服务的 AUC0-12 全测预测的 MPPE 和 MAPE 均<15%(当仅使用 C0 时除外)。当使用来自第一次采样时的数据估算 AUC 时,2 个方案/服务(第三个方案/服务在排除了采样日之间调整他克莫司剂量的参与者后)的 AUC0-12 全测预测的 MPPE 和 MAPE<15%,比较了 C0、C1 和 C3 的第一次采样 AUCpredicted 与第二次采样 AUCmeasured。
使用 C0、C1 和 C3 的他克莫司浓度,评价的 3 种贝叶斯预测方案/服务在预测未来 AUC0-12 方面具有可接受的偏差和不精确性,可用于准确预测成年肾移植受者的他克莫司暴露情况。