Sarem Sarem, Li Jun, Barriere Olivier, Litalien Catherine, Théorêt Yves, Lapeyraque Anne-Laure, Nekka Fahima
Faculty of Pharmacy, Université de Montréal, C,P, 6128, Succ, Centre-ville, H3C 3J7 Montreal, Canada.
Theor Biol Med Model. 2014 Sep 5;11:39. doi: 10.1186/1742-4682-11-39.
The optimal marker for cyclosporine (CsA) monitoring in transplantation patients remains controversial. However, there is a growing interest in the use of the area under the concentration-time curve (AUC), particularly for cyclosporine dose adjustment in pediatric hematopoietic stem cell transplantation. In this paper, we develop Bayesian limited sampling strategies (B-LSS) for cyclosporine AUC estimation using population pharmacokinetic (Pop-PK) models and investigate related issues, with the aim to improve B-LSS prediction performance.
Twenty five pediatric hematopoietic stem cell transplantation patients receiving intravenous and oral cyclosporine were investigated. Pop-PK analyses were carried out and the predictive performance of B-LSS was evaluated using the final Pop-PK model and several related ones. The performance of B-LSS when targeting different versions of AUC was also discussed.
A two-compartment structure model with a lag time and a combined additive and proportional error is retained. The final covariate model does not improve the B-LSS prediction performance. The best performing models for intravenous and oral cyclosporine are the structure ones with combined and additive error, respectively. Twelve B-LSS, consisting of 4 or less sampling points obtained within 4 hours post-dose, predict AUC with 95th percentile of the absolute values of relative prediction errors of 20% or less. Moreover, B-LSS perform better for the prediction of the 'underlying' AUC derived from the Pop-PK model estimated concentrations that exclude the residual errors, in comparison to their prediction of the observed AUC directly calculated using measured concentrations.
B-LSS can adequately estimate cyclosporine AUC. However, B-LSS performance is not perfectly in line with the standard Pop-PK model selection criteria; hence the final model might not be ideal for AUC prediction purpose. Therefore, for B-LSS application, Pop-PK model diagnostic criteria should additionally account for AUC prediction errors.
移植患者中环孢素（CsA）监测的最佳标志物仍存在争议。然而，浓度-时间曲线下面积（AUC）的应用越来越受到关注，尤其是在儿科造血干细胞移植中用于环孢素剂量调整。在本文中，我们利用群体药代动力学（Pop-PK）模型开发了用于环孢素AUC估计的贝叶斯有限采样策略（B-LSS），并研究相关问题，旨在提高B-LSS的预测性能。
对25例接受静脉和口服环孢素的儿科造血干细胞移植患者进行了研究。进行了Pop-PK分析，并使用最终的Pop-PK模型和几个相关模型评估了B-LSS的预测性能。还讨论了针对不同版本AUC时B-LSS的性能。
保留了具有滞后时间以及组合加法和比例误差的二室结构模型。最终的协变量模型并未提高B-LSS的预测性能。静脉注射和口服环孢素的最佳性能模型分别是具有组合误差和加法误差的结构模型。12种B-LSS由给药后4小时内获得的4个或更少采样点组成，预测AUC时相对预测误差绝对值的第95百分位数为20%或更低。此外，与直接使用测量浓度计算的观察到的AUC相比，B-LSS在预测从Pop-PK模型估计浓度中排除残差误差得到的“基础”AUC时表现更好。
B-LSS可以充分估计环孢素AUC。然而，B-LSS的性能并不完全符合标准的Pop-PK模型选择标准；因此最终模型可能并非用于AUC预测目的的理想模型。因此，对于B-LSS的应用，Pop-PK模型诊断标准应额外考虑AUC预测误差。
