Zicheng Yu, Xianghui Wang, Peijun Zhou, Da Xu, Weixia Zhang, Hongzhuan Chen
Institute of Clinical Pharmacology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.
Ther Drug Monit. 2007 Oct;29(5):600-6. doi: 10.1097/FTD.0b013e3181559f8a.
The immunosuppressive potential of mycophenolic acid (MPA) correlates well with MPA exposure [area under the concentration-time curve (AUC)]. Monitoring MPA AUC is important and helpful for maintaining the efficacy of mycophenolate mofetil while minimizing its side effects, but full MPA AUC monitoring is laborious, cost prohibitive, and impractical. Limited sampling strategies have been proposed as an alternative method for estimating MPA exposure. The objective of this study was to evaluate the practicability of different limited sampling strategies for the estimation of MPA exposure. A total of 56 pharmacokinetic profiles from 53 adult renal recipients were used to evaluate the practicability of 10 published models. Standard correlation and linear regression analysis were used to compare the estimated MPA AUCs and corresponding full MPA AUCs, and the percentage of profiles for which prediction error fell within +/-20% was also used to assess the practicability of these models. Agreement between the estimated MPA AUCs and full MPA AUCs was further tested by Bland and Altman analysis. The model, based on four sampling time points, used the formula AUC = 12.61 + 0.37 x C0.5 + 0.49 x C1 + 3.22 x C4 + 8.17 x C10, was superior to all other evaluated models, with the highest coefficient of determination (r = 0.88), a low percentage prediction error (2.79%), and good agreement according to Bland and Altman analysis. Prediction errors of 87.5% (49/56) of profiles were within 20%, which was the highest of all the models. This algorithm can be reliably used for estimating MPA exposure in adult renal transplant patients treated with cyclosporine as concomitant immunosuppressant. Another model based on the formula AUC = 8.22 + 3.16 x C0 + 0.99 x C1 + 1.33 x C2 + 4.18 x C4 also has acceptable predictive performance, and it may also be practical, especially in outpatient settings, in view of its distribution of time points.
霉酚酸（MPA）的免疫抑制潜能与MPA暴露量[浓度-时间曲线下面积（AUC）]密切相关。监测MPA的AUC对于维持霉酚酸酯的疗效同时将其副作用降至最低很重要且有帮助，但完整监测MPA的AUC费力、成本高昂且不切实际。有限采样策略已被提议作为估计MPA暴露量的替代方法。本研究的目的是评估不同有限采样策略用于估计MPA暴露量的实用性。来自53名成年肾移植受者的总共56份药代动力学数据被用于评估10种已发表模型的实用性。采用标准相关性和线性回归分析来比较估计的MPA AUC和相应的完整MPA AUC，预测误差在+/-20%范围内的数据所占百分比也被用于评估这些模型的实用性。通过Bland和Altman分析进一步检验估计的MPA AUC与完整MPA AUC之间的一致性。基于四个采样时间点的模型使用公式AUC = 12.61 + 0.37 x C0.5 + 0.49 x C1 + 3.22 x C4 + 8.17 x C10，优于所有其他评估模型，具有最高的决定系数（r = 0.88）、较低的预测误差百分比（2.79%），并且根据Bland和Altman分析一致性良好。87.5%（49/56）的数据预测误差在20%以内，这在所有模型中是最高的。该算法可可靠地用于估计接受环孢素作为联合免疫抑制剂治疗的成年肾移植患者的MPA暴露量。另一个基于公式AUC = 8.22 + 3.16 x C0 + 0.99 x C1 + 1.33 x C2 + 4.18 x C4的模型也具有可接受的预测性能，鉴于其时间点分布，它可能也很实用，尤其是在门诊环境中。
