Ray John E, Keogh Anne M, McLachlan Andrew J
Department of Clinical Pharmacology and Toxicology, SydPath, St. Vincent's Hospital, Sydney, Australia.
J Heart Lung Transplant. 2006 Oct;25(10):1223-9. doi: 10.1016/j.healun.2006.07.002.
Monitoring of the 2-hour post-dose sample (C(2)) for cyclosporine (CsA) has gained favor; however, choosing a single-point surrogate marker of therapeutic effect for a drug with extensive pharmacokinetic variability is problematic and has limitations.
A Bayesian decision support tool was developed using published pharmacokinetic information implemented using ABBOTTBASE pharmacokinetic software. The model was evaluated in 47 stable heart transplant recipients who received concomitant administration of drugs known to inhibit CsA metabolism: diltiazem; ketoconazole; and a combination of diltiazem and ketoconazole.
A 3-point feedback strategy with samples collected at 0, 1 and 2 hours after an oral CsA dose was used to predict area under the concentration-time curve in the first 12 hours post-dose (AUC(0-12)). In Group A, patients who received CsA alone showed a good correlation between observed and model-predicted CsA AUC(0-12) (r(2) = 0.871, p < 0.001, precision of 12%, accuracy of 13%). Furthermore, the Bayesian model provided acceptable predictions in patients who received CsA with metabolic inhibitors: Group B (diltiazem), r(2) = 0.791, p < 0.001, precision of 19%, accuracy of 22%; Group C (ketoconazole), r(2) = 0.761, p < 0.001, precision of 4%, accuracy of 12%; and Group D (diltiazem plus ketoconazole), r(2) = 0.818, p < 0.001, precision of 14%, accuracy of 17%.
A Bayesian decision support tool is described that can predict CsA AUC(0-12) in a cohort of patients with variable CsA absorption who received metabolic inhibitors. Bayesian modeling offers a number of advantages over single point metrics that are used to adjust CsA dose and may provide another refinement to optimize CsA therapy.
监测环孢素（CsA）给药后2小时样本（C(2)）已受到青睐；然而，对于一种具有广泛药代动力学变异性的药物，选择单一的治疗效果替代标志物存在问题且有局限性。
利用已发表的药代动力学信息，使用ABBOTTBASE药代动力学软件开发了一种贝叶斯决策支持工具。该模型在47名接受已知抑制CsA代谢药物联合给药的稳定心脏移植受者中进行了评估：地尔硫䓬；酮康唑；以及地尔硫䓬和酮康唑的组合。
采用口服CsA剂量后0、1和2小时采集样本的三点反馈策略来预测给药后前12小时的浓度 - 时间曲线下面积（AUC(0 - 12)）。在A组中，单独接受CsA的患者观察到的和模型预测的CsAC(0 - 12)之间显示出良好的相关性（r(2) = 0.871，p < 0.001，精密度为12%，准确度为13%）。此外，贝叶斯模型在接受CsA与代谢抑制剂联合给药的患者中提供了可接受的预测：B组（地尔硫䓬），r(2) = 0.791，p < 0.001，精密度为19%，准确度为22%；C组（酮康唑），r(2) = 0.761，p < 0.001，精密度为4%，准确度为12%；D组（地尔硫䓬加酮康唑），r(2) = 0.818，p < 0.001，精密度为14%，准确度为17%。
描述了一种贝叶斯决策支持工具，它可以预测接受代谢抑制剂的CsA吸收可变的一组患者的CsAC(0 - 12)。与用于调整CsA剂量的单点指标相比，贝叶斯建模具有许多优势，并且可能为优化CsA治疗提供另一项改进。
