EMR 3738, Ciblage, Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, 165 chemin du Grand Revoyet-BP 12, 69921, Oullins Cedex, France.
Hospices Civils de Lyon, Groupement Hospitalier Nord, Hôpital Pierre Garraud, Service pharmaceutique, 136 rue du Commandant Charcot, 69005, Lyon, France.
Drugs R D. 2018 Mar;18(1):67-75. doi: 10.1007/s40268-017-0223-7.
Pediatric hematopoietic stem cell transplantation (HSCT) allows the treatment of numerous diseases, both malignant and non-malignant. Cyclosporine, a narrow therapeutic index drug, is the major immunosuppressant used to prevent graft-versus-host disease (GVHD), but may also cause severe adverse effects in case of overdosing.
The objective of this study is to predict the initial cyclosporine residual blood concentration value after pediatric HSCT, and consequently the dose necessary to reach the therapeutic range, using a mathematical individual predictive model.
Clinical and biological data collected from the graft infusion for 2 months after transplantation in 155 pediatric patients undergoing HSCT between 2008 and 2016 were used to generate synthetic data for 1000 subjects which were used to build a Bayesian network model. We compared the characteristics and sensitivity to clinical or biological missing data of this model with four other methods.
The tree-augmented Naïve Bayesian network showed the best characteristics, with no missing data (area under the curve of the receiving operator characteristics curve [AUC-ROC] of 0.89 ± 0.02), 18.9 ± 2.6% of patients misclassified, and positive and negative predictive values of 85.9 ± 3.4% and 74.2 ± 5.1%, respectively, and this trend is found in the synthetic dataset from no to 10% missing data. The most relevant variables that could influence whether the initial residual cyclosporine concentration is in the therapeutic range are the last dose before measurement and the mean dose before measurement.
We developed and cross-validated an online Bayesian network to predict the first cyclosporine concentration after pediatric HSCT. This model allows simulation of different dosing regimens, and enables the best dosing regimen to reach the therapeutic range immediately after transplantation to be found, minimizing the risk of adverse effects and GVHD occurrence.
儿科造血干细胞移植（HSCT）可治疗多种疾病，包括恶性和非恶性疾病。环孢素是一种治疗指数较窄的药物，是预防移植物抗宿主病（GVHD）的主要免疫抑制剂，但在过量用药时也可能引起严重的不良反应。
本研究旨在利用数学个体预测模型预测儿科 HSCT 后初始环孢素残留血药浓度值，从而预测达到治疗范围所需的剂量。
我们收集了 2008 年至 2016 年间 155 例接受 HSCT 的儿科患者在移植后 2 个月内输注移植物时的临床和生物学数据，用于为 1000 名患者生成合成数据，并用这些数据构建贝叶斯网络模型。我们比较了该模型与其他四种方法的特征和对临床或生物学缺失数据的敏感性。
树增强朴素贝叶斯网络的特征最好，没有缺失数据（接受者操作特征曲线下的面积 [AUC-ROC]为 0.89±0.02），18.9%±2.6%的患者被错误分类，阳性和阴性预测值分别为 85.9%±3.4%和 74.2%±5.1%，且在从无缺失数据到 10%缺失数据的合成数据中都呈现这种趋势。最能影响初始残留环孢素浓度是否处于治疗范围内的相关变量是测量前的最后一次剂量和测量前的平均剂量。
我们开发并交叉验证了一个在线贝叶斯网络来预测儿科 HSCT 后首次环孢素浓度。该模型可模拟不同的给药方案，并能找到立即在移植后达到治疗范围的最佳给药方案，最大限度地降低不良反应和 GVHD 发生的风险。
