基于群体药动学信息与机器学习的他克莫司血药浓度预测模型研究 点击下载
| 论文标题： | 基于群体药动学信息与机器学习的他克莫司血药浓度预测模型研究 |
| 英文标题： | |
| 中文摘要： | 目的 构建基于群体药动学（PPK）信息与机器学习的他克莫司谷浓度预测模型，评估其在临床治疗药物监测（TDM）数据中的预测性能，为肾移植术后他克莫司个体化剂量调整提供循证依据。方法采用回顾性队列研究设计，纳入2025年1－6月在中国科学技术大学附属第一医院（安徽省立医院）治疗的肾移植受者的连续TDM记录，构建最终分析集。围绕前次TDM浓度、前次浓度/剂量（C/D）比、个体预测暴露量（IPRE）等29个输入变量，系统比较13种常用回归算法模型及1种加权集成模型。以患者为单位，按80∶20将数据集分为训练集与测试集，在训练集上开展基于患者分组的5折交叉验证，最终在获得的评估点上采用决定系数（R2）、平均绝对误差（MAE）、均方根误差（RMSE）、Pearson相关系数（Corr）及±20%/±30%准确率评价模型性能。采用沙普利加性解释（SHAP）法开展模型可解释性分析，明确核心预测特征。结果共纳入83例肾移植受者的809条连续TDM记录，其中训练集66例、测试集17例。SVR模型表现最优，R2为0.6498，MAE为1.1260ng/mL，RMSE为1.4786ng/mL，Corr为0.8191，±20%准确率为59.68%，±30%准确率为79.03%。WeightedEnsemble模型与ExtraTrees模型位列其后。SHAP可解释性分析显示，贡献排前6名的特征依次为IPRE、前次TDM浓度、前次C/D比、剂量标准化浓度、红细胞压积（HCT）、浓度-剂量系数（θcd），其中IPRE为核心预测特征（平均SHAP值0.9154）。结论融合PPK信息的机器学习模型在当前TDM队列中展现了较好的他克莫司浓度预测能力；SVR为最优模型，IPRE为模型核心预测特征，少数核心PK派生特征（IPRE、剂量标准化浓度、θcd）与临床特征（前次TDM浓度、HCT）共同构成模型预测的主要信息源。 |
| 英文摘要： | OBJECTIVE To construct a prediction model for tacrolimus trough concentration based on population pharmacokinetics （PPK） information and machine learning， evaluate its predictive performance in clinical therapeutic drug monitoring （TDM） data， and provide evidence-based support for individualized dose adjustment of tacrolimus after renal transplantation. METHODS A retrospective cohort study design was adopted， and consecutive TDM records of kidney transplant recipients treated in the First Affiliated Hospital of University of Science and Technology of China （Anhui Provincial Hospital） from January to June 2025 were included to construct the final analysis set. Based on 29 input variables including previous TDM concentration， previous concentration/dose （ C / D ） ratio， and individual predicted exposure （IPRE）， 13 commonly used regression algorithm models and 1 weighted ensemble model were systematically compared. The dataset was split into training set and test set in an 80∶20 ratio by patient. Five-fold cross-validation based on patient grouping was performed on the training set， and the model performance was evaluated on the obtained evaluation points using the coefficient of determination （ R 2 ）， mean absolute error （MAE）， root mean square error （RMSE）， Pearson correlation coefficient （Corr） and ±20%/±30% accuracy. The SHapley Additive exPlanations （SHAP） method was used for model interpretability analysis to identify core predictive features. RESULTS A total of 809 consecutive TDM records from 83 kidney transplant recipients were included， with 66 cases in the training set and 17 cases in the test set. The SVR model showed the best performance， with R 2 of 0.649 8， MAE of 1.126 0 ng/mL， RMSE of 1.478 6 ng/mL， Corr of 0.819 1， ±20% accuracy of 59.68%， and ±30% accuracy of 79.03%. The Weighted Ensemble model and Extra Trees model ranked second and third， respectively. SHAP interpretability analysis showed that the top 6 features contributing to the prediction were IPRE， previous TDM concentration， previous C/D ratio， dose-normalized concentration， hematocrit （HCT）， and concentration-dose coefficient （ θ cd ）， among which IPRE was the core predictive feature （SHAP mean value 0.915 4）. CONCLUSIONS The machine learning model incorporating PPK information demonstrates good predictive ability for tacrolimus concentration in the current TDM cohort. SVR is the optimal model， IPRE is the core predictive feature of the model， and a few core PK-derived features （IPRE， dose-normalized concentration， θ cd ） together with clinical features （previous TDM concentration， HCT， etc.） constitute the main information sources for model prediction. |
| 期刊： | 2026年第37卷第14期 |
| 作者： | 陈昭琳；朱鹏里；倪伟建；张蕾；唐丽琴 |
| 英文作者： | CHEN Zhaolin，ZHU Pengli，NI Weijian，ZHANG Lei，TANG Liqin |
| 关键字： | 肾移植; 他克莫司; 群体药动学; 机器学习; 治疗药物监测 |
| KEYWORDS： | renal transplantation; tacrolimus; population pharmacokinetics; machine learning; therapeutic drug monitoring |
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