Iwata Hiroaki, Kageyama Michiharu, Handa Koichi
Division of School of Health Science, Department of Biological Regulation, Faculty of Medicine, Tottori University, 86 Nishi-cho, Yonago 683-8503, Japan.
Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan.
Mol Pharm. 2025 Jun 2;22(6):2976-2984. doi: 10.1021/acs.molpharmaceut.4c01431. Epub 2025 May 9.
Recent research has increasingly focused on using machine learning for covariate selection in population pharmacokinetics (PPK) analysis. However, few studies have explored the prediction of plasma concentration profiles of drugs using nonlinear mixed-effect models combined with machine learning. This gap includes limited validation of prediction accuracy and applicability to diverse patient populations and dosing conditions. This study addresses these gaps by using remifentanil as a model drug and applying machine learning models to predict plasma concentration profiles based on virtual and real-world data. We created various training data sets for the virtual data by clustering based on the size and diversity of the test data set. Our results demonstrated high prediction accuracy for virtual and real-world data sets using Random Forest models. These results suggest that machine learning models are effective for large-scale data sets and real-world data with variable dosing times and amounts per patient. Considering the efficiency of machine learning, it offers a fit-for-purpose approach alongside traditional PPK methods, potentially enhancing future pharmacokinetic and pharmacodynamic studies.
最近的研究越来越多地聚焦于在群体药代动力学（PPK）分析中使用机器学习进行协变量选择。然而，很少有研究探索使用非线性混合效应模型结合机器学习来预测药物的血浆浓度曲线。这一差距包括对预测准确性的验证有限，以及对不同患者群体和给药条件的适用性有限。本研究通过使用瑞芬太尼作为模型药物，并应用机器学习模型基于虚拟数据和真实世界数据来预测血浆浓度曲线，解决了这些差距。我们通过根据测试数据集的大小和多样性进行聚类，为虚拟数据创建了各种训练数据集。我们的结果表明，使用随机森林模型对虚拟数据集和真实世界数据集都具有很高的预测准确性。这些结果表明，机器学习模型对于具有可变给药时间和每位患者给药量的大规模数据集和真实世界数据是有效的。考虑到机器学习的效率，它为传统PPK方法提供了一种适用的方法，有可能加强未来的药代动力学和药效学研究。
