Irie Kei, Minar Phillip, Reifenberg Jack, Boyle Brendan M, Noe Joshua D, Hyams Jeffrey S, Mizuno Tomoyuki
Division of Translational and Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
Division of Gastroenterology, Hepatology and Nutrition, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
bioRxiv. 2025 May 7:2025.05.01.651780. doi: 10.1101/2025.05.01.651780.
Population pharmacokinetic (PK) model-based Bayesian estimation is widely used for dose individualization, particularly when sample availability is limited. However, its predictive accuracy can be compromised by factors such as misspecified prior information, intra-patient variability, and uncertainties in PK variations. In this study, we developed a hybrid approach that combines machine learning (ML) with population PK-based Bayesian methods to improve the prediction of infliximab concentrations in children with Crohn's disease. We calculated prediction errors between Bayesian-estimated and observed infliximab concentrations from 292 measurements across 93 patients. Incorporating clinical patient features, we explored various ML algorithms, including linear regression, random forest, support vector regression, neural networks, and XGBoost to correct the Bayesian-based prediction errors. The predictive performance of these ML models was assessed using root mean square error (RMSE) and mean prediction error (MPE) with 5-fold cross-validation. For Bayesian estimation alone, the RMSE and MPE were 4.8 μg/mL and -0.67 μg/mL, respectively. Among the ML algorithms, the XGBoost model demonstrated the best performance, achieving an RMSE of 3.78 ± 0.85 μg/mL and an MPE of -0.03 ± 0.69 μg/mL in 5-fold cross-validation. The ML-corrected Bayesian estimation significantly reduced the absolute prediction error compared to Bayesian estimation alone. This hybrid population PK-ML approach provides a promising framework for improving the predictive performance of Bayesian estimation, with the potential for continuous learning from new clinical data to enhance dose individualization.
基于群体药代动力学（PK）模型的贝叶斯估计广泛用于剂量个体化，尤其是在样本可获取性有限时。然而，其预测准确性可能会受到诸如先验信息指定错误、患者内变异性以及PK变异中的不确定性等因素的影响。在本研究中，我们开发了一种将机器学习（ML）与基于群体PK的贝叶斯方法相结合的混合方法，以改善对克罗恩病患儿英夫利昔单抗浓度的预测。我们计算了93例患者292次测量中贝叶斯估计的英夫利昔单抗浓度与观察到的浓度之间的预测误差。纳入临床患者特征后，我们探索了各种ML算法，包括线性回归、随机森林、支持向量回归、神经网络和XGBoost，以校正基于贝叶斯的预测误差。使用均方根误差（RMSE）和平均预测误差（MPE）并通过5折交叉验证来评估这些ML模型的预测性能。仅对于贝叶斯估计，RMSE和MPE分别为4.8μg/mL和 -0.67μg/mL。在ML算法中，XGBoost模型表现最佳，在5折交叉验证中RMSE为3.78±0.85μg/mL，MPE为 -0.03±0.69μg/mL。与单独的贝叶斯估计相比，经ML校正的贝叶斯估计显著降低了绝对预测误差。这种群体PK - ML混合方法为提高贝叶斯估计的预测性能提供了一个有前景的框架，具有从新的临床数据中持续学习以增强剂量个体化的潜力。
