Background

Maximum a posteriori Bayesian estimation (MAP-BE) based on a limited sampling strategy and a population pharmacokinetic model is frequently used to estimate pharmacokinetic parameters in individuals, however with some uncertainty (bias). Recent works have shown that the performance in individual estimation or pharmacokinetic parameters can be improved by combining population pharmacokinetic and machine learning algorithms. Objective: The objective of this work was to investigate the use of a hybrid machine learning/population pharmacokinetic approach to improve individual iohexol clearance estimation.

Methods

The reference iohexol clearance values were derived from 500 simulated profiles (samples collected between 0.1 and 24.7 h) using a population pharmacokinetic model we recently developed in Monolix and obtained using all the concentration timepoints available. Xgboost and glmnet algorithms able to predict the error of MAP-BE clearance estimates based on a limited sampling strategy (0.1 h, 1 h, and 9 h) versus reference values were developed in a training subset (75%) and were evaluated in a testing subset (25%) and in 36 real patients.

Results

The MAP-BE limited sampling strategy estimated clearance was corrected by the machine learning predicted error leading to a decrease in root mean squared error by 29% and 24%, and in the percentage of profiles with the mean prediction error out of the ± 20% bias by 60% and 40% in the external validation dataset for the glmnet and Xgboost machine learning algorithms, respectively. These results were attributable to a decrease in the eta-shrinkage (shrinkage for a MAP-BE limited sampling strategy = 32.4%, glmnet = 18.2%, and Xgboost = 19.4% in the external dataset).

Conclusions

 In conclusion, this hybrid algorithm represents a significant improvement in comparison to MAP-BE estimation alone.

中文翻译：

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将群体药代动力学与机器学习相关联的混合模型：碘海醇清除率估计的案例研究

背景

基于有限抽样策略和群体药代动力学模型的最大后验贝叶斯估计 (MAP-BE) 经常用于估计个体的药代动力学参数，但存在一些不确定性（偏差）。最近的工作表明，通过结合群体药代动力学和机器学习算法，可以提高个体估计或药代动力学参数的性能。目的：这项工作的目的是研究使用混合机器学习/群体药代动力学方法来改进个体碘海醇清除率估计。

方法

参考碘海醇清除率值来自 500 个模拟曲线（在 0.1 和 24.7 小时之间收集的样本），使用我们最近在 Monolix 中开发并使用所有可用浓度时间点获得的群体药代动力学模型。Xgboost 和 glmnet 算法能够预测基于有限抽样策略（0.1 小时、1 小时和 9 小时）相对于参考值的 MAP-BE 清除率估计误差，在训练子集 (75%) 中开发，并在测试子集（25%）和 36 名真实患者。

结果

MAP-BE 有限抽样策略估计清除率通过机器学习预测误差进行校正，导致均方根误差降低 29% 和 24%，并且平均预测误差超出 ± 20% 的配置文件的百分比在 glmnet 和 Xgboost 机器学习算法的外部验证数据集中，偏差分别为 60% 和 40%。这些结果可归因于 eta 收缩率的降低（外部数据集中 MAP-BE 有限采样策略的收缩率 = 32.4%、glmnet = 18.2% 和 Xgboost = 19.4%）。

结论

 总之，与单独的 MAP-BE 估计相比，这种混合算法代表了显着的改进。