Table_1_Applying a Global Sensitivity Analysis Workflow to Improve the Computational Efficiencies in Physiologically-Based Pharmacokinetic Modeling.PDF

Traditionally, the solution to reduce parameter dimensionality in a physiologically-based pharmacokinetic (PBPK) model is through expert judgment. However, this approach may lead to bias in parameter estimates and model predictions if important parameters are fixed at uncertain or inappropriate values. The purpose of this study was to explore the application of global sensitivity analysis (GSA) to ascertain which parameters in the PBPK model are non-influential, and therefore can be assigned fixed values in Bayesian parameter estimation with minimal bias. We compared the elementary effect-based Morris method and three variance-based Sobol indices in their ability to distinguish “influential” parameters to be estimated and “non-influential” parameters to be fixed. We illustrated this approach using a published human PBPK model for acetaminophen (APAP) and its two primary metabolites APAP-glucuronide and APAP-sulfate. We first applied GSA to the original published model, comparing Bayesian model calibration results using all the 21 originally calibrated model parameters (OMP, determined by “expert judgment”-based approach) vs. the subset of original influential parameters (OIP, determined by GSA from the OMP). We then applied GSA to all the PBPK parameters, including those fixed in the published model, comparing the model calibration results using this full set of 58 model parameters (FMP) vs. the full set influential parameters (FIP, determined by GSA from FMP). We also examined the impact of different cut-off points to distinguish the influential and non-influential parameters. We found that Sobol indices calculated by eFAST provided the best combination of reliability (consistency with other variance-based methods) and efficiency (lowest computational cost to achieve convergence) in identifying influential parameters. We identified several originally calibrated parameters that were not influential, and could be fixed to improve computational efficiency without discernable changes in prediction accuracy or precision. We further found six previously fixed parameters that were actually influential to the model predictions. Adding these additional influential parameters improved the model performance beyond that of the original publication while maintaining similar computational efficiency. We conclude that GSA provides an objective, transparent, and reproducible approach to improve the performance and computational efficiency of PBPK models.

传统上，降低基于生理药代动力学（PBPK）模型参数维度的解决方案往往依赖于专家判断。然而，若关键参数被设定在不确定或不当的值上，此方法可能导致参数估计和模型预测中的偏差。本研究旨在探讨全局敏感性分析（GSA）在确定PBPK模型中哪些参数不具有影响力，从而在贝叶斯参数估计中赋予其固定值，以最小化偏差的应用。我们对比了基于基本效应的Morris方法和三种基于方差的Sobol指数在区分需估计的“影响力参数”和需固定的“非影响力参数”方面的能力。我们利用已发表的针对对乙酰氨基酚（APAP）及其两种主要代谢物APAP-葡萄糖苷和APAP-硫酸盐的人类PBPK模型来阐述这一方法。首先，我们对原始模型进行了GSA分析，比较了使用所有21个原始校准模型参数（OMP，由基于“专家判断”的方法确定）与仅使用由GSA从OMP中确定的原始影响力参数（OIP）的贝叶斯模型校准结果。随后，我们对所有PBPK参数，包括在已发表模型中固定的参数，进行了GSA分析，比较了使用此完整集的58个模型参数（FMP）与从FMP中通过GSA确定的完整集影响力参数（FIP）的模型校准结果。我们还考察了不同截止点对区分影响力参数和非影响力参数的影响。我们发现，由eFAST计算出的Sobol指数在可靠性（与其他方差方法的一致性）和效率（实现收敛的最小计算成本）方面提供了最佳组合，以识别影响力参数。我们识别出一些原本校准的参数实际上并不具有影响力，可以被固定以提高计算效率，而不会在预测精度或准确性上产生可察觉的变化。我们还发现六个先前固定的参数实际上对模型预测具有影响力。添加这些额外的具有影响力的参数提高了模型性能，超过了原始发表的模型，同时保持了类似的计算效率。我们得出结论，GSA提供了一种客观、透明且可复制的改进PBPK模型性能和计算效率的方法。