Brooks Logan, Harun Rashed, Jin Jin Y, Lu James
Clinical Pharmacology, Genentech, Inc., South San Francisco, California, USA.
PTC Genomics, Bioinformatics & Biospecimens, Genentech, Inc., South San Francisco, California, USA.
CPT Pharmacometrics Syst Pharmacol. 2025 Aug;14(8):1322-1331. doi: 10.1002/psp4.70044. Epub 2025 Jul 2.
Covariate identification in population pharmacokinetic/pharmacodynamic (popPK/PD) modeling is a key component in model development that is often prone to bias, time-consuming, and even intractable when too many covariates or complicated models are being considered. Early work leveraging machine learning (ML) for covariate screening has shown promising results over traditional methods. In this work, we expand this effort by integrating explainable machine learning facilitated by Shapley Additive Explanations (SHAP) analysis and covariate uncertainty quantification as well as a formal framework for establishing statistical significance of covariate relationships. Finally, we have packaged the proposed methodology into a flexible set of functions (shap-cov) to support popPK/PD modeling covariate identification.
群体药代动力学/药效学（popPK/PD）模型中的协变量识别是模型开发的关键组成部分，在考虑过多协变量或复杂模型时，该过程往往容易产生偏差、耗时，甚至难以处理。早期利用机器学习（ML）进行协变量筛选的工作已显示出比传统方法更有前景的结果。在这项工作中，我们通过整合由夏普利加法解释（SHAP）分析推动的可解释机器学习、协变量不确定性量化以及用于确定协变量关系统计显著性的正式框架来扩展这一工作。最后，我们将所提出的方法打包成一组灵活的函数（shap-cov），以支持popPK/PD模型协变量识别。
