Zhang Mengjun, Wu Keheng, Long Sihui, Jin Xiong, Liu Bo
School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan, China.
Yinghan Pharmaceutical Technology (Shanghai) Co., Ltd., Shanghai, China.
Front Pharmacol. 2025 Apr 28;16:1578117. doi: 10.3389/fphar.2025.1578117. eCollection 2025.
The objective of this study is to develop an artificial intelligence-physiologically based pharmacokinetic (AI-PBPK) model to predict the pharmacokinetic (PK) and pharmacodynamic (PD) properties of aldosterone synthase inhibitors (ASIs), enabling selection of the right candidate with high potency and good selectivity at the drug discovery stage. On a web-based platform, an AI-PBPK model, integrating machine learning and a classical PBPK model for the PK simulation of ASIs, was developed. Baxdrostat, with the most clinical data available, was selected as the model compound. Following calibration and validation using published data, the model was applied to estimate the PK parameters of Baxdrostat, Dexfadrostat, Lorundrostat, BI689648, and the 11β-hydroxylase inhibitor LCI699. The PD of all five compounds was predicted based on plasma free drug concentrations. The results demonstrated that the PK/PD properties of an ASI could be inferred from its structural formula within a certain error range, providing a reference for early ASI lead compounds screening and optimization. Further validation and refinement of this model will enhance its predictive accuracy and expand its application in drug discovery.
本研究的目的是开发一种基于人工智能的生理药代动力学（AI-PBPK）模型，以预测醛固酮合酶抑制剂（ASI）的药代动力学（PK）和药效学（PD）特性，从而在药物发现阶段能够选择出具有高效能和良好选择性的合适候选药物。在一个基于网络的平台上，开发了一种整合机器学习和经典PBPK模型用于ASI的PK模拟的AI-PBPK模型。选择了拥有最多可用临床数据的巴多司他作为模型化合物。在使用已发表数据进行校准和验证后，该模型被应用于估计巴多司他、地氟司他、洛伦司他、BI689648以及11β-羟化酶抑制剂LCI699的PK参数。基于血浆游离药物浓度预测了所有五种化合物的PD。结果表明，在一定误差范围内，可以从ASI的结构式推断其PK/PD特性，为早期ASI先导化合物的筛选和优化提供参考。对该模型的进一步验证和完善将提高其预测准确性，并扩大其在药物发现中的应用。
