van den Maagdenberg Helle W, de Mol van Otterloo Jikke, van Hasselt J G Coen, van der Graaf Piet H, van Westen Gerard J P
Leiden Academic Centre of Drug Research, Leiden University, 2333, Leiden, The Netherlands.
Certara, CT2 7FG Canterbury, U.K.
J Chem Inf Model. 2025 May 26;65(10):4783-4796. doi: 10.1021/acs.jcim.5c00107. Epub 2025 May 9.
Integrated understanding of pharmacokinetics (PK) and pharmacodynamics (PD) is a key aspect of successful drug discovery. Yet in generative computational drug design, the focus often lies on optimizing potency. Here we integrate PK property predictions in DrugEx, a generative drug design framework and we explore the generated compounds' PD through simulations with a quantitative systems pharmacology (QSP) model. Quantitative structure-property relationship models were developed to predict molecule PK (clearance, volume of distribution and unbound fraction) and affinity for the Adenosine AR receptor (AR), a drug target in immuno-oncology. These models were used to score compounds in a reinforcement learning framework to generate molecules with a specific PK profile and high affinity for the AR. We predicted the expected tumor growth inhibition profiles using the QSP model for selected candidate molecules with varying PK and affinity profiles. We show that optimizing affinity to the AR, while minimizing or maximizing a PK property, shifts the type of molecular scaffolds that are generated. The difference in physicochemical properties of the compounds with different predicted PK parameters was found to correspond with the differences observed in the PK data set. We demonstrated the use of the QSP model by simulating the effect of a broad range of compound properties on the predicted tumor volume. In conclusion, our proposed integrated workflow incorporating affinity predictions with PKPD may provide a template for the next generation of advanced generative computational drug design.
对药代动力学（PK）和药效学（PD）的综合理解是成功进行药物发现的关键方面。然而，在生成式计算药物设计中，重点通常在于优化效力。在此，我们将PK性质预测整合到DrugEx（一个生成式药物设计框架）中，并通过定量系统药理学（QSP）模型的模拟来探索生成化合物的PD。我们开发了定量结构 - 性质关系模型来预测分子的PK（清除率、分布容积和游离分数）以及对腺苷A受体（AR，免疫肿瘤学中的一个药物靶点）的亲和力。这些模型用于在强化学习框架中对化合物进行评分，以生成具有特定PK谱且对AR具有高亲和力的分子。我们使用QSP模型预测了具有不同PK和亲和力谱的选定候选分子的预期肿瘤生长抑制谱。我们表明，在最小化或最大化PK性质的同时优化对AR的亲和力，会改变所生成分子支架的类型。发现具有不同预测PK参数的化合物在物理化学性质上的差异与PK数据集中观察到的差异相对应。我们通过模拟广泛的化合物性质对预测肿瘤体积的影响，展示了QSP模型的用途。总之，我们提出的将亲和力预测与PKPD相结合的综合工作流程可能为下一代先进的生成式计算药物设计提供一个模板。
