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通过机器学习区分OAT、OATP和MRP药物底物的分子特性

Distinguishing Molecular Properties of OAT, OATP, and MRP Drug Substrates by Machine Learning.

作者信息

Nigam Anisha K, Momper Jeremiah D, Ojha Anupam Anand, Nigam Sanjay K

机构信息

Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA 92093, USA.

Department of Chemistry and Biochemistry, University of California, San Diego, CA 92093, USA.

出版信息

Pharmaceutics. 2024 Apr 26;16(5):592. doi: 10.3390/pharmaceutics16050592.

Abstract

The movement of organic anionic drugs across cell membranes is partly governed by interactions with SLC and ABC transporters in the intestine, liver, kidney, blood-brain barrier, placenta, breast, and other tissues. Major transporters involved include organic anion transporters (OATs, SLC22 family), organic anion transporting polypeptides (OATPs, SLCO family), and multidrug resistance proteins (MRPs, ABCC family). However, the sets of molecular properties of drugs that are necessary for interactions with OATs (OAT1, OAT3) vs. OATPs (OATP1B1, OATP1B3) vs. MRPs (MRP2, MRP4) are not well-understood. Defining these molecular properties is necessary for a better understanding of drug and metabolite handling across the gut-liver-kidney axis, gut-brain axis, and other multi-organ axes. It is also useful for tissue targeting of small molecule drugs and predicting drug-drug interactions and drug-metabolite interactions. Here, we curated a database of drugs shown to interact with these transporters in vitro and used chemoinformatic approaches to describe their molecular properties. We then sought to define sets of molecular properties that distinguish drugs interacting with OATs, OATPs, and MRPs in binary classifications using machine learning and artificial intelligence approaches. We identified sets of key molecular properties (e.g., rotatable bond count, lipophilicity, number of ringed structures) for classifying OATs vs. MRPs and OATs vs. OATPs. However, sets of molecular properties differentiating OATP vs. MRP substrates were less evident, as drugs interacting with MRP2 and MRP4 do not form a tight group owing to differing hydrophobicity and molecular complexity for interactions with the two transporters. If the results also hold for endogenous metabolites, they may deepen our knowledge of organ crosstalk, as described in the Remote Sensing and Signaling Theory. The results also provide a molecular basis for understanding how small organic molecules differentially interact with OATs, OATPs, and MRPs.

摘要

有机阴离子药物跨细胞膜的转运部分受其与肠道、肝脏、肾脏、血脑屏障、胎盘、乳腺及其他组织中的溶质载体(SLC)和ATP结合盒(ABC)转运蛋白相互作用的调控。涉及的主要转运蛋白包括有机阴离子转运体(OATs,SLC22家族)、有机阴离子转运多肽(OATPs,SLCO家族)和多药耐药蛋白(MRPs,ABCC家族)。然而,对于药物与OATs(OAT1、OAT3)、OATPs(OATP1B1、OATP1B3)以及MRPs(MRP2、MRP4)相互作用所必需的分子特性组合,我们还了解得不够透彻。明确这些分子特性对于更好地理解药物和代谢物在肠-肝-肾轴、肠-脑轴及其他多器官轴中的处理过程至关重要。这对于小分子药物的组织靶向以及预测药物-药物相互作用和药物-代谢物相互作用也很有帮助。在此,我们精心构建了一个体外显示与这些转运蛋白相互作用药物的数据库,并使用化学信息学方法描述它们的分子特性。然后,我们试图利用机器学习和人工智能方法,在二元分类中定义区分与OATs、OATPs和MRPs相互作用药物的分子特性集。我们确定了用于区分OATs与MRPs以及OATs与OATPs的关键分子特性集(例如可旋转键数、亲脂性、环状结构数量)。然而,区分OATP与MRP底物的分子特性集不太明显, 因为与MRP2和MRP4相互作用药物由于与这两种转运蛋白相互作用的疏水性和分子复杂性不同,并未形成紧密的类别。如果这些结果也适用于内源性代谢物,那么它们可能会加深我们对如遥感与信号理论中所述器官间串扰的认识。这些结果还为理解小分子有机化合物如何与OATs、OATPs和MRPs产生差异相互作用提供了分子基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f82/11125978/49acce9af47c/pharmaceutics-16-00592-g001.jpg

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