Molecular Properties of Drugs Handled by Kidney OATs and Liver OATPs Revealed by Chemoinformatics and Machine Learning: Implications for Kidney and Liver Disease.
Molecular Properties of Drugs Handled by Kidney OATs and Liver OATPs Revealed by Chemoinformatics and Machine Learning: Implications for Kidney and Liver Disease.
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化学信息学和机器学习揭示的肾燕麦和肝燕麦处理药物的分子特性：对肾脏和肝脏疾病的影响。
DOI：
10.3390/pharmaceutics13101720
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发表时间：
2021-10-18
期刊：
影响因子：
5.4
通讯作者：
Nigam SK
中科院分区：
文献类型：
--
作者：
Nigam AK;Ojha AA;Li JG;Shi D;Bhatnagar V;Nigam KB;Abagyan R;Nigam SK
在肝脏或肾脏疾病患者中，考虑小分子药物的代谢和消除途径尤为重要。一旦进入血液，许多药物被肝脏吸收用于代谢和/或胆汁消除，或被肾脏吸收用于肾脏消除。许多常见的药物是有机阴离子。有机阴离子药物的主要肝脏摄取转运蛋白是有机阴离子转运蛋白多肽（OATP 1B 1或SLCO 1B 1; OATP 1B 3或SLCO 1B 3），而在肾脏中，它们是有机阴离子转运蛋白（OAT 1或SLC 22 A6; OAT 3或SLC 22 A8）。由于这些特定的OATP绝大多数存在于肝脏中而不是肾脏中，并且这些OAT绝大多数存在于肾脏中而不是肝脏中，因此可以使用化学信息学，机器学习（ML）和深度学习来分析肝脏OATP转运药物与肾脏OAT转运药物。我们对OATP和OAT相互作用药物的>30种定量物理化学性质的分析揭示了8种性质，这些性质组合起来表明基于机器学习与“肝脏”转运蛋白相对于“肾脏”转运蛋白的相互作用倾向高（例如，随机森林，k-最近邻）和深度学习分类算法。肝脏OATPs偏好具有更大疏水性、更高复杂性和更多环状结构的药物，而肾脏OATs偏好具有更多羧基的极性药物。这些结果为组织特异性靶向策略提供了强有力的分子基础，了解了药物-药物相互作用以及药物-代谢物相互作用，并提出了如何在慢性肝脏或肾脏疾病（CKD）中选择具有可比疗效的药物以最大限度地减少毒性的策略。
In patients with liver or kidney disease, it is especially important to consider the routes of metabolism and elimination of small-molecule pharmaceuticals. Once in the blood, numerous drugs are taken up by the liver for metabolism and/or biliary elimination, or by the kidney for renal elimination. Many common drugs are organic anions. The major liver uptake transporters for organic anion drugs are organic anion transporter polypeptides (OATP1B1 or SLCO1B1; OATP1B3 or SLCO1B3), whereas in the kidney they are organic anion transporters (OAT1 or SLC22A6; OAT3 or SLC22A8). Since these particular OATPs are overwhelmingly found in the liver but not the kidney, and these OATs are overwhelmingly found in the kidney but not liver, it is possible to use chemoinformatics, machine learning (ML) and deep learning to analyze liver OATP-transported drugs versus kidney OAT-transported drugs. Our analysis of >30 quantitative physicochemical properties of OATP- and OAT-interacting drugs revealed eight properties that in combination, indicate a high propensity for interaction with “liver” transporters versus “kidney” ones based on machine learning (e.g., random forest, k-nearest neighbors) and deep-learning classification algorithms. Liver OATPs preferred drugs with greater hydrophobicity, higher complexity, and more ringed structures whereas kidney OATs preferred more polar drugs with more carboxyl groups. The results provide a strong molecular basis for tissue-specific targeting strategies, understanding drug–drug interactions as well as drug–metabolite interactions, and suggest a strategy for how drugs with comparable efficacy might be chosen in chronic liver or kidney disease (CKD) to minimize toxicity.
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影响因子：
4.3
作者：
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通讯作者：
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影响因子：
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