Nigam Anisha K, Ojha Anupam A, Li Julia G, Shi Da, Bhatnagar Vibha, Nigam Kabir B, Abagyan Ruben, Nigam Sanjay K
Skaggs School of Pharmacy, University of California San Diego, La Jolla, CA 92093, USA.
Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, USA.
Pharmaceutics. 2021 Oct 18;13(10):1720. doi: 10.3390/pharmaceutics13101720.
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.
对于患有肝脏或肾脏疾病的患者，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，许多药物会被肝脏摄取以进行代谢和/或胆汁排泄，或者被肾脏摄取以进行肾脏排泄。许多常见药物是有机阴离子。肝脏中有机阴离子药物的主要摄取转运体是有机阴离子转运多肽（OATP1B1或SLCO1B1；OATP1B3或SLCO1B3），而在肾脏中则是有机阴离子转运体（OAT1或SLC22A6；OAT3或SLC22A8）。由于这些特定的OATP主要存在于肝脏而非肾脏中，而这些OAT主要存在于肾脏而非肝脏中，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转运的药物与肾脏OAT转运的药物。我们对与OATP和OAT相互作用的药物的30多种定量物理化学性质的分析揭示了八种性质，基于机器学习（例如随机森林、k近邻）和深度学习分类算法，这些性质结合起来表明与“肝脏”转运体而非“肾脏”转运体相互作用的可能性很高。肝脏OATP更喜欢具有更高疏水性、更高复杂性和更多环状结构的药物，而肾脏OAT更喜欢具有更多羧基的极性更强的药物。这些结果为组织特异性靶向策略、理解药物-药物相互作用以及药物-代谢物相互作用提供了强有力的分子基础，并提出了一种在慢性肝脏或肾脏疾病（CKD）中如何选择具有可比疗效的药物以最小化毒性的策略。
