{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:08:16Z","timestamp":1791925696509,"version":"4.3.4"},"reference-count":23,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute of Diabetes, Digestive, and Kidney Diseases","award":["R01 DK109392"],"award-info":[{"award-number":["R01 DK109392"]}]},{"name":"National Institute of Diabetes, Digestive, and Kidney Diseases","award":["P50HD106463"],"award-info":[{"award-number":["P50HD106463"]}]},{"name":"National Institute of Child Health and Human Development","award":["R01 DK109392"],"award-info":[{"award-number":["R01 DK109392"]}]},{"name":"National Institute of Child Health and Human Development","award":["P50HD106463"],"award-info":[{"award-number":["P50HD106463"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pharmaceutics"],"abstract":"<jats:p>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\u2013brain 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\u2013liver\u2013kidney axis, gut\u2013brain axis, and other multi-organ axes. It is also useful for tissue targeting of small molecule drugs and predicting drug\u2013drug interactions and drug\u2013metabolite 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.<\/jats:p>","DOI":"10.3390\/pharmaceutics16050592","type":"journal-article","created":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T03:57:56Z","timestamp":1714622276000},"page":"592","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Distinguishing Molecular Properties of OAT, OATP, and MRP Drug Substrates by Machine Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Anisha K.","family":"Nigam","sequence":"first","affiliation":[{"name":"Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeremiah D.","family":"Momper","sequence":"additional","affiliation":[{"name":"Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6588-3092","authenticated-orcid":false,"given":"Anupam Anand","family":"Ojha","sequence":"additional","affiliation":[{"name":"Department of Chemistry and Biochemistry, University of California, San Diego, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanjay K.","family":"Nigam","sequence":"additional","affiliation":[{"name":"Departments of Pediatrics and Medicine (Nephrology), University of California, San Diego, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"746208","DOI":"10.3389\/fphar.2021.746208","article-title":"Drug Transporters in the Kidney: Perspectives on Species Differences, Disease Status, and Molecular Docking","volume":"12","author":"Zou","year":"2021","journal-title":"Front. Pharmacol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1038\/nrd4461","article-title":"What do drug transporters really do?","volume":"14","author":"Nigam","year":"2015","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1080\/17425255.2020.1755253","article-title":"Role of SLC transporters in toxicity induced by anticancer drugs","volume":"16","author":"Huang","year":"2020","journal-title":"Expert Opin. Drug Metab. Toxicol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1111\/j.1476-5381.2009.00430.x","article-title":"Impact of OATP transporters on pharmacokinetics","volume":"158","author":"Kalliokoski","year":"2009","journal-title":"Br. J. Pharmacol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1152\/physrev.00009.2021","article-title":"Regulation of solute carriers oct2 and OAT1\/3 in the kidney: A phylogenetic, ontogenetic, and cell dynamic perspective","volume":"102","author":"Jansen","year":"2022","journal-title":"Physiol. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108271","DOI":"10.1016\/j.pharmthera.2022.108271","article-title":"The next frontier in ADME science: Predicting transporter-based drug disposition, tissue concentrations and drug-drug interactions in humans","volume":"238","author":"Storelli","year":"2022","journal-title":"Pharmacol. Ther."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1146\/annurev-pharmtox-030322-084058","article-title":"OAT, OATP, and MRP Drug Transporters and the Remote Sensing and Signaling Theory","volume":"63","author":"Nigam","year":"2023","journal-title":"Annu. Rev. Pharmacol. Toxicol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1002\/cpt.2644","article-title":"Transporters in Drug Development: International Transporter Consortium Update on Emerging Transporters of Clinical Importance","volume":"112","author":"Sangha","year":"2022","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1260","DOI":"10.1111\/j.1476-5381.2011.01724.x","article-title":"OATPs, OATs and OCTs: The organic anion and cation transporters of the SLCO and SLC22A gene superfamilies","volume":"165","author":"Roth","year":"2012","journal-title":"Br. J. Pharmacol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107647","DOI":"10.1016\/j.pharmthera.2020.107647","article-title":"Regulation of organic anion transporters: Role in physiology, pathophysiology, and drug elimination","volume":"217","author":"Zhang","year":"2021","journal-title":"Pharmacol. Ther."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1208\/s12248-015-9757-1","article-title":"Multidrug Resistance Proteins (MRPs) and Cancer Therapy","volume":"17","author":"Zhang","year":"2015","journal-title":"AAPS J."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Murtaza, A.A., Saher, A., Mohyuddin, H., Moosavi, S.K., Zafar, M.H., and Sanfilippo, F. (2023, January 28\u201330). Enhancing Cardiovascular Disease Prediction via Hybrid Deep Learning Architectures: A Step Towards Smart Healthcare. Proceedings of the 2023 2nd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE), Lahore, Pakistan.","DOI":"10.1109\/ETECTE59617.2023.10396716"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1002\/cpt.1818","article-title":"The Systems Biology of Drug Metabolizing Enzymes and Transporters: Relevance to Quantitative Systems Pharmacology","volume":"108","author":"Nigam","year":"2020","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/978-981-13-7647-4_5","article-title":"Transporter-Mediated Drug-Drug Interactions and Their Significance","volume":"1141","author":"Liu","year":"2019","journal-title":"Adv. Exp. Med. Biol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1007\/s40262-017-0506-8","article-title":"Renal Drug Transporters and Drug Interactions","volume":"56","author":"Ivanyuk","year":"2017","journal-title":"Clin. Pharmacokinet."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.1074\/jbc.RA119.010729","article-title":"Unique metabolite preferences of the drug transporters OAT1 and OAT3 analyzed by machine learning","volume":"295","author":"Nigam","year":"2020","journal-title":"J. Biol. Chem."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Nigam, A.K., Ojha, A.A., Li, J.G., Shi, D., Bhatnagar, V., Nigam, K.B., Abagyan, R., and Nigam, S.K. (2021). Molecular Properties of Drugs Handled by Kidney OATs and Liver OATPs Revealed by Chemoinformatics and Machine Learning: Implications for Kidney and Liver Disease. Pharmaceutics, 13.","DOI":"10.3390\/pharmaceutics13101720"},{"key":"ref_18","first-page":"2349","article-title":"Orange: Data mining toolbox in python","volume":"14","author":"Demsar","year":"2013","journal-title":"J. Mach. Learn. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1016\/j.jbi.2007.03.010","article-title":"FreeViz\u2013an intelligent multi- variate visualization approach to explorative analysis of biomedical data","volume":"40","author":"Demsar","year":"2007","journal-title":"J. Biomed. Inform."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kong, X., Lin, K., Wu, G., Tao, X., Zhai, X., Lv, L., Dong, D., Zhu, Y., and Yang, S. (2023). Machine Learning Techniques Applied to the Study of Drug Transporters. Molecules, 28.","DOI":"10.3390\/molecules28165936"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4320","DOI":"10.1021\/acs.molpharmaceut.2c00662","article-title":"Machine Learning Models Identify New Inhibitors for Human OATP1B1","volume":"19","author":"Lane","year":"2022","journal-title":"Mol. Pharm."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1124\/jpet.116.232660","article-title":"Molecular Properties of Drugs Interacting with SLC22 Transporters OAT1, OAT3, OCT1, and OCT2: A Machine-Learning Approach","volume":"359","author":"Liu","year":"2016","journal-title":"J. Pharmacol. Exp. Ther."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s13321-020-00479-8","article-title":"Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models","volume":"13","author":"Jiang","year":"2021","journal-title":"J. Cheminform."}],"container-title":["Pharmaceutics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4923\/16\/5\/592\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:34:22Z","timestamp":1760106862000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4923\/16\/5\/592"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,26]]},"references-count":23,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["pharmaceutics16050592"],"URL":"https:\/\/doi.org\/10.3390\/pharmaceutics16050592","relation":{},"ISSN":["1999-4923"],"issn-type":[{"value":"1999-4923","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,26]]}}}