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

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),...

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Main Authors: Anisha K. Nigam (Author), Jeremiah D. Momper (Author), Anupam Anand Ojha (Author), Sanjay K. Nigam (Author)
Format: Book
Published: MDPI AG, 2024-04-01T00:00:00Z.
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001 doaj_22cfc76d521c43cf8c27e293b99e5443
042 |a dc 
100 1 0 |a Anisha K. Nigam  |e author 
700 1 0 |a Jeremiah D. Momper  |e author 
700 1 0 |a Anupam Anand Ojha  |e author 
700 1 0 |a Sanjay K. Nigam  |e author 
245 0 0 |a Distinguishing Molecular Properties of OAT, OATP, and MRP Drug Substrates by Machine Learning 
260 |b MDPI AG,   |c 2024-04-01T00:00:00Z. 
500 |a 10.3390/pharmaceutics16050592 
500 |a 1999-4923 
520 |a 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. 
546 |a EN 
690 |a drug transport 
690 |a machine learning 
690 |a AI 
690 |a organ crosstalk 
690 |a gut microbiome 
690 |a proximal tubule 
690 |a Pharmacy and materia medica 
690 |a RS1-441 
655 7 |a article  |2 local 
786 0 |n Pharmaceutics, Vol 16, Iss 5, p 592 (2024) 
787 0 |n https://www.mdpi.com/1999-4923/16/5/592 
787 0 |n https://doaj.org/toc/1999-4923 
856 4 1 |u https://doaj.org/article/22cfc76d521c43cf8c27e293b99e5443  |z Connect to this object online.