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Article

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

by
Anisha K. Nigam
1,†,
Jeremiah D. Momper
1,*,†,
Anupam Anand Ojha
2 and
Sanjay K. Nigam
3
1
Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA 92093, USA
2
Department of Chemistry and Biochemistry, University of California, San Diego, CA 92093, USA
3
Departments of Pediatrics and Medicine (Nephrology), University of California, San Diego, CA 92093, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Pharmaceutics 2024, 16(5), 592; https://doi.org/10.3390/pharmaceutics16050592
Submission received: 2 February 2024 / Revised: 11 April 2024 / Accepted: 18 April 2024 / Published: 26 April 2024

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.
Keywords: drug transport; machine learning; AI; organ crosstalk; gut microbiome; proximal tubule; hepatocyte; SLC22A6; SLC22A8; SLCO1B1; SLCO1B3; ABCC2; ABCC4 drug transport; machine learning; AI; organ crosstalk; gut microbiome; proximal tubule; hepatocyte; SLC22A6; SLC22A8; SLCO1B1; SLCO1B3; ABCC2; ABCC4

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MDPI and ACS Style

Nigam, A.K.; Momper, J.D.; Ojha, A.A.; Nigam, S.K. Distinguishing Molecular Properties of OAT, OATP, and MRP Drug Substrates by Machine Learning. Pharmaceutics 2024, 16, 592. https://doi.org/10.3390/pharmaceutics16050592

AMA Style

Nigam AK, Momper JD, Ojha AA, Nigam SK. Distinguishing Molecular Properties of OAT, OATP, and MRP Drug Substrates by Machine Learning. Pharmaceutics. 2024; 16(5):592. https://doi.org/10.3390/pharmaceutics16050592

Chicago/Turabian Style

Nigam, Anisha K., Jeremiah D. Momper, Anupam Anand Ojha, and Sanjay K. Nigam. 2024. "Distinguishing Molecular Properties of OAT, OATP, and MRP Drug Substrates by Machine Learning" Pharmaceutics 16, no. 5: 592. https://doi.org/10.3390/pharmaceutics16050592

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