Evidence map›Paper›PMID 38794254›Full record

ArticlePharmaceutics2024

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

Anisha K Nigam, Jeremiah D Momper, Anupam Anand Ojha, Sanjay K Nigam

Abstract read
In one paragraph

Article in Pharmaceutics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Associations Between Gut Microbiota and Tissue Exposure ofPharmaceuticals (Basel, Switzerland) · 2026
    Article
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  3. Review
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  5. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Anisha K NigamSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA 92093, USA.
Jeremiah D MomperSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA 92093, USA.
Anupam Anand OjhaDepartment of Chemistry and Biochemistry, University of California, San Diego, CA 92093, USA.ORCID 0000-0001-6588-3092
Sanjay K NigamDepartments of Pediatrics and Medicine (Nephrology), University of California, San Diego, CA 92093, USA.

Funding

The impact of ampicillin and breast milk oligosaccharides on the infant microbiome and immune functionsP50HD106463 · NICHD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Lars Bode, CHRISTINA CHAMBERS · 2021 to 2026
$19.6M
The Role of OAT1 in UremiaR01DK109392 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI NIGAM, SANJAY K · 2017 to 2025
$3.8M
NICHD NIH HHS P50 HD106463NIDDK NIH HHS R01 DK109392
6 · The paper itself

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.

Indexed as

ABCC2ABCC4AIdrug transportgut microbiomehepatocytemachine learningorgan crosstalkproximal tubuleSLC22A6SLC22A8SLCO1B1SLCO1B3

Identifiers

PMID38794254
PMCPMC11125978

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.