ArticlePharmaceutics2021
Molecular Properties of Drugs Handled by Kidney OATs and Liver OATPs Revealed by Chemoinformatics and Machine Learning: Implications for Kidney and Liver Disease.
Article in Pharmaceutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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.
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.
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Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- Article
- A review on integrated machine learning and deep learning driven artificial intelligence models for pharmacokinetics and toxicokinetics predictions, and their application.Drug metabolism and disposition: the biological fate of chemicals · 2026Review
- Distinguishing Molecular Properties of OAT, OATP, and MRP Drug Substrates by Machine Learning.Pharmaceutics · 2024Article
- VARIDT 3.0: the phenotypic and regulatory variability of drug transporter.Nucleic acids research · 2024Article
- Liposome-Based Silibinin for Mitigating Nonalcoholic Fatty Liver Disease: Dual Effects via Parenteral and Intestinal Routes.ACS pharmacology & translational science · 2023Article
- Cryo-EM structures of human organic anion transporting polypeptide OATP1B1.Cell research · 2023Article
- Machine Learning Techniques Applied to the Study of Drug Transporters.Molecules (Basel, Switzerland) · 2023Review
- Loss of the Kidney Urate Transporter, Urat1, Leads to Disrupted Redox Homeostasis in Mice.Antioxidants (Basel, Switzerland) · 2023Article
- Machine Learning Models Identify New Inhibitors for Human OATP1B1.Molecular pharmaceutics · 2022Article
- The Interplay between Uremic Toxins and Albumin, Membrane Transporters and Drug Interaction.Toxins · 2022Review
- Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
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.
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Registered trials
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.