ArticleIn silico pharmacology2015
Computational modeling to predict the functions and impact of drug transporters.
Article in In silico pharmacology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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
7 citing papers in PubMed.
- Explaining Blood-Brain Barrier Permeability of Small Molecules by Integrated Analysis of Different Transport Mechanisms.Journal of medicinal chemistry · 2023Article
- Molecular Properties of Drugs Handled by Kidney OATs and Liver OATPs Revealed by Chemoinformatics and Machine Learning: Implications for Kidney and Liver Disease.Pharmaceutics · 2021Article
- Unique metabolite preferences of the drug transporters OAT1 and OAT3 analyzed by machine learning.The Journal of biological chemistry · 2020Article
- Advances in Glioblastoma Multiforme Treatment: New Models for Nanoparticle Therapy.Frontiers in physiology · 2018Review
- Chemical Modulation of the Human Oligopeptide Transporter 1, hPepT1.Molecular pharmaceutics · 2017Article
- Rosetta Broker for membrane protein structure prediction: concentrative nucleoside transporter 3 and corticotropin-releasing factor receptor 1 test cases.BMC structural biology · 2017Article
- Molecular Properties of Drugs Interacting with SLC22 Transporters OAT1, OAT3, OCT1, and OCT2: A Machine-Learning Approach.The Journal of pharmacology and experimental therapeutics · 2016Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transport proteins are important mediators of cellular drug influx and efflux and play crucial roles in drug distribution, disposition and clearance. Drug-drug interactions have increasingly been found to occur at the transporter level and, hence, computational tools for studying drug-transporter interactions have gained in interest. In this short review, we present the most important transport proteins for drug influx and efflux. Computational tools for predicting and understanding the substrate and inhibitor interactions with these membrane-bound proteins are discussed. We have primarily focused on ligand-based and structure-based modeling, for which the state-of-the-art and future challenges are also discussed.
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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.