ArticlePharmaceutical research2013
Human intestinal transporter database: QSAR modeling and virtual profiling of drug uptake, efflux and interactions.
Article in Pharmaceutical research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 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.
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Who cites it
46 citing papers in PubMed, 92 citations in OpenAlex.
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- Screening oral drugs for their interactions with the intestinal transportome via porcine tissue explants and machine learning.Nature biomedical engineering · 2024Article
- Ensemble Machine Learning Approaches Based on Molecular Descriptors and Graph Convolutional Networks for Predicting the Efflux Activities of MDR1 and BCRP Transporters.The AAPS journal · 2023Article
- Towards best use and regulatory acceptance of generic physiologically based kinetic (PBK) models for in vitro-to-in vivo extrapolation (IVIVE) in chemical risk assessment.Archives of toxicology · 2022Article
- Machine Learning Models Identify New Inhibitors for Human OATP1B1.Molecular pharmaceutics · 2022Article
- Pharmacophore-Based Discovery of Substrates of a Novel Drug/Proton-Antiporter in the Human Brain Endothelial hCMEC/D3 Cell Line.Pharmaceutics · 2022Article
- Direct Comparison of the Prediction of the Unbound Brain-to-Plasma Partitioning Utilizing Machine Learning Approach and Mechanistic Neuropharmacokinetic Model.The AAPS journal · 2021Article
- Current Models for Predicting Drug-induced Cholestasis: The Role of Hepatobiliary Transport System.Iranian journal of pharmaceutical research : IJPR · 2021Review
- ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning.Journal of cheminformatics · 2020Article
- Membrane transporter data to support kinetically-informed chemical risk assessment using non-animal methods: Scientific and regulatory perspectives.Environment international · 2019Review
- An In Silico Approach for Assessment of the Membrane Transporter Activities of Phenols: A Case Study Based on Computational Models of Transport Activity for the Transporter Bilitranslocase.Molecules (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 3 countries.
Funding
Abstract
purposeMembrane transporters mediate many biological effects of chemicals and play a major role in pharmacokinetics and drug resistance. The selection of viable drug candidates among biologically active compounds requires the assessment of their transporter interaction profiles.
methodsUsing public sources, we have assembled and curated the largest, to our knowledge, human intestinal transporter database (>5,000 interaction entries for >3,700 molecules). This data was used to develop thoroughly validated classification Quantitative Structure-Activity Relationship (QSAR) models of transport and/or inhibition of several major transporters including MDR1, BCRP, MRP1-4, PEPT1, ASBT, OATP2B1, OCT1, and MCT1.
resultsQSAR models have been developed with advanced machine learning techniques such as Support Vector Machines, Random Forest, and k Nearest Neighbors using Dragon and MOE chemical descriptors. These models afforded high external prediction accuracies of 71-100% estimated by 5-fold external validation, and showed hit retrieval rates with up to 20-fold enrichment in the virtual screening of DrugBank compounds.
conclusionsThe compendium of predictive QSAR models developed in this study can be used for virtual profiling of drug candidates and/or environmental agents with the optimal transporter profiles.
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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.