Human Intestinal Transporter Database: QSAR Modeling and Virtual Profiling of Drug Uptake, Efflux and Interactions
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Membrane 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.
Using 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.
QSAR 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.
The 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.
KEY WORDSADMET drug transport efflux membrane transport proteins permeability
ATP binding cassette family of transporters
absorption distribution, metabolism, excretion, toxicity
apical sodium-dependent bile acid transporter
area under curve
breast cancer resistance protein
correct classification rate
k nearest neighbors
monocarboxylate transporter 1
multidrug resistance protein 1
multidrug resistance-associated proteins 1-4
organic anion transporting polypeptide 2B1
organic cation transporter 1
organic solute transporter alpha/beta
peptide transporter 1
quantitative structure-activity relationships
receiver operating characteristic
- SLC transporters
solute carrier family of transporters
support vector machines
tanimoto (similarity) coefficient
Acknowledgments and disclosures
We thank Dr. Tingjun Hou (University of California at San Diego, USA) and Dr. Kazuya Maeda (The University of Tokyo, Japan) for sharing their data, Dr. Dhiren Thakker, Dr. Kim Brouwer and Kathleen Köck (all - University of North Carolina at Chapel Hill, USA), Dr. Alexander Böcker and Dr. Sanjay Srivastava (Boehringer Ingelheim (Canada) Ltd) for helpful discussions, Dr. Nancy Baker for her assistance with ChemoText, and Dr. Fabio Broccatelli (University of Perugio, Italy) for the comments on MDR1 inhibition. This work was supported, in part, by grants from NIH (GM66940 and R21GM076059), The Johns Hopkins Center for Alternatives to Animal Testing (20011–21) and Boehringer Ingelheim (Canada) Ltd.
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