期刊
PHARMACEUTICAL RESEARCH
卷 31, 期 4, 页码 1002-1014出版社
SPRINGER/PLENUM PUBLISHERS
DOI: 10.1007/s11095-013-1222-1
关键词
drugs; intestinal membrane transporter; oral bioavailability; QSAR
资金
- National Institute of Environmental Health Sciences of the National Institutes of Health [R15ES023148]
- Colgate-Palmolive Grant for Alternative Research
Oral bioavailability (%F) is a key factor that determines the fate of a new drug in clinical trials. Traditionally, %F is measured using costly and time-consuming experimental tests. Developing computational models to evaluate the %F of new drugs before they are synthesized would be beneficial in the drug discovery process. We employed Combinatorial Quantitative Structure-Activity Relationship approach to develop several computational %F models. We compiled a %F dataset of 995 drugs from public sources. After generating chemical descriptors for each compound, we used random forest, support vector machine, k nearest neighbor, and CASE Ultra to develop the relevant QSAR models. The resulting models were validated using five-fold cross-validation. The external predictivity of %F values was poor (R-2 = 0.28, n = 995, MAE = 24), but was improved (R-2 = 0.40, n = 362, MAE = 21) by filtering unreliable predictions that had a high probability of interacting with MDR1 and MRP2 transporters. Furthermore, classifying the compounds according to the %F values (%F < 50% as low, %F a parts per thousand yenaEuro parts per thousand 50% as 'high) and developing category QSAR models resulted in an external accuracy of 76%. In this study, we developed predictive %F QSAR models that could be used to evaluate new drug compounds, and integrating drug-transporter interactions data greatly benefits the resulting models.
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