Computational models for drug inhibition of the human apical sodium-dependent bile acid transporter.
Computational models for drug inhibition of the human apical sodium-dependent bile acid transporter.
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DOI：
10.1021/mp900163d
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发表时间：
2009-09
影响因子：
4.9
通讯作者：
Polli JE
中科院分区：
文献类型：
--
作者：
Zheng X;Ekins S;Raufman JP;Polli JE
The human apical sodium-dependent bile acid transporter (ASBT; SLC10A2) is the primary mechanism for intestinal bile acid re-absorption. In the colon, secondary bile acids increase the risk of cancer. Therefore, drugs that inhibit ASBT have the potential to increase the risk of colon cancer. The objectives of this study were to identify FDA-approved drugs that inhibit ASBT and to derive computational models for ASBT inhibition. Inhibition was evaluated using ASBT-MDCK monolayers and taurocholate as the model substrate. Computational modeling employed a HipHop qualitative approach, a Hypogen quantitative approach, as well as a modified Laplacian Bayesian modeling method using 2D descriptors. Initially, 30 compounds were screened for ASBT inhibition. A qualitative pharmacophore was developed using the most potent 11 compounds and applied to search a drug database, yielding 58 hits. Additional compounds were tested and their Ki values were measured. A 3D-QSAR and a Bayesian model were developed using 38 molecules. The quantitative pharmacophore consisted of one hydrogen bond acceptor, three hydrophobic features, and five excluded volumes. Each model was further validated with two external test sets of 30 and 19 molecules. Validation analysis showed both models exhibited good predictability in determining whether a drug is a potent or non-potent ASBT inhibitor. The Bayesian model correctly ranked the most active compounds. In summary, using a combined in vitro and computational approach, we found that many FDA-approved drugs from diverse classes, such as the dihydropyridine calcium channel blockers and HMG CoA-reductase inhibitors, are ASBT inhibitors.
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影响因子：
2.6
作者：
Assimes, Themistocles L.;Elstein, Eleanor;Suissa, Samy
通讯作者：
Suissa, Samy
影响因子：
3.7
作者：
Balakrishnan, A;Sussman, DJ;Polli, JE
通讯作者：
Polli, JE
影响因子：
4.4
作者：
Dawson, Paul A.;Oelkers, Peter
通讯作者：
Oelkers, Peter
影响因子：
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作者：
Abraham, Bincy;Sellin, Joseph H
通讯作者：
Sellin, Joseph H
影响因子：
3.1
作者：
CHAWLA, A;KARL, PI;SCHNEIDER, BL
通讯作者：
SCHNEIDER, BL