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通过BSEP抑制剂的基于3D配体的药效团模型对潜在胆汁淤积化合物进行计算机模拟鉴定和体外验证。

In silico identification and in vitro validation of potential cholestatic compounds through 3D ligand-based pharmacophore modeling of BSEP inhibitors.

作者信息

Ritschel Tina, Hermans Susanne M A, Schreurs Marieke, van den Heuvel Jeroen J M W, Koenderink Jan B, Greupink Rick, Russel Frans G M

机构信息

Computational Discovery and Design (CDD) Group, Centre for Molecular and Biomolecular Informatics (CMBI), Radboud university medical center , P.O. Box 9101, 6500 HB Nijmegen, The Netherlands.

出版信息

Chem Res Toxicol. 2014 May 19;27(5):873-81. doi: 10.1021/tx5000393. Epub 2014 Apr 23.

Abstract

Drug-induced cholestasis is a frequently observed side effect of drugs and is often caused by an unexpected interaction with the bile salt export pump (BSEP/ABCB11). BSEP is the key membrane transporter responsible for the transport of bile acids from hepatocytes into bile. Here, we developed a pharmacophore model that describes the molecular features of compounds associated with BSEP inhibitory activity. To generate input and validation data sets, in vitro experiments with membrane vesicles overexpressing human BSEP were used to assess the effect of compounds (50 μM) on BSEP-mediated (3)H-taurocholic acid transport. The model contains two hydrogen bond acceptor/anionic features, two hydrogen bond acceptor vector features, four hydrophobic/aromatic features, and exclusion volumes. The pharmacophore was validated against a set of 59 compounds, including registered drugs. The model recognized 9 out of 12 inhibitors (75%), which could not be identified based on general parameters, such as molecular weight or SlogP, alone. Finally, the model was used to screen a virtual compound database. A number of compounds found via virtual screening were tested and displayed statistically significant BSEP inhibition, ranging from 13 ± 1% to 67 ± 7% of control (P < 0.05). In conclusion, we developed and validated a pharmacophore model that describes molecular features found in BSEP inhibitors. The model may be used as an in silico screening tool to identify potentially harmful drug candidates at an early stage in drug development.

摘要

药物性胆汁淤积是一种常见的药物副作用,通常由与胆盐输出泵(BSEP/ABCB11)的意外相互作用引起。BSEP是负责将胆汁酸从肝细胞转运到胆汁中的关键膜转运蛋白。在此,我们开发了一种药效团模型,该模型描述了与BSEP抑制活性相关的化合物的分子特征。为了生成输入和验证数据集,我们使用过表达人BSEP的膜囊泡进行体外实验,以评估化合物(50 μM)对BSEP介导的(3)H-牛磺胆酸转运的影响。该模型包含两个氢键受体/阴离子特征、两个氢键受体向量特征、四个疏水/芳香特征和排除体积。该药效团针对一组59种化合物(包括已注册药物)进行了验证。该模型识别出了12种抑制剂中的9种(75%),仅根据分子量或SlogP等一般参数无法识别这些抑制剂。最后,该模型用于筛选虚拟化合物数据库。通过虚拟筛选发现的许多化合物经过测试,显示出具有统计学意义的BSEP抑制作用,抑制率范围为对照的13±1%至67±7%(P<0.05)。总之,我们开发并验证了一种药效团模型,该模型描述了BSEP抑制剂中的分子特征。该模型可作为一种计算机筛选工具,在药物开发的早期阶段识别潜在有害的候选药物。

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