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Screening Ingredients from Herbs against Pregnane X Receptor in the Study of Inductive Herb-Drug Interactions: Combining Pharmacophore and Docking-Based Rank Aggregation.

Cui Z, Kang H, Tang K, Liu Q, Cao Z, Zhu R - Biomed Res Int (2015)

Bottom Line: Secondly, DRA was used to rerank the result of pharmacophore filtering.The accuracy of our method is higher than other traditional methods.The strategy could be extended to studies on other inductive herb-drug interactions.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics, Tongji University, Shanghai 200092, China.

ABSTRACT
The issue of herb-drug interactions has been widely reported. Herbal ingredients can activate nuclear receptors and further induce the gene expression alteration of drug-metabolizing enzyme and/or transporter. Therefore, the herb-drug interaction will happen when the herbs and drugs are coadministered. This kind of interaction is called inductive herb-drug interactions. Pregnane X Receptor (PXR) and drug-metabolizing target genes are involved in most of inductive herb-drug interactions. To predict this kind of herb-drug interaction, the protocol could be simplified to only screen agonists of PXR from herbs because the relations of drugs with their metabolizing enzymes are well studied. Here, a combinational in silico strategy of pharmacophore modelling and docking-based rank aggregation (DRA) was employed to identify PXR's agonists. Firstly, 305 ingredients were screened out from 820 ingredients as candidate agonists of PXR with our pharmacophore model. Secondly, DRA was used to rerank the result of pharmacophore filtering. To validate our prediction, a curated herb-drug interaction database was built, which recorded 380 herb-drug interactions. Finally, among the top 10 herb ingredients from the ranking list, 6 ingredients were reported to involve in herb-drug interactions. The accuracy of our method is higher than other traditional methods. The strategy could be extended to studies on other inductive herb-drug interactions.

No MeSH data available.


Related in: MedlinePlus

The pharmacophore of PXR (F1: Hyd/Acc; F2: Acc/Acc2/Don2; F3: Hyd/Acc2; F4: Hyd/Acc; F5: ARO/Hyd; V1–V8: excluded volume).
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fig2: The pharmacophore of PXR (F1: Hyd/Acc; F2: Acc/Acc2/Don2; F3: Hyd/Acc2; F4: Hyd/Acc; F5: ARO/Hyd; V1–V8: excluded volume).

Mentions: As shown in Figure 3, three different conformations of SRL12813 were, respectively, extracted from complex crystal structures of PXR (PDB id: 1NRL [29], 1ILH [26], and 3HVL [30]). The red conformation of SRL12813 was extracted from complex 3HVL; the yellow one was extracted from complex 1ILH; the blue one was extracted from complex 1NRL. They were provided as template molecules. Pharmacophore was generated by collecting a common set of template molecules structural features. These structural features are related to the ligand's biological activity and recognition at binding site of receptor. In our model, five pharmacophoric structural features (shown in Figure 2) were fit by all template molecules. The process of pharmacophore modelling was performed in Molecular Operation Environment (MOE) 2008.10.


Screening Ingredients from Herbs against Pregnane X Receptor in the Study of Inductive Herb-Drug Interactions: Combining Pharmacophore and Docking-Based Rank Aggregation.

Cui Z, Kang H, Tang K, Liu Q, Cao Z, Zhu R - Biomed Res Int (2015)

The pharmacophore of PXR (F1: Hyd/Acc; F2: Acc/Acc2/Don2; F3: Hyd/Acc2; F4: Hyd/Acc; F5: ARO/Hyd; V1–V8: excluded volume).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4538340&req=5

fig2: The pharmacophore of PXR (F1: Hyd/Acc; F2: Acc/Acc2/Don2; F3: Hyd/Acc2; F4: Hyd/Acc; F5: ARO/Hyd; V1–V8: excluded volume).
Mentions: As shown in Figure 3, three different conformations of SRL12813 were, respectively, extracted from complex crystal structures of PXR (PDB id: 1NRL [29], 1ILH [26], and 3HVL [30]). The red conformation of SRL12813 was extracted from complex 3HVL; the yellow one was extracted from complex 1ILH; the blue one was extracted from complex 1NRL. They were provided as template molecules. Pharmacophore was generated by collecting a common set of template molecules structural features. These structural features are related to the ligand's biological activity and recognition at binding site of receptor. In our model, five pharmacophoric structural features (shown in Figure 2) were fit by all template molecules. The process of pharmacophore modelling was performed in Molecular Operation Environment (MOE) 2008.10.

Bottom Line: Secondly, DRA was used to rerank the result of pharmacophore filtering.The accuracy of our method is higher than other traditional methods.The strategy could be extended to studies on other inductive herb-drug interactions.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics, Tongji University, Shanghai 200092, China.

ABSTRACT
The issue of herb-drug interactions has been widely reported. Herbal ingredients can activate nuclear receptors and further induce the gene expression alteration of drug-metabolizing enzyme and/or transporter. Therefore, the herb-drug interaction will happen when the herbs and drugs are coadministered. This kind of interaction is called inductive herb-drug interactions. Pregnane X Receptor (PXR) and drug-metabolizing target genes are involved in most of inductive herb-drug interactions. To predict this kind of herb-drug interaction, the protocol could be simplified to only screen agonists of PXR from herbs because the relations of drugs with their metabolizing enzymes are well studied. Here, a combinational in silico strategy of pharmacophore modelling and docking-based rank aggregation (DRA) was employed to identify PXR's agonists. Firstly, 305 ingredients were screened out from 820 ingredients as candidate agonists of PXR with our pharmacophore model. Secondly, DRA was used to rerank the result of pharmacophore filtering. To validate our prediction, a curated herb-drug interaction database was built, which recorded 380 herb-drug interactions. Finally, among the top 10 herb ingredients from the ranking list, 6 ingredients were reported to involve in herb-drug interactions. The accuracy of our method is higher than other traditional methods. The strategy could be extended to studies on other inductive herb-drug interactions.

No MeSH data available.


Related in: MedlinePlus