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Exploring QSAR and pharmacophore mapping of structurally diverse selective matrix metalloproteinase-2 inhibitors.
J Pharm Pharmacol. 2013 Oct; 65(10):1541-54.JP

Abstract

OBJECTIVES AND METHODS

Matrix metalloproteinase-2 (MMP-2) is a potential target in metastases. Regression (conventional 2D QSAR) and classification (recursive partitioning (RP), Bayesian modelling) QSAR, pharmacophore mapping and 3D QSAR (comparative molecular field analysis and comparative molecular similarity analysis) were performed on 202 MMP-2 inhibitors.

KEY FINDINGS

Quality of the regression models was justified by internal (Q(2)) and external (R(2) Pred) cross-validation parameters. Stepwise regression was used to develop linear model (Q(2) = 0.822, R(2) Pred = 0.667). Genetic algorithm developed linear (Q(2) = 0.845, R(2) Pred = 0.638) and spline model (Q(2) = 0.882, R(2) Pred = 0.644). The RP and Bayesian models showed cross-validated area under receiver operating characteristic curve (AUCROC _ CV) of 0.805 and 0.979 respectively. QSAR models depicted importance of descriptors like five-membered rings, fractional positively charged surface area, lipophilocity and so on. Higher molecular volume was found to be detrimental. Pharmacophore mapping was performed with two tools - Hypogen and PHASE. Both models indicated that one hydrophobic and three hydrogen bond acceptor features are essential. The Pharmacophore-aligned structures were used for CoMFA (Q(2) of 0.586 and R(2) Pred of 0.689) and CoMSIA (Q(2) of 0.673 and R(2) Pred of 0.758), results of which complied with the other analyses.

CONCLUSIONS

All modelling techniques were compared to each other. The current study may help in designing novel MMP-2 inhibitors.

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Authors+Show Affiliations

Halder AK
Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India.
Saha A
No affiliation info available
Jha T
No affiliation info available

MeSH

Bayes TheoremChemical PhenomenaDrug DesignHydrophobic and Hydrophilic InteractionsMatrix Metalloproteinase 2Matrix Metalloproteinase InhibitorsModels, ChemicalQuantitative Structure-Activity RelationshipRegression AnalysisSmall Molecule Libraries

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

24028622

Citation

Halder, Amit Kumar, et al. "Exploring QSAR and Pharmacophore Mapping of Structurally Diverse Selective Matrix Metalloproteinase-2 Inhibitors." The Journal of Pharmacy and Pharmacology, vol. 65, no. 10, 2013, pp. 1541-54.
Halder AK, Saha A, Jha T. Exploring QSAR and pharmacophore mapping of structurally diverse selective matrix metalloproteinase-2 inhibitors. J Pharm Pharmacol. 2013;65(10):1541-54.
Halder, A. K., Saha, A., & Jha, T. (2013). Exploring QSAR and pharmacophore mapping of structurally diverse selective matrix metalloproteinase-2 inhibitors. The Journal of Pharmacy and Pharmacology, 65(10), 1541-54. https://doi.org/10.1111/jphp.12133
Halder AK, Saha A, Jha T. Exploring QSAR and Pharmacophore Mapping of Structurally Diverse Selective Matrix Metalloproteinase-2 Inhibitors. J Pharm Pharmacol. 2013;65(10):1541-54. PubMed PMID: 24028622.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Exploring QSAR and pharmacophore mapping of structurally diverse selective matrix metalloproteinase-2 inhibitors. AU - Halder,Amit Kumar, AU - Saha,Achintya, AU - Jha,Tarun, Y1 - 2013/08/25/ PY - 2013/02/26/received PY - 2013/07/23/accepted PY - 2013/9/14/entrez PY - 2013/9/14/pubmed PY - 2014/3/25/medline KW - 2D QSAR KW - 3D QSAR KW - matrix-metalloproteinase-2 KW - pharmacophore mapping KW - recursive partitioning SP - 1541 EP - 54 JF - The Journal of pharmacy and pharmacology JO - J Pharm Pharmacol VL - 65 IS - 10 N2 - OBJECTIVES AND METHODS: Matrix metalloproteinase-2 (MMP-2) is a potential target in metastases. Regression (conventional 2D QSAR) and classification (recursive partitioning (RP), Bayesian modelling) QSAR, pharmacophore mapping and 3D QSAR (comparative molecular field analysis and comparative molecular similarity analysis) were performed on 202 MMP-2 inhibitors. KEY FINDINGS: Quality of the regression models was justified by internal (Q(2)) and external (R(2) Pred) cross-validation parameters. Stepwise regression was used to develop linear model (Q(2) = 0.822, R(2) Pred = 0.667). Genetic algorithm developed linear (Q(2) = 0.845, R(2) Pred = 0.638) and spline model (Q(2) = 0.882, R(2) Pred = 0.644). The RP and Bayesian models showed cross-validated area under receiver operating characteristic curve (AUCROC _ CV) of 0.805 and 0.979 respectively. QSAR models depicted importance of descriptors like five-membered rings, fractional positively charged surface area, lipophilocity and so on. Higher molecular volume was found to be detrimental. Pharmacophore mapping was performed with two tools - Hypogen and PHASE. Both models indicated that one hydrophobic and three hydrogen bond acceptor features are essential. The Pharmacophore-aligned structures were used for CoMFA (Q(2) of 0.586 and R(2) Pred of 0.689) and CoMSIA (Q(2) of 0.673 and R(2) Pred of 0.758), results of which complied with the other analyses. CONCLUSIONS: All modelling techniques were compared to each other. The current study may help in designing novel MMP-2 inhibitors. SN - 2042-7158 UR - https://www.unboundmedicine.com/medline/citation/24028622/Exploring_QSAR_and_pharmacophore_mapping_of_structurally_diverse_selective_matrix_metalloproteinase_2_inhibitors_ L2 - https://doi.org/10.1111/jphp.12133 DB - PRIME DP - Unbound Medicine ER -
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