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In silico exploration of c-KIT inhibitors by pharmaco-informatics methodology: pharmacophore modeling, 3D QSAR, docking studies, and virtual screening.
Mol Divers. 2016 Feb; 20(1):41-53.MD

Abstract

c-KIT is a component of the platelet-derived growth factor receptor family, classified as type-III receptor tyrosine kinase. c-KIT has been reported to be involved in, small cell lung cancer, other malignant human cancers, and inflammatory and autoimmune diseases associated with mast cells. Available c-KIT inhibitors suffer from tribulations of growing resistance or cardiac toxicity. A combined in silico pharmacophore and structure-based virtual screening was performed to identify novel potential c-KIT inhibitors. In the present study, five molecules from the ZINC database were retrieved as new potential c-KIT inhibitors, using Schrödinger's Maestro 9.0 molecular modeling suite. An atom-featured 3D QSAR model was built using previously reported c-KIT inhibitors containing the indolin-2-one scaffold. The developed 3D QSAR model ADHRR.24 was found to be significant (R2 = 0.9378, Q2 = 0.7832) and instituted to be sufficiently robust with good predictive accuracy, as confirmed through external validation approaches, Y-randomization and GH approach [GH score 0.84 and Enrichment factor (E) 4.964]. The present QSAR model was further validated for the OECD principle 3, in that the applicability domain was calculated using a "standardization approach." Molecular docking of the QSAR dataset molecules and final ZINC hits were performed on the c-KIT receptor (PDB ID: 3G0E). Docking interactions were in agreement with the developed 3D QSAR model. Model ADHRR.24 was explored for ligand-based virtual screening followed by in silico ADME prediction studies. Five molecules from the ZINC database were obtained as potential c-KIT inhibitors with high in -silico predicted activity and strong key binding interactions with the c-KIT receptor.

Authors+Show Affiliations

Department of Pharmaceutical Chemistry, R. C. Patel Institute of Pharmaceutical Education and Research, Dist-Dhule, Shirpur, Maharashtra, 425 405, India. prashantniperk@gmail.com.Department of Pharmaceutical Chemistry, H. R. Patel Institute of Pharmaceutical Education and Research, Dist-Dhule, Shirpur, Maharashtra, 425 405, India.

Pub Type(s)

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

Language

eng

PubMed ID

26416560

Citation

Chaudhari, Prashant, and Sanjay Bari. "In Silico Exploration of c-KIT Inhibitors By Pharmaco-informatics Methodology: Pharmacophore Modeling, 3D QSAR, Docking Studies, and Virtual Screening." Molecular Diversity, vol. 20, no. 1, 2016, pp. 41-53.
Chaudhari P, Bari S. In silico exploration of c-KIT inhibitors by pharmaco-informatics methodology: pharmacophore modeling, 3D QSAR, docking studies, and virtual screening. Mol Divers. 2016;20(1):41-53.
Chaudhari, P., & Bari, S. (2016). In silico exploration of c-KIT inhibitors by pharmaco-informatics methodology: pharmacophore modeling, 3D QSAR, docking studies, and virtual screening. Molecular Diversity, 20(1), 41-53. https://doi.org/10.1007/s11030-015-9635-x
Chaudhari P, Bari S. In Silico Exploration of c-KIT Inhibitors By Pharmaco-informatics Methodology: Pharmacophore Modeling, 3D QSAR, Docking Studies, and Virtual Screening. Mol Divers. 2016;20(1):41-53. PubMed PMID: 26416560.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - In silico exploration of c-KIT inhibitors by pharmaco-informatics methodology: pharmacophore modeling, 3D QSAR, docking studies, and virtual screening. AU - Chaudhari,Prashant, AU - Bari,Sanjay, Y1 - 2015/09/28/ PY - 2015/03/12/received PY - 2015/09/14/accepted PY - 2015/9/30/entrez PY - 2015/9/30/pubmed PY - 2016/11/12/medline KW - 3D QSAR KW - Docking KW - Indolin-2-one KW - Pharmacophore KW - ZINC KW - c-KIT inhibitors SP - 41 EP - 53 JF - Molecular diversity JO - Mol Divers VL - 20 IS - 1 N2 - c-KIT is a component of the platelet-derived growth factor receptor family, classified as type-III receptor tyrosine kinase. c-KIT has been reported to be involved in, small cell lung cancer, other malignant human cancers, and inflammatory and autoimmune diseases associated with mast cells. Available c-KIT inhibitors suffer from tribulations of growing resistance or cardiac toxicity. A combined in silico pharmacophore and structure-based virtual screening was performed to identify novel potential c-KIT inhibitors. In the present study, five molecules from the ZINC database were retrieved as new potential c-KIT inhibitors, using Schrödinger's Maestro 9.0 molecular modeling suite. An atom-featured 3D QSAR model was built using previously reported c-KIT inhibitors containing the indolin-2-one scaffold. The developed 3D QSAR model ADHRR.24 was found to be significant (R2 = 0.9378, Q2 = 0.7832) and instituted to be sufficiently robust with good predictive accuracy, as confirmed through external validation approaches, Y-randomization and GH approach [GH score 0.84 and Enrichment factor (E) 4.964]. The present QSAR model was further validated for the OECD principle 3, in that the applicability domain was calculated using a "standardization approach." Molecular docking of the QSAR dataset molecules and final ZINC hits were performed on the c-KIT receptor (PDB ID: 3G0E). Docking interactions were in agreement with the developed 3D QSAR model. Model ADHRR.24 was explored for ligand-based virtual screening followed by in silico ADME prediction studies. Five molecules from the ZINC database were obtained as potential c-KIT inhibitors with high in -silico predicted activity and strong key binding interactions with the c-KIT receptor. SN - 1573-501X UR - https://www.unboundmedicine.com/medline/citation/26416560/In_silico_exploration_of_c_KIT_inhibitors_by_pharmaco_informatics_methodology:_pharmacophore_modeling_3D_QSAR_docking_studies_and_virtual_screening_ L2 - https://doi.org/10.1007/s11030-015-9635-x DB - PRIME DP - Unbound Medicine ER -