Molecular Diversity

, Volume 20, Issue 1, pp 41–53 | Cite as

In silico exploration of c-KIT inhibitors by pharmaco-informatics methodology: pharmacophore modeling, 3D QSAR, docking studies, and virtual screening

  • Prashant Chaudhari
  • Sanjay Bari
Full-Length Paper


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 (\(R^{2}=0.9378, Q^{2}=0.7832\)) 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.

Graphical abstract


c-KIT inhibitors Indolin-2-one  Pharmacophore  3D QSAR ZINC Docking 



Authors are thankful to Prof. Sanjay J. Surana, Principal, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, India, for availing the facility to carry out the computational work and his valuable support. Authors are also grateful to the North Maharashtra University, Jalgaon, Maharashtra, for providing financial assistance under the scheme “Vice chancellor Research Motivation Scheme (VCRMS),” Sanction No. NMU/11A/VCRMS/Budget-2014-15/Pharmacy-15/170/2015.

Compliance with ethical standards

Conflicts of Interest

Corresponding author Prashant J. Chaudhari presented a part of this manuscript at ‘State Level AVISHKAR-2014: The inter-university research convention, Maharashtra’.

Supplementary material

11030_2015_9635_MOESM1_ESM.docx (188 kb)
Supplementary material 1 (docx 189 KB)
11030_2015_9635_MOESM2_ESM.xlsx (4.5 mb)
Supplementary material 2 (xlsx 4597 KB)
11030_2015_9635_MOESM3_ESM.xlsx (10 kb)
Supplementary material 3 (xlsx 10 KB)
11030_2015_9635_MOESM4_ESM.xlsx (14 kb)
Supplementary material 4 (xlsx 15 KB)


  1. 1.
    Zsebo KM, Williams DA, Geissler EN, Broudy VC, Martin FH, Atkins HL, Hsu RY, Birkett NC, Okino KH, Murdock DC, Jacobsen FW, Langley KE, Smith KA, Takeish T, Cattanach BM, Galli SJ (1990) Stem cell factor is encoded at the SI locus of the mouse and is the ligand for the c-kit tyrosine kinase receptor. Cell 63:213–224. doi: 10.1016/0092-8674(90)90302-U CrossRefPubMedGoogle Scholar
  2. 2.
    Isakov N, Biesinger B (2000) Lck protein tyrosine kinase is a key regulator of T-cell activation and a target for signal intervention by Herpesvirus saimiri and other viral gene products. Eur J Biochem 267:3413–3421. doi: 10.1046/j.1432-1327.2000.01412.x CrossRefPubMedGoogle Scholar
  3. 3.
    Drube S, Schmitz F, Gopfert C, Weber F, Kamradt T (2012) C-Kit controls IL-\(1\upbeta \)-induced effector functions in HMC-cells. Eur J Pharmacol 675:57–62. doi: 10.1016/j.ejphar.2011.11.035 CrossRefPubMedGoogle Scholar
  4. 4.
    Robert R Jr (2005) Signaling by Kit protein-tyrosine kinase: the stem cell factor receptor. Biochem Biophys Res Commun 337:1–13. doi: 10.1016/j.bbrc.2005.08.055 CrossRefGoogle Scholar
  5. 5.
    Kansal N, Silakari O, Ravikumar M (2010) Three dimensional pharmacophore modelling for c-Kit receptor tyrosine kinase inhibitors. Eur J Med Chem 45:393–404. doi: 10.1016/j.ejmech.2009.09.013 CrossRefPubMedGoogle Scholar
  6. 6.
    Wang WL, Healy ME, Sattler M, Verma S, Lin J, Maulik G, Stiles CD, James DG, Johnson BE, Salgia R (2000) Growth inhibition and modulation of kinase pathways of small cell lung cancer cell lines by the novel tyrosine kinase inhibitor STI 571. Oncogene 19:3521–3528. doi: 10.1038/sj.onc.1203698 CrossRefPubMedGoogle Scholar
  7. 7.
    Heinrich MC, Blanke CD, Druker BJ, Corless CL (2002) Inhibition of KIT tyrosine kinase activity: a novel molecular approach to the treatment of KIT-positive malignancies. J Clin Oncol 20:1692–1703. doi: 10.1200/JCO.20.6.1692 CrossRefPubMedGoogle Scholar
  8. 8.
    Imai K, Takaoka A (2006) Comparing antibody and small-molecule therapies for cancer. Nat Rev Cancer 6:714–727. doi: 10.1038/nrc1913 CrossRefPubMedGoogle Scholar
  9. 9.
    Eklund KK (2007) Mast cells in the pathogenesis of rheumatic diseases and as potential targets for anti-rheumatic therapy. Immunol Rev 217:38–52. doi: 10.1111/j.1600-065X.2007.00504.x CrossRefPubMedGoogle Scholar
  10. 10.
    Jensen BM, Metcalfe DD, Gilfillan AM (2007) Targeting kit activation: a potential therapeutic approach in the treatment of allergic inflammation. Inflamm Allergy Drug Targets 6:57–62. doi: 10.2174/187152807780077255 CrossRefPubMedGoogle Scholar
  11. 11.
    Chen N, Burli RW, Neira S, Hungate R, Zhang D, Yu V, Nguyen Y, Tudor Y, Plant M, Flynn S, Meagher KL, Lee MR, Zhang X, Itano A, Schrag M, Xu Y, Gordon YN, Hu E (2008) Discovery of a potent and selective c-Kit inhibitor for the treatment of inflammatory diseases. Bioorg Med Chem Lett 18:4137–4141. doi: 10.1016/j.bmcl.2008.05.089 CrossRefPubMedGoogle Scholar
  12. 12.
    Gajiwala KS, Wu JC, Christensen J, Deshmukh GD, Diehl W, DiNitto JP, English JM, Greig YH, Jacques SL, Lunney EA, McTigue M, Molina D, Quenzer T, Wells PA, Yu X, Zhang Y, Zou A, Emmett MR, Marshall AG, Zhang HM, Demetri GD (2009) KIT kinase mutants show unique mechanisms of drug resistance to imatinib and sunitinib in gastrointestinal stromal tumor patients. Proc Natl Acad Sci USA 106:1542–1547. doi: 10.1073/pnas.0812413106 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Force T, Krause DS, Van Etten RA (2007) Molecular mechanisms of cardiotoxicity of tyrosine kinase inhibition. Nat Rev Cancer 7:332–344. doi: 10.1038/nrc2106 CrossRefPubMedGoogle Scholar
  14. 14.
    Zhang L, Zheng Q, Yang Y, Zhou H, Gong X, Zhao S, Fan C (2014) Synthesis and in vivo SAR study of indolin-2-one-based multi-targeted inhibitors as potential anticancer agents. Eur J Med Chem 82:139–151. doi: 10.1016/j.ejmech.2014.05.051 CrossRefPubMedGoogle Scholar
  15. 15.
    Cho TP, Dong SY, Jun F, Hong FJ, Liang YJ, Lu X, Hua PJ, Li LY, Lei Z, Bing H, Ying Z, Qiong LF, Bei FB, Guang LL, Shen GA, Hong SG, Hong SW, Tai MX (2010) Novel potent orally active multitargeted receptor tyrosine kinase inhibitors: synthesis, structure-activity relationships, and antitumor activities of 2-indolinone derivatives. J Med Chem 53:8140–8149. doi: 10.1021/jm101036c CrossRefPubMedGoogle Scholar
  16. 16.
    Almerico AM, Tutone M, Lauria A (2012) Receptor-guided 3D-QSAR approach for the discovery of c-kit tyrosine kinase inhibitors. J Mol Model 18:2885–2895. doi: 10.1007/s00894-011-1304-0 CrossRefPubMedGoogle Scholar
  17. 17.
    Pan Y, Wang Y, Bryant SH (2013) Pharmacophore and 3D-QSAR characterization of 6-arylquinazolin- 4-amines as Cdc2-like kinase 4 (Clk4) and dual specificity tyrosine-phosphorylation-regulated-kinase 1A (Dyrk1A) inhibitors. J Chem Inf Model 53:938–947. doi: 10.1021/ci300625c CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Cruciani G, Watson KA (1994) Comparative molecular field analysis using GRID force-field and GOLPE variable selection methods in a study of inhibitors of glycogen phosphorylase b. J Med Chem 37:2589–2601. doi: 10.1021/jm00042a012 CrossRefPubMedGoogle Scholar
  19. 19.
    Ballante F, Ragno R (2012) 3-D QSAutogrid/R: an alternative procedure to build 3-D QSAR models. Methodologies and applications. J Chem Inf Model 52:1674–1685. doi: 10.1021/ci300123x CrossRefPubMedGoogle Scholar
  20. 20.
    Schuster D, Langer T (2005) The identification of ligand features essential for PXR activation by pharmacophore modeling. J Chem Inf Model 45:431–439. doi: 10.1021/ci049722q CrossRefPubMedGoogle Scholar
  21. 21.
    Ding L, Tang F, Huang W, Jin Q, Shen H, Wei P (2013) Design, synthesis, and biological evaluation of novel 3-pyrrolo[b]cyclohexylene-2-dihydroindolinone derivatives as potent receptor tyrosine kinase inhibitors. Bioorg Med Chem Lett 23:5630–5633. doi: 10.1016/j.bmcl.2013.08.037 CrossRefPubMedGoogle Scholar
  22. 22.
    Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA (2006) PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol Des 20:647–671. doi: 10.1007/s10822-006-9087-6 CrossRefPubMedGoogle Scholar
  23. 23.
    Dixon SL, Smondyrev AM, Rao SN (2006) PHASE: a novel approach to pharmacophore modeling and 3D database searching. Chem Biol Drug Des 67:370–372. doi: 10.1111/j.1747-0285.2006.00384.x CrossRefPubMedGoogle Scholar
  24. 24.
    Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749. doi: 10.1021/jm0306430 CrossRefPubMedGoogle Scholar
  25. 25.
    Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) Glide: a new approach for rapid, accurate docking and scoring. Enrichment factors in database screening. J Med Chem 47:1750–1759. doi: 10.1021/jm030644s CrossRefPubMedGoogle Scholar
  26. 26.
    Schrödinger Suite 2009 Virtual screening workflow; Glide version 5.5; LigPrep 2.3; QikProp 3.2, Schrödinger, LLC, New York.
  27. 27.
    Roy K, Kar S, Ambure P (2015) On a simple approach for determining applicability domain of QSAR models. Chemom Intell Lab Syst 145:22–29. doi: 10.1016/j.chemolab.2015.04.013 CrossRefGoogle Scholar
  28. 28.
    Golbraikh A, Shen M, Xiao Z, Xiao Y, Lee K, Tropsha A (2003) Rational selection of training and test sets for the development of validated QSAR models. J Comput Aided Mol Des 17:241–253. doi: 10.1023/A:3A1025386326946 CrossRefPubMedGoogle Scholar
  29. 29.
    Teli MK, Rajanikant GK (2012) Pharmacophore generation and atom based 3D-QSAR of N-iso-propyl pyrrole-based derivatives as HMG-CoA reductase inhibitors. Org Med Chem Lett 2:1–10. doi: 10.1186/2191-2858-2-25 CrossRefGoogle Scholar
  30. 30.
    Shah UA, Deokar HS, Kadam SS, Kulkarni VM (2010) Pharmacophore generation and atom-based 3D-QSAR of novel 2-(4-methylsulfonylphenyl)pyrimidines as COX-2 inhibitors. Mol Divers 14:559–568. doi: 10.1007/s11030-009-9183-3 CrossRefPubMedGoogle Scholar
  31. 31.
    Golbraikh A, Tropsha A (2002) Beware of \(q2!\). J Mol Graph Mod 20:269–276. doi: 10.1016/S1093-3263(01)00123-1 CrossRefGoogle Scholar
  32. 32.
    Zhang S, Golbraikh A, Oloff S, Kohn H, Tropsha A (2006) A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models. J Chem Inf Model 46:1984–1995. doi: 10.1021/ci060132x
  33. 33.
    Melagraki G, Afantitis A (2013) Enalos KNIME nodes: exploring corrosion inhibition of steel in acidic medium. Chemom Intell Lab Syst 123:9–14. doi: 10.1016/j.chemolab.2013.02.003 CrossRefGoogle Scholar
  34. 34.
    Guner O, Henry D (1998) Formula for determining the “goodness of hit lists” in 3D database searches. Accelrys/MDL Information Systems, Inc., San Diego/San Leandro. Accessed 12 Aug 2015
  35. 35.
    Chen X, Liu M, Gilson MK (2002) BindingDB: a web-accessible molecular recognition database. Comb Chem High Throughput Screen 4:719–725. doi: 10.2174/1386207013330670 CrossRefGoogle Scholar
  36. 36.
    Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK (2007) BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 35(Database issue):198–201. doi: 10.1093/nar/gkl999 CrossRefGoogle Scholar
  37. 37.
    Chen X, Lin Y, Gilson MK (2001) The binding database: overview and user’s guide. Biopolymers 61:127–141CrossRefPubMedGoogle Scholar
  38. 38.
    Compounds: release 4 file series: May 2012. Accessed 8 Aug 2015
  39. 39.
    Melagraki G, Afantitis A (2014) Enalos InSilicoNano platform: an online decision support tool for the design and virtual screening of nanoparticles. RSC Adv 4:50713–50725. doi: 10.1039/C4RA07756C CrossRefGoogle Scholar
  40. 40.
    The simple, user-friendly and reliable online application for the AD computation. or Accessed 16 Aug 2015
  41. 41.
    Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52:1757–1768. doi: 10.1021/ci3001277 CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46:3–26. doi: 10.1016/S0169-409X(00)00129-0 CrossRefPubMedGoogle Scholar
  43. 43.
    Guner OF, Waldman M, Hoffmann RD, Kim JH (2000) Pharmacophore perception, development, and use in drug design, IUL biotechnology series. In: Guner OF (ed) Strategies for database mining and pharmacophore development, 1st edn. International University Line, La Jolla, pp 213–236Google Scholar
  44. 44.
    Park H, Lee S, Lee S, Hong S (2014) Structure-based de novo design and identification of D816V mutant-selective c-KIT inhibitors. Org Biomol Chem 26:4644–4655. doi: 10.1039/c4ob00053f
  45. 45.
    Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Zhou Z, Han L, Karapetyan K, Dracheva S, Shoemaker BA, Bolton E, Gindulyte A, Bryant SH (2012) PubChem’s BioAssay database. Nucleic Acids Res 40:400–412. doi: 10.1093/nar/gkr1132 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Pharmaceutical ChemistryR. C. Patel Institute of Pharmaceutical Education and ResearchShirpurIndia
  2. 2.Department of Pharmaceutical ChemistryH. R. Patel Institute of Pharmaceutical Education and ResearchShirpurIndia

Personalised recommendations