Skip to main content
SpringerLink
Log in

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

  • Full-Length Paper
  • Published:
Molecular Diversity Aims and scope Submit manuscript

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

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  8. Imai K, Takaoka A (2006) Comparing antibody and small-molecule therapies for cancer. Nat Rev Cancer 6:714–727. doi:10.1038/nrc1913

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  26. Schrödinger Suite 2009 Virtual screening workflow; Glide version 5.5; LigPrep 2.3; QikProp 3.2, Schrödinger, LLC, New York. http://www.schrodinger.com

  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

    Article  CAS  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  31. Golbraikh A, Tropsha A (2002) Beware of \(q2!\). J Mol Graph Mod 20:269–276. doi:10.1016/S1093-3263(01)00123-1

    Article  CAS  Google Scholar 

  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. 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

    Article  CAS  Google Scholar 

  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. http://www.netsci.org/Science/Cheminform/feature09.html. Accessed 12 Aug 2015

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  37. Chen X, Lin Y, Gilson MK (2001) The binding database: overview and user’s guide. Biopolymers 61:127–141

    Article  CAS  PubMed  Google Scholar 

  38. Compounds: release 4 file series: May 2012. http://cactus.nci.nih.gov/download/nci. Accessed 8 Aug 2015

  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

    Article  CAS  Google Scholar 

  40. The simple, user-friendly and reliable online application for the AD computation. http://dtclab.webs.com/software-tools or http://teqip.jdvu.ac.in/QSAR_Tools. Accessed 16 Aug 2015

  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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 213–236

    Google Scholar 

  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. 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

    Article  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. Department of Pharmaceutical Chemistry, R. C. Patel Institute of Pharmaceutical Education and Research, Dist-Dhule, Shirpur, Maharashtra, 425 405, India

    Prashant Chaudhari

  2. Department of Pharmaceutical Chemistry, H. R. Patel Institute of Pharmaceutical Education and Research, Dist-Dhule, Shirpur, Maharashtra, 425 405, India

    Sanjay Bari

Authors
  1. Prashant Chaudhari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanjay Bari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Prashant Chaudhari.

Ethics declarations

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’.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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 20, 41–53 (2016). https://doi.org/10.1007/s11030-015-9635-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11030-015-9635-x

Keywords

Search

Navigation