Archives of Pharmacal Research

, Volume 38, Issue 11, pp 2008–2019 | Cite as

Finding new scaffolds of JAK3 inhibitors in public database: 3D-QSAR models & shape-based screening

Research Article


The STAT/JAK3 pathway is a well-known therapeutic target in various diseases (ex. rheumatoid arthritis and psoriasis). The therapeutic advantage of JAK3 inhibition motivated to find new scaffolds with desired DMPK. For the purpose, in silico high-throughput sieves method is developed consisting of a receptor-guided three-dimensional quantitative structure–activity relationship study and shape-based virtual screening. We developed robust and predictive comparative molecular field analysis (q2 = 0.760, r2 = 0.915) and comparative molecular similarity index analysis (q2 = 0.817, r2 = 0.981) models and validated these using a test set, which produced satisfactory predictions of 0.925 and 0.838, respectively.

Graphical Abstract


Diversity of scaffolds 3D-QSAR Shape-based screening Anti-inflammatory JAK3 inhibitors 

Supplementary material

12272_2015_607_MOESM1_ESM.docx (2.8 mb)
Supplementary material 1 (DOCX 2871 kb)


  1. Ashburn, T.T., and K.B. Thor. 2004. Drug repositioning: identifying and developing new uses for existing drugs. Nature Reviews Drug Discovery 3: 673–683.CrossRefPubMedGoogle Scholar
  2. Bickerton, G.R., G.V. Paolini, J. Besnard, S. Muresan, and A.L. Hopkins. 2012. Quantifying the chemical beauty of drugs. Nature Chemistry. 4: 90–98.PubMedCentralCrossRefPubMedGoogle Scholar
  3. Feng, B.Y., A. Shelat, T.N. Doman, R.K. Guy, and B.K. Shoichet. 2005. High-throughput assays for promiscuous inhibitors. Nature Chemical Biology 1: 146–148.CrossRefPubMedGoogle Scholar
  4. Glide v6.2. 2014. Schrödinger. New York: LLC.Google Scholar
  5. Hughes, J.P., S. Rees, S.B. Kalindjian, and K.L. Philpott. 2011. Principles of early drug discovery. British Journal of Pharmacology 162: 1239.PubMedCentralCrossRefPubMedGoogle Scholar
  6. Hawkins, P.C., A.G. Skillman, G.L. Warren, B.A. Ellingson, and M.T. Stahl. 2010. Conformer generation with OMEGA: Algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural Database. Journal of Chemical Information and Modeling 50: 572–584.PubMedCentralCrossRefPubMedGoogle Scholar
  7. Hawkins, P.C., and A. Nicholls. 2012. Conformer generation with OMEGA: Learning from the data set and the analysis of failures. Journal of Chemical Information and Modeling 52: 2919–2936.CrossRefPubMedGoogle Scholar
  8. Huang, N., B.K. Shoichet, and J.J. Irwin. 2006. Benchmarking sets for molecular docking. Journal of Medicinal Chemistry 49: 6789–6801.PubMedCentralCrossRefPubMedGoogle Scholar
  9. Horvath, D., G. Marcou, and A. Varnek. 2013. Do not hesitate to use Tversky-and other hints for successful active analogue searches with feature count descriptors. Journal of Chemical Information and Modeling 53: 1543–1562.CrossRefPubMedGoogle Scholar
  10. Ivashkiv, L.B., and X. Hu. 2003. The JAK/STAT pathway in rheumatoid arthritis: pathogenic or protective? Arthritis and Rheumatism 48: 2092–2096.CrossRefPubMedGoogle Scholar
  11. Jaime-Figueroa, S., J. De Vicente, J. Hermann, A. Jahangir, S. Jin, A. Kuglstatter, et al. 2013. Discovery of a series of novel 5H-pyrrolo [2, 3-b] pyrazine-2-phenyl ethers, as potent JAK3 kinase inhibitors. Bioorganic & Medicinal Chemistry Letters 23: 2522–2526.CrossRefGoogle Scholar
  12. Koncz, T., M. Pentek, V. Brodszky, K. Ersek, E. Orlewska, and L. Gulacsi. 2010. Adherence to biologic DMARD therapies in rheumatoid arthritis. Expert Opinion on Biological Therapy 10: 1367–1378.CrossRefPubMedGoogle Scholar
  13. Krzywinski, M., and N. Altman. 2014. Points of significance: Visualizing samples with box plots. Nature Methods 11: 119–120.CrossRefPubMedGoogle Scholar
  14. Kubinyi, H., G. Folkers, and Y.C. Martin. 1998. 3D QSAR in drug design. In Three-dimensional quantitative structure activity relationships, ed. H.D. Hoeltje, and W. Sippl. Dordrecht: Kluwer Academic Publishers.Google Scholar
  15. Lynch, S.M., J. DeVicente, J.C. Hermann, S. Jaime-Figueroa, S. Jin, A. Kuglstatter, et al. 2013. Strategic use of conformational bias and structure based design to identify potent JAK3 inhibitors with improved selectivity against the JAK family and the kinome. Bioorganic and Medicinal Chemistry Letters 23: 2793–2800.CrossRefPubMedGoogle Scholar
  16. Macarron, R., M.N. Banks, D. Bojanic, D.J. Burns, D.A. Cirovic, T. Garyantes, et al. 2011. Impact of high-throughput screening in biomedical research. Nature Reviews Drug Discovery 10: 188–195.CrossRefPubMedGoogle Scholar
  17. MacroModel. 2014. v., Schrödinger. New York: LLC.Google Scholar
  18. Maestro v9.6. 2014. Schrödinger. New York: LLC.Google Scholar
  19. Nicholls, A. 2014. Confidence limits, error bars and method comparison in molecular modeling. Part 1: The calculation of confidence intervals. Journal of Computer-Aided Molecular Design 28(9): 887–918.PubMedCentralCrossRefPubMedGoogle Scholar
  20. OMEGA. 2014. v. OpenEye Scientific Software. Santa Fe: OMEGA.Google Scholar
  21. ROCS. v. OpenEye Scientific Software. Santa Fe: ROCS.Google Scholar
  22. Roy, A., P.R. McDonald, S. Sittampalam, and R. Chaguturu. 2010. Open access high throughput drug discovery in the public domain: A Mount Everest in the making. Current Pharmaceutical Biotechnology 11: 764.PubMedCentralCrossRefPubMedGoogle Scholar
  23. Russell, S.M., N. Tayebi, H. Nakajima, M.C. Riedy, J.L. Roberts, M.J. Aman, et al. 1995. Mutation of Jak3 in a patient with SCID: Essential role of JAK3 in lymphoid development. Science 270: 797–800.CrossRefPubMedGoogle Scholar
  24. Shuai, K., and B. Liu. 2003. Regulation of JAK–STAT signalling in the immune system. Nature Reviews Immunology 3: 900–911.CrossRefPubMedGoogle Scholar
  25. SYBYL-X2.1. Tripos International. St. Louis: SYBYL-X2.1.Google Scholar
  26. Tropsha, A. 2010. Best practices for QSAR model development, validation, and exploitation. Molecular Informatics 29: 476–488.CrossRefGoogle Scholar

Copyright information

© The Pharmaceutical Society of Korea 2015

Authors and Affiliations

  • Changdev G. Gadhe
    • 1
    • 2
  • Eunhee Lee
    • 1
    • 2
  • Mi-hyun Kim
    • 1
    • 2
  1. 1.Department of Pharmacy, College of PharmacyGachon UniversityIncheonRepublic of Korea
  2. 2.Gachon Institute of Pharmaceutical ScienceGachon UniversityIncheonRepublic of Korea

Personalised recommendations