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In Silico Pharmacology

, 5:9 | Cite as

Exploration of 3,6-dihydroimidazo(4,5-d)pyrrolo(2,3-b)pyridin-2(1H)-one derivatives as JAK inhibitors using various in silico techniques

  • Radhakrishnan S. Jisha
  • Lilly Aswathy
  • Vijay H. Masand
  • Jayant M. Gajbhiye
  • Indira G. Shibi
Original Research

Abstract

This study focuses on understanding the structural features of 3,6-dihydroimidazo(4,5-d)pyrrolo(2,3-b)pyridin-2(1H)-one (dpp) derivatives to computationally identify new JAK inhibiting compounds. For the purpose, a novel virtual screening strategy, with 2D and 3D-QSAR (CoMFA and CoMSIA), data mining, pharmacophore modeling, ADMET prediction, multi-targeted protein-based docking and inverse QSAR, was employed. The 2D-QSAR equations developed for the JAK3, JAK2 and JAK1 involved five physicochemical descriptors. These descriptors correlate with the anti-RA activity with R2 values for JAK3, JAK2 and JAK1 are 0.9811, 0.8620 and 0.9740, respectively. The 3D-QSAR studies such as CoMFA and CoMSIA carried out through PLS analysis of the training set of JAK3, JAK2 and JAK1, gave Q2 values as 0.369, 0.476 and 0.490; \( {\text{R}}_{\text{nev}}^{2} \) values as 0.863, 0.684 and 0.724 and, F values as 23.098, 28.139 and 31.438, respectively. The contour maps produced by the CoMFA and CoMSIA models were used to understand the importance of hydrogen bond donor, acceptor, hydrophobic, steric and electrostatic interactions. The molecular docking studies of these selected compounds with various JAK proteins were carried out and the protein–ligand interactions were also studied. The study concluded that dpp15(s) is a highly potent JAK inhibitor with a very good predicted IC50 value.

Keywords

Rheumatoid arthritis Janus kinase 2D and 3D QSAR Weka Molecular docking 

Notes

Acknowledgements

Jisha, R.S. is thankful to the University of Kerala, Thiruvananthapuram for providing financial assistance in the form of University Junior Research Fellowship for this work. Aswathy L. is thankful to CSIR, New Delhi for the financial assistance in the form of Senior Research Fellowship.

Supplementary material

40203_2017_29_MOESM1_ESM.docx (275 kb)
Supplementary material 1 (DOCX 274 kb)
40203_2017_29_MOESM2_ESM.docx (31 kb)
Supplementary material 2 (DOCX 30 kb)

References

  1. Afantitis A, Melagraki G, Sarimveis H et al (2009) A novel QSAR model for predicting the inhibition of CXCR3 receptor by 4-N-aryl-[1,4] diazepane ureas. Eur J Med Chem 44:877–884. doi: 10.1016/j.ejmech.2008.05.028 CrossRefPubMedGoogle Scholar
  2. Aswathy L, Jisha RS, Masand VH et al (2016) Computational strategies to explore antimalarial thiazine alkaloid lead compounds based on an Australian marine sponge Plakortis lita. J Biomol Struct Dyn. doi: 10.1080/07391102.2016.1220870 PubMedGoogle Scholar
  3. Benigni R, Bossa C (2008) Structure alerts for carcinogenicity, and the Salmonella assay system: a novel insight through the chemical relational databases technology. Mutat Res 659:248–261. doi: 10.1016/j.mrrev.2008.05.003 CrossRefPubMedGoogle Scholar
  4. Boers M, Verhoeven AC, Markusse HM et al (1997) Randomised comparison of combined step-down prednisolone, methotrexate and sulphasalazine with sulphasalazine alone in early rheumatoid arthritis. Lancet 350:309–318. doi: 10.1016/S0140-6736(97)01300-7 CrossRefPubMedGoogle Scholar
  5. Böhm M, St Rzebecher J, Klebe G (1999) Three-dimensional quantitative structure-activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa. J Med Chem 42:458–477. doi: 10.1021/jm981062r CrossRefPubMedGoogle Scholar
  6. Bringmann G, Rummey C (2003) 3D QSAR investigations on antimalarial naphthylisoquinoline alkaloids by comparative molecular similarity indices analysis (CoMSIA), based on different alignment approaches. J Chem Inf Comput Sci 43:304–316. doi: 10.1021/ci025570s CrossRefPubMedGoogle Scholar
  7. Bush BL, Nachbar RB (1993) Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA. J Comput Aided Mol Des 7:587–619. doi: 10.1007/BF00124364 CrossRefPubMedGoogle Scholar
  8. Clark M, Cramer RD, Van Opdenbosch N (1989) Validation of the general purpose tripos 5.2 force field. J Comput Chem 10:982–1012. doi: 10.1002/jcc.540100804 CrossRefGoogle Scholar
  9. Cramer RD, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967. doi: 10.1021/ja00226a005 CrossRefPubMedGoogle Scholar
  10. Cruciani G, Crivori P, Carrupt P-A, Testa B (2000) Molecular fields in quantitative structure–permeation relationships: the VolSurf approach. J Mol Struct THEOCHEM 503:17–30. doi: 10.1016/S0166-1280(99)00360-7 CrossRefGoogle Scholar
  11. Frank E, Hall M, Trigg L et al (2004) Data mining in bioinformatics using Weka. Bioinformatics 20:2479–2481. doi: 10.1093/bioinformatics/bth261 CrossRefPubMedGoogle Scholar
  12. Fridman JS, Scherle PA, Collins R et al (2010) Selective inhibition of JAK1 and JAK2 is efficacious in rodent models of arthritis: preclinical characterization of INCB028050. J Immunol 184:5298–5307. doi: 10.4049/jimmunol.0902819 CrossRefPubMedGoogle Scholar
  13. Fujii M, Adachi S, Shimizu T et al (1993) Interstitial lung disease in rheumatoid arthritis: assessment with high-resolution computed tomography. J Thorac Imaging 8:54–62CrossRefPubMedGoogle Scholar
  14. Gaba S, Jamal S, Drug Discovery Consortium OS, Scaria V (2014) Cheminformatics models for inhibitors of Schistosoma mansoni thioredoxin glutathione reductase. Sci World J 2014:1–9. doi: 10.1155/2014/957107 CrossRefGoogle Scholar
  15. Gabbay E, Tarala R, Will R et al (1997) Interstitial lung disease in recent onset rheumatoid arthritis. Am J Respir Crit Care Med 156:528–535. doi: 10.1164/ajrccm.156.2.9609016 CrossRefPubMedGoogle Scholar
  16. Gasteiger J, Marsili M (1980) Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron 36:3219–3228. doi: 10.1016/0040-4020(80)801682 CrossRefGoogle Scholar
  17. Golbraikh A, Tropsha A (2002) Beware of Q2. J Mol Graph Model 20:269–276. doi: 10.1016/S1093-3263(01)00123-1 CrossRefPubMedGoogle Scholar
  18. Gonzalez A, Maradit Kremers H, Crowson CS et al (2007) The widening mortality gap between rheumatoid arthritis patients and the general population. Arthritis Rheum 56:3583–3587. doi: 10.1002/art.22979 CrossRefPubMedGoogle Scholar
  19. Guner OF, Bowen JP (2013) Pharmacophore modeling for ADME. Curr Top Med Chem 13:1327–1342. doi: 10.2174/15680266113139990037 CrossRefPubMedGoogle Scholar
  20. Halgren TA (1996) Merck molecular force field. III. Molecular geometries and vibrational frequencies for MMFF94. J Comput Chem 17:553–586. doi: 10.1002/(SICI)1096-987X(199604)17:5/6<553:AID-JCC3>3.0.CO;2-T CrossRefGoogle Scholar
  21. Imada K, Leonard WJ (2000) The Jak-STAT pathway. Mol Immunol 37(1–2):1–11. doi: 10.1016/S0161-5890(00)00018-3 CrossRefPubMedGoogle Scholar
  22. Irvine JD, Takahashi L, Lockhart K et al (1999) MDCK (Madin–Darby canine kidney) cells: a tool for membrane permeability screening. J Pharm Sci 88:28–33. doi: 10.1021/js9803205 CrossRefPubMedGoogle Scholar
  23. Jacobsson LT, Hanson RL, Knowler WC et al (1994) Decreasing incidence and prevalence of rheumatoid arthritis in Pima Indians over a twenty-five-year period. Arthritis Rheum 37:1158–1165. doi: 10.1002/art.1780370808 CrossRefPubMedGoogle Scholar
  24. Jamal S, Periwal V, Open Source Drug Discovery Consortium, Scaria V (2013) Predictive modeling of anti-malarial molecules inhibiting apicoplast formation. BMC Bioinform 14:55. doi: 10.1186/1471-2105-14-55 CrossRefGoogle Scholar
  25. Kapelyukh Y, Paine MJI, Maréchal J-D et al (2008) Multiple substrate binding by cytochrome P450 3A4: estimation of the number of bound substrate molecules. Drug Metab Dispos 36:2136–2144. doi: 10.1124/dmd.108.021733 CrossRefPubMedGoogle Scholar
  26. Kapetanovic IM (2008) Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. Chem Biol Interact 171:165–176. doi: 10.1016/j.cbi.2006.12.006 CrossRefPubMedGoogle Scholar
  27. Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem 37:4130–4146. doi: 10.1021/jm00050a010 CrossRefPubMedGoogle Scholar
  28. Klekota J, Roth FP (2008) Chemical substructures that enrich for biological activity. Bioinformatics 24:2518–2525. doi: 10.1093/bioinformatics/btn479 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Korzekwa KR, Krishnamachary N, Shou M et al (1998) Evaluation of atypical cytochrome P450 kinetics with two-substrate models: evidence that multiple substrates can simultaneously bind to cytochrome P450 active sites. Biochemistry 37:4137–4147. doi: 10.1021/bi9715627 CrossRefPubMedGoogle Scholar
  30. Kroot EJ, de Jong BA, van Leeuwen MA et al (2000) The prognostic value of anti-cyclic citrullinated peptide antibody in patients with recent-onset rheumatoid arthritis. Arthritis Rheum 43:1831–1835. doi: 10.1002/1529-0131(200008)43:8<1831:AID-ANR19>3.0.CO;2-6 CrossRefPubMedGoogle Scholar
  31. Liu K, Feng J, Young SS (2005) PowerMV: a software environment for molecular viewing, descriptor generation, data analysis and hit evaluation. J Chem Inf Model 45:515–522. doi: 10.1021/ci049847v CrossRefPubMedGoogle Scholar
  32. Ma X, Chen C, Yang J (2005) Predictive model of blood–brain barrier penetration of organic compounds. Acta Pharmacol Sin 26:500–512. doi: 10.1111/j.1745-7254.2005.00068.x CrossRefPubMedGoogle Scholar
  33. Masand VH, Rastija V (2017) PyDescriptor: a new PyMOL plugin for calculating thousands of easily understandable molecular descriptors. Chemom Intell Lab Syst 169:12–18. doi: 10.1016/j.chemolab.2017.08.003 CrossRefGoogle Scholar
  34. Milano TR (2010) Chemometrics and QSAR Research Group University of Milano-Bicocca Milano Italy (personal communication)Google Scholar
  35. Miners JO, Birkett DJ (1998) Cytochrome P4502C9: an enzyme of major importance in human drug metabolism. Br J Clin Pharmacol 45:525–538. doi: 10.1046/j.1365-2125.1998.00721.x CrossRefPubMedPubMedCentralGoogle Scholar
  36. Orbach H, Gilburd B, Brickman CM et al (2002) Anti-cyclic citrullinated peptide antibodies as a diagnostic test for rheumatoid arthritis and predictor of an erosive disease. Isr Med Assoc J 4:892–893PubMedGoogle Scholar
  37. Pang KS (2009) Safety testing of metabolites: expectations and outcomes. Chem Biol Interact 179:45–59. doi: 10.1016/j.cbi.2008.09.013 CrossRefPubMedGoogle Scholar
  38. Patlewicz G, Jeliazkova N, Safford RJ, Worth AP, Aleksiev B (2008) An evaluation of the implementation of the Cramer classification scheme in the Toxtree software. SAR QSAR Environ Res 19(5–6):495–524. doi: 10.1080/10629360802083871 CrossRefPubMedGoogle Scholar
  39. Periwal V, Rajappan JK, Open Source Drug Discovery Consortium et al (2011) Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets. BMC Res Notes 4:504. doi: 10.1186/1756-0500-4-504 CrossRefPubMedPubMedCentralGoogle Scholar
  40. Plenge RM (2009) Rheumatoid arthritis genetics: 2009 update. Curr Rheumatol Rep 11:351–356. doi: 10.1007/s11926-009-0050-0 CrossRefPubMedGoogle Scholar
  41. Ponce YM, Garit JAC, Torrens F et al (2004) Atom, atom-type, and total linear indices of the “molecular pseudograph’s atom adjacency matrix”: application to QSPR/QSAR studies of organic compounds. Molecules 9:1100–1123. doi: 10.3390/91201100 CrossRefPubMedGoogle Scholar
  42. Prusis P, Afzelius L (2009) Improvement of site of metabolism predictions for CYP3A4 by using discriminant analysis of compound preference of CYP3A4 X-ray structural conformers and subsequent docking. QSAR Comb Sci 28:865–868. doi: 10.1002/qsar.200810182 CrossRefGoogle Scholar
  43. Rannar S, Lindgren F, Geladi P, Wold S (1994) A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1: theory and algorithm. J Chemom 8:111–125. doi: 10.1002/cem.1180080204 CrossRefGoogle Scholar
  44. Riise T, Jacobsen BK, Gran JT (2000) Incidence and prevalence of rheumatoid arthritis in the county of Troms, northern Norway. J Rheumatol 27:1386–1389PubMedGoogle Scholar
  45. 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
  46. Sajeev R, Athira RS, Nufail M et al (2013) Computational predictive models for organic semiconductors. J Comput Electron 12:790–795. doi: 10.1007/s10825-013-0486-3 CrossRefGoogle Scholar
  47. Schlichting I (2000) The catalytic pathway of cytochrome P450cam at atomic resolution. Science 287:1615–1622. doi: 10.1126/science.287.5458.1615 CrossRefPubMedGoogle Scholar
  48. Scott EE, Halpert JR (2005) Structures of cytochrome P450 3A4. Trends Biochem Sci 30:5–7. doi: 10.1016/j.tibs.2004.11.004 CrossRefPubMedGoogle Scholar
  49. Seal A, Passi A, Jaleel UA et al (2012) In-silico predictive mutagenicity model generation using supervised learning approaches. J Cheminform 4:10. doi: 10.1186/1758-2946-4-10 CrossRefPubMedPubMedCentralGoogle Scholar
  50. Sheridan RP, Korzekwa KR, Torres RA, Walker MJ (2007) Empirical regioselectivity models for human cytochromes P450 3A4, 2D6, and 2C9. J Med Chem 50:3173–3184. doi: 10.1021/jm0613471 CrossRefPubMedPubMedCentralGoogle Scholar
  51. Shibi IG, Aswathy L, Jisha RS et al (2015) Molecular docking and QSAR analyses for understanding the antimalarial activity of some 7-substituted-4-aminoquinoline derivatives. Eur J Pharm Sci 77:9–23. doi: 10.1016/j.ejps.2015.05.025 CrossRefPubMedGoogle Scholar
  52. Shibi IG, Aswathy L, Jisha RS et al (2016) Virtual screening techniques to probe the antimalarial activity of some traditionally used phytochemicals. Comb Chem High Throughput Screen 19:572–591. doi: 10.2174/1386207319666160420141200 CrossRefPubMedGoogle Scholar
  53. Shou M, Grogan J, Mancewicz JA et al (1994) Activation of CYP3A4: evidence for the simultaneous binding of two substrates in a cytochrome P450 active site. Biochemistry 33:6450–6455CrossRefPubMedGoogle Scholar
  54. Smith JB, Haynes MK (2002) Rheumatoid arthritis—a molecular understanding. Ann Intern Med 136:908–922. doi: 10.7326/0003-4819-136-12-200206180-00012 CrossRefPubMedGoogle Scholar
  55. Smolen JS, Steiner G (2003) Therapeutic strategies for rheumatoid arthritis. Nat Rev Drug Discov 2:473–488. doi: 10.1038/nrd1109 CrossRefPubMedGoogle Scholar
  56. Smolen JS, Aletaha D, Koeller M et al (2007) New therapies for treatment of rheumatoid arthritis. Lancet 370:1861–1874. doi: 10.1016/S0140-6736(07)60784-3 CrossRefPubMedGoogle Scholar
  57. Stewart JJP (1993) MOPAC manual, 7th edn, 1993Google Scholar
  58. Strazielle N, Ghersi-Egea J-F (2005) Factors affecting delivery of antiviral drugs to the brain. Rev Med Virol 15:105–133. doi: 10.1002/rmv.454 CrossRefPubMedGoogle Scholar
  59. Todeschini R, Gramatica P (1998) New 3D molecular descriptors: the WHIM theory and QAR applications. Perspect Drug Discov 355–380Google Scholar
  60. Toyoshima H, Kusaba T, Yamaguchi M (1993) Cause of death in autopsied RA patients. Ryumachi 33(3):209–214 PubMedGoogle Scholar
  61. Trunzer M, Faller B, Zimmerlin A (2009) Metabolic soft spot identification and compound optimization in early discovery phases using MetaSite and LC-MS/MS validation. J Med Chem 52:329–335. doi: 10.1021/jm8008663 CrossRefPubMedGoogle Scholar
  62. Vasanthanathan P, Hritz J, Taboureau O et al (2009) Virtual screening and prediction of site of metabolism for cytochrome P450 1A2 ligands. J Chem Inf Model 49:43–52. doi: 10.1021/ci800371f CrossRefPubMedGoogle Scholar
  63. Vinita P, Jinuraj KR, Jaleel UCA, Scaria V (2011) Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets. BMC Res Notes 4:504. doi: 10.1186/1756-0500-4-504 CrossRefGoogle Scholar
  64. Wallace AC, Laskowski RA, Thornton JM (1995) LIGPLOT: a program to generate schematic diagrams of protein–ligand interactions. Protein Eng 8:127–134. doi: 10.1093/protein/8.2.127 CrossRefPubMedGoogle Scholar
  65. Weyand CM, Hicok KC, Conn DL, Goronzy JJ (1992) The influence of HLA-DRB1 genes on disease severity in rheumatoid arthritis. Ann Intern Med 117:801–806. doi: 10.7326/0003-4819-117-10-801 CrossRefPubMedGoogle Scholar
  66. Wildman SA, Crippen GM (1999) Prediction of physicochemical parameters by atomic contributions. J Chem Inf Comput Sci 39:868–873. doi: 10.1021/ci990307l CrossRefGoogle Scholar
  67. Williams JA, Hyland R, Jones BC et al (2004a) Drug–drug interactions for UDP-glucuronosyltransferase substrates: a pharmacokinetic explanation for typically observed low exposure (AUCi/AUC) ratios. Drug Metab Dispos 32:1201–1208. doi: 10.1124/dmd.104.000794 CrossRefPubMedGoogle Scholar
  68. Williams PA, Cosme J, Vinkovic DM et al (2004b) Crystal structures of human cytochrome P450 3A4 bound to metyrapone and progesterone. Science 305:683–686. doi: 10.1126/science.1099736 CrossRefPubMedGoogle Scholar
  69. Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58:109–130. doi: 10.1016/S0169-7439(01)00155-1 CrossRefGoogle Scholar
  70. Yamagishi H, Shirakami S, Nakajima Y et al (2015) Discovery of 3,6-dihydroimidazo[4,5-d]pyrrolo[2,3-b]pyridin-2(1H)-one derivatives as novel JAK inhibitors. Bioorg Med Chem 23:4846–4859. doi: 10.1016/j.bmc.2015.05.028 CrossRefPubMedGoogle Scholar
  71. Yamashita S, Furubayashi T, Kataoka M et al (2000) Optimized conditions for prediction of intestinal drug permeability using Caco-2 cells. Eur J Pharm Sci 10:195–204. doi: 10.1016/S0928-0987(00)00076-2 CrossRefPubMedGoogle Scholar
  72. Yano JK, Wester MR, Schoch GA et al (2004) The structure of human microsomal cytochrome P450 3A4 determined by X-ray crystallography to 2.05-A resolution. J Biol Chem 279:38091–38094. doi: 10.1074/jbc.C400293200 CrossRefPubMedGoogle Scholar
  73. Yee S (1997) In vitro permeability across Caco-2 cells (colonic) can predict in vivo (small intestinal) absorption in man—fact or myth. Pharm Res 14:763–766. doi: 10.1023/A:1012102522787 CrossRefPubMedGoogle Scholar
  74. Zhao X, Chen M, Huang B et al (2011) Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies on α(1A)-adrenergic receptor antagonists based on pharmacophore molecular alignment. Int J Mol Sci 12:7022–7037. doi: 10.3390/ijms12107022 CrossRefPubMedPubMedCentralGoogle Scholar
  75. Zhou D, Afzelius L, Grimm SW et al (2006) Comparison of methods for the prediction of the metabolic sites for CYP3A4-mediated metabolic reactions. Drug Metab Dispos 34:976–983. doi: 10.1124/dmd.105.008631 PubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Radhakrishnan S. Jisha
    • 1
  • Lilly Aswathy
    • 1
  • Vijay H. Masand
    • 2
  • Jayant M. Gajbhiye
    • 3
  • Indira G. Shibi
    • 1
  1. 1.Department of ChemistrySree Narayana CollegeThiruvananthapuramIndia
  2. 2.Department of ChemistryVidya Bharati CollegeAmravatiIndia
  3. 3.Division of Organic ChemistryCSIR-National Chemical LaboratoryPuneIndia

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