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Exploring structural requirements for peripherally acting 1,5-diaryl pyrazole-containing cannabinoid 1 receptor antagonists for the treatment of obesity

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Abstract

Peripherally acting cannabinoid 1 (CB1) receptor antagonists are considered as potential therapeutics for the treatment of obesity with desired efficacy and reduced central nervous system side effects. A dataset of 72 compounds containing the 1,5-diaryl pyrazole basic skeleton having peripheral CB1 receptor antagonistic activity was utilized for three-dimensional quantitative structure–activity relationship studies. Compounds of the series exhibited high variations in the biological activity and chemical structures. Different types of molecular alignments, such as atom-based, data-based, centroid-based and centroid/atom-based were utilized to develop the best CoMFA model. The best CoMFA model was obtained with a database alignment and the same alignment was further used for the development of a CoMSIA model. The best developed CoMFA model had \(r^{2}_\mathrm{cv} = 0.552\) with six components, \(r^{2}_\mathrm{ncv} = 0.973,\,\mathrm{standard\,error\,of\,estimate} = 0.162,\,F{\text {-value}} = 281.239,\) while the best developed CoMSIA model had \(r^{2}_\mathrm{cv} = 0.571\) with six components, \(r^{2}_\mathrm{ncv} = 0.960,\, \mathrm{standard} \mathrm{error\,of\,estimate} = 0.196\) and \(F{\text {-value}} = 188.701.\) The predictive \(r^{2}\) values of these two models showed test set predictions of 0.528 and 0.679 for the best CoMFA and CoMSIA models, respectively. Based on a higher \(r^{2}_\mathrm{pred}\) value, the CoMSIA model was found to be the best one. The prediction accuracy and reliability of the best developed CoMSIA model have been validated using well-established methods. Using the inputs from the best CoMSIA contour maps, several novel highly selective peripherally acting CB1 receptor antagonists have been designed and reported herein.

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References

  1. http://www.mexicobariatriccenter.com/obesity-statistics-2013-usa-world. Accessed 13 Oct 2014

  2. Swinburn BA, Caterson I, Seidell JC, James WPT (2004) Diet, nutrition and the prevention of excess weight gain and obesity. Public Health Nutr 7:123–146. doi:10.1079/PHN2003585

    CAS  PubMed  Google Scholar 

  3. Halford JC (2001) Pharmacology of appetite suppression: implication for the treatment of obesity. Curr Drug Targets 2:353–370. doi:10.2174/1389450013348209

    Article  CAS  PubMed  Google Scholar 

  4. Kirilly E, Gonda X, Bagdy G (2012) CB1 receptor antagonists: new discoveries leading to new perspectives. Acta Physiol 205:1–20. doi:10.1111/j.1748-1716.2011.02402.x

    Article  Google Scholar 

  5. http://www.who.int/mediacentre/factsheets/fs311/en/index.html. Accessed 13 Oct 2014

  6. Rodgers RJ, Tschop MH, Wilding JPH (2012) Anti-obesity drugs: past, present and future. Dis Model Mech 5:621–626. doi:10.1242/dmm.009621

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  7. Powell AG, Apovian CM, Aronne LJ (2011) New drug targets for the treatment of obesity. Clin Pharmacol Ther 90:40–51. doi:10.1038/clpt.2011.82

    Article  CAS  PubMed  Google Scholar 

  8. Di Marzo V, Despres JP (2009) CB1 antagonists for obesity—what lessons have we learned from rimonabant? Nat Rev Endocrinol 5:633–638. doi:10.1038/nrendo.2009.197

    Article  PubMed  Google Scholar 

  9. Pertwee RG (1997) Pharmacology of cannabinoid CB1 and CB2 receptors. Pharmacol Ther 74:129–180. doi:10.1016/S0163-7258(97)82001-3

    CAS  PubMed  Google Scholar 

  10. Bermudez-Silva FJ, Viveros MP, McPartland JM, Rodriguez de Fonseca F (2010) The endocannabinoid system, eating behavior and energy homeostasis: the end or a new beginning? Pharmacol Biochem Behav 95:375–382. doi:10.1016/j.pbb.2010.03.012

    Article  CAS  PubMed  Google Scholar 

  11. Vettor R, Pagano C (2009) The role of the endocannabinoid system in lipogenesis and fatty acid metabolism. Best Pract Res Clin Endocrinol Metab 23:51–63. doi:10.1016/j.beem.2008.10.002

    Article  CAS  PubMed  Google Scholar 

  12. Yen-Ku W, Ching-Fang Y, Tai WL, Ming-Shiu H (2011) A new perspective of cannabinoid 1 receptor antagonists: approaches toward peripheral CB1R blockers without crossing the blood–brain barrier. Curr Top Med Chem 11:1421–1429. doi:10.2174/156802611795860997

    Article  Google Scholar 

  13. Kennett GA, Clifton PG (2010) New approaches to the pharmacological treatment of obesity: can they break through the efficacy barrier? Pharmacol Biochem Behav 97:63–83. doi:10.1016/j.pbb.2010.07.020

    Article  CAS  PubMed  Google Scholar 

  14. Leite CE, Mocelin CA, Petersen GO, Leal MB, Thiesen FV (2009) Rimonabant: an antagonist drug of the endocannabinoid system for the treatment of obesity. Pharmacol Rep 61:217–224. doi:10.1016/S1734-1140(09)70025-8

    Article  CAS  PubMed  Google Scholar 

  15. Sharma MK, Murumkar PR, Kanhed AM, Giridhar R, Yadav MR (2014) Prospective therapeutic agents for obesity: molecular modification approaches of centrally and peripherally acting selective cannabinoid 1 receptor antagonists. Eur J Med Chem 79:298–339. doi:10.1016/j.ejmech.2014.04.011

    Article  CAS  PubMed  Google Scholar 

  16. Hung MS, Chang CP, Li TC, Yeh TK, Song JS, Lin Y, Wu CH, Kuo PC, Amancha PK, Wong YC, Hsiao WC, Chao YS, Shia KS (2010) Discovery of 1-(2,4-dichlorophenyl)-4-ethyl-5-(5-(2-(4-(trifluoromethyl)phenyl)ethynyl)thiophen-2-yl)-N-(piperidin-1-yl)-1Hpyrazole-3-carboxamide as a potential peripheral cannabinoid-1 receptor inverse agonist. Chem Med Chem 5:1439–1443. doi:10.1002/cmdc.201000246

    Article  CAS  PubMed  Google Scholar 

  17. Klumpers LE, Fridberg M, de Kam ML, Little PB, Jensen NO, Kleinloog HD, Elling CE, van Gerven JM (2013) Peripheral selectivity of the novel cannabinoid receptor antagonist TM38837 in healthy subjects. Br J Clin Pharmacol 76:846–857. doi:10.1111/bcp.12141

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. Quarta C, Mazza R, Obici S, Pasquali R, Pagotto U (2011) Energy balance regulation by endocannabinoids at central and peripheral levels. Trends Mol Med 17:518–526. doi:10.1016/j.molmed.2011.05.002

    Article  CAS  PubMed  Google Scholar 

  19. Douguet D, Munier-Lehmann H, Labesse G, Pochet S (2005) LEA3D: a computer-aided ligand design for structure-based drug design. J Med Chem 48:2457–2468. doi:10.1021/jm0492296

    Article  CAS  PubMed  Google Scholar 

  20. Wang P, Cai J, Chen J, Li L, Sun C, Xue B, Ji M (2014) 3D-QSAR and docking studies of piperidine carboxamide derivatives as ALK inhibitors. Med Chem Res 23:2576–2583. doi:10.1007/s00044-013-0853-4

    Article  CAS  Google Scholar 

  21. Murumkar PR, Giridhar R, Yadav MR (2008) 3D-quantitative structure–activity relationship studies on benzothiadiazepine hydroxamates as inhibitors of tumor necrosis factor-\(\alpha \) converting enzyme. Chem Biol Drug Des 71:363–373. doi:10.1111/j.1747-0285.2008.00639.x

    Article  CAS  PubMed  Google Scholar 

  22. Murumkar PR, DasGupta S, Zambre VP, Giridhar R, Yadav MR (2009) Development of predictive 3D-QSAR CoMFA and CoMSIA models for \(\beta \)-aminohydroxamic acid-derived tumor necrosis factor-\(\alpha \) converting enzyme inhibitors. Chem Biol Drug Des 73:97–107. doi:10.1111/j.1747-0285.2008.00737.x

    Article  CAS  PubMed  Google Scholar 

  23. Murumkar PR, Zambre VP, Yadav MR (2010) Development of predictive pharmacophore model for in silico screening, and 3D QSAR CoMFA and CoMSIA studies for lead optimization, for designing of potent tumor necrosis factor alpha converting enzyme inhibitors. J Comput Aided Mol Des 24:143–156. doi:10.1007/s10822-010-9322-z

    Article  CAS  PubMed  Google Scholar 

  24. Murumkar PR, Le L, Truong TN, Yadav MR (2011) Determination of structural requirements of influenza neuraminidase type A inhibitors and binding interaction analysis with the active site of A/H1N1 by 3D-QSAR CoMFA and CoMSIA modeling. Med Chem Commun 2:710–719. doi:10.1039/c1md00050k

    Article  CAS  Google Scholar 

  25. Murumkar PR, Sharma MK, Shinde AC, Bothara KG (2013) Three-dimensional quantitative structure–activity relationship CoMFA/CoMSIA on pyrrolidine-based tartrate diamides as TACE inhibitors. Med Chem Res 22:4192–4201. doi:10.1007/s00044-012-0409-z

    Article  CAS  Google Scholar 

  26. Murumkar PR, Sharma MK, Giridhar R, Yadav MR (2014) Virtual screening-based identification of lead molecules as selective TACE inhibitors. Med Chem Res 24:226–244. doi:10.1007/s00044-014-1097-7

    Article  Google Scholar 

  27. Zambre VP, Murumkar PR, Giridhar R, Yadav MR (2009) Structural investigations of acridine derivatives by CoMFA and CoMSIA reveal novel insight into their structures toward DNA G-Quadruplex mediated telomerase inhibition and Offer a highly predictive 3D-model for substituted Acridines. J Chem Inf Model 49:1298–1311. doi:10.1021/ci900036w

    Article  CAS  PubMed  Google Scholar 

  28. Zambre VP, Murumkar PR, Giridhar R, Yadav MR (2010) Development of highly predictive 3D-QSAR CoMSIA models for anthraquinone and acridone derivatives as telomerase inhibitors targeting G-quadruplex DNA telomere. J Mol Graph Model 29:229–239. doi:10.1016/j.jmgm.2010.07.003

    Article  CAS  PubMed  Google Scholar 

  29. Zambre VP, Giridhar R, Yadav MR (2013) Pharmacophore modeling and 3D-QSAR (CoMSIA) studies for structural requirements of some triazine derivatives as G-quadruplex binders for telomerase inhibition. Med Chem Res 22:4685–4699. doi:10.1007/s00044-012-0447-6

    Article  CAS  Google Scholar 

  30. Puntambekar DS, Giridhar R, Yadav MR (2006) 3D-QSAR CoMFA/CoMSIA studies on 5-aryl-2,2-dialkyl-4-phenyl-3(2H)-furanone derivatives, as selective COX-2 inhibitors. Acta Pharm 56:157–174

    CAS  PubMed  Google Scholar 

  31. Puntambekar DS, Giridhar R, Yadav MR (2006) 3D-QSAR studies of farnesyltransferase inhibitors: a comparative molecular field analysis approach. Bioorg Med Chem Lett 16:1821–1827. doi:10.1016/j.bmcl.2006.01.019

    Article  CAS  PubMed  Google Scholar 

  32. Puntambekar DS, Giridhar R, Yadav MR (2006) Understanding the antitumor activity of novel tricyclicpiperazinyl derivatives as farnesyltransferase inhibitors using CoMFA and CoMSIA. Eur J Med Chem 41:1279–1292. doi:10.1016/j.ejmech.2006.07.002

    Article  CAS  PubMed  Google Scholar 

  33. Puntambekar DS, Giridhar R, Yadav MR (2008) Insights into the structural requirements of farnesyltransferase inhibitors as potential anti-tumor agents based on 3D-QSAR CoMFA and CoMSIA models. Eur J Med Chem 43:142–154. doi:10.1016/j.ejmech.2007.02.003

    Article  CAS  PubMed  Google Scholar 

  34. Hernandez-Vazquez E, Mendez-Lucio O, Hernandez-Luis F (2013) Activity landscape analysis, CoMFA and CoMSIA studies of pyrazole CB1 antagonists. Med Chem Res 22:4133–4145. doi:10.1007/s00044-012-0418-y

    Article  CAS  Google Scholar 

  35. Sasmal PK, Reddy DS, Talwar R, Venkatesham B, Balasubrahmanyam D, Kannan M, Srinivas P, Kumar KS, Devi BN, Jadhav VP, Khan SK, Mohan P, Chaudhury H, Bhuniya D, Iqbal J, Chakrabarti R (2011) Novel pyrazole-3-carboxamide derivatives as cannabinoid-1 (CB1) antagonists: journey from non-polar to polar amides. Bioorg Med Chem Lett 21:562–568. doi:10.1016/j.bmcl.2010.10.055

    Article  CAS  PubMed  Google Scholar 

  36. Sasmal PK, Talwar R, Swetha J, Balasubrahmanyam D, Venkatesham B, Rawoof KA, Devi BN, Jadhav VP, Khan SK, Mohan P, Reddy DS, Nyavanandi VK, Nanduri S, Kumar SK, Kannan M, Srinivas P, Nadipalli P, Chaudhury H, Sebastian VJ (2011) Structure–activity relationship studies of novel pyrazole and imidazole carboxamides as cannabinoid-1 (CB1) antagonists. Bioorg Med Chem Lett 21:4913–4918. doi:10.1016/j.bmcl.2011.06.017

    Article  CAS  PubMed  Google Scholar 

  37. SYBYL 7.0. Tripos, Inc., St. Louis

  38. Adhikari N, Halder AK, Mondal C, Jha T (2013) Exploring structural requirements of aurone derivatives as antimalarials by validated DFT-based QSAR, HQSAR, and COMFA-COMSIA approach. Med Chem Res 22:6029–6045. doi:10.1007/s00044-013-0590-8

    Article  CAS  Google Scholar 

  39. 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

    Article  CAS  PubMed  Google Scholar 

  40. Zha C, Brown GB, Brouillette WJ (2014) A highly predictive 3D-QSAR model for binding to the voltage-gated sodium channel: design of potent new ligands. Bioorg Med Chem 22:95–104. doi:10.1016/j.bmc.2013.11.049

    Article  CAS  PubMed  Google Scholar 

  41. Kliebe 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

    Article  Google Scholar 

  42. Cramer RD, Bunce JD, Patterson DE (1988) Crossvalidation, bootstrapping, and partial least squares compared with multiple regression in conventional QSAR studies. Quant Struct Act Relatsh 7:18–25. doi:10.1002/qsar.19880070105

    Article  Google Scholar 

  43. Kamath S, Buolamwini JK (2003) Receptor-guided alignment-based comparative 3D-QSAR studies of benzylidene malonitrile tyrphostins as EGFR and HER-2 kinase inhibitors. J Med Chem 46:4657–4668. doi:10.1021/jm030065n

    Article  CAS  PubMed  Google Scholar 

  44. Araujo JQ, de Brito MA, Hoelz LVB, de Alencastro RB, Castro HC, Rodrigues CR, Albuquerque MG (2011) Receptor-dependent (RD) 3D-QSAR approach of a series of benzylpiperidine inhibitors of human acetylcholinesterase (HuAChE). Eur J Med Chem 46:39–51. doi:10.1016/j.ejmech.2010.10.009

    Article  CAS  PubMed  Google Scholar 

  45. Dunn WJ, Wold S, Edlund V, Helberg S (1984) Multivariate structure–activity relationship between data from a battery of biological tests and an ensemble of structure descriptors: the PLS method. Quant Struct Act Relatsh 3:131–137. doi:10.1002/qsar.19840030402

    Article  CAS  Google Scholar 

  46. Hansch C, Verma RP (2009) Overcoming tumor drug resistance with C\(_{2}\)-modified 10-deacetyl-7-propionyl cephalomannines: a QSAR study. Mol Pharm 6:849–860. doi:10.1021/mp800138w

    Article  CAS  PubMed  Google Scholar 

  47. Verma RP, Hansch C (2008) Combating the threat of anthrax: a quantitative structure–activity relationship approach. Mol Pharm 5:745–759. doi:10.1021/mp8000149

    Article  CAS  PubMed  Google Scholar 

  48. Kristama R, Parmara V, Viswanadhana VN (2013) 3D-QSAR analysis of TRPV1 inhibitors reveals a pharmacophore applicable to diverse scaffolds and clinical candidates. J Mol Graph Model 45:157–172. doi:10.1016/j.jmgm.2013.08.014

    Article  Google Scholar 

  49. Liu Y, Lu X, Xue T, Hu S, Zhang H (2014) Receptor and ligand-based 3D-QSAR study on a series of pyrazines/pyrrolidylquinazolines as inhibitors of PDE10A enzyme. Med Chem Res 23:775–789. doi:10.1007/s00044-013-0619-z

    Article  CAS  Google Scholar 

  50. Roy PP, Paul S, Mitra I, Roy K (2009) On two novel parameters for validation of predictive QSAR models. Molecules 14:1660–1701. doi:10.3390/molecules14051660

    Article  CAS  PubMed  Google Scholar 

  51. Roy PP, Roy K (2008) On some aspects of variable selection for partial least squares regression models. QSAR Comb Sci 27:302–313. doi:10.1002/qsar.200710043

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  53. 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 

  54. 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

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  55. 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 

  56. Dossou KS, Devkota KP, Kavanagh PV, Beutler JA, Egan JM, Moaddel R (2013) Development and preliminary validation of a plate-based CB1/CB2 receptor functional assay. Anal Biochem 437:138–143. doi:10.1016/j.ab.2013.02.025

  57. Cooper M, Receveur JM, Bjurling E, Norregaard PK, Nielsen PA, Skold N, Hogberg T (2010) Exploring SAR features in diverse library of 4-cyanomethyl-pyrazole-3-carboxamides suitable for further elaborations as CB1 antagonists. Bioorg Med Chem Lett 20:26–30. doi:10.1016/j.bmcl.2009.11.047

  58. Receveur J-M, Murray A, Linget J-M et al (2010) Conversion of 4-cyanomethyl-pyrazole-3-carboxamides into CB1 antagonists with lowered propensity to pass the blood–brain-barrier. Bioorg Med Chem Lett 20:453–457. doi:10.1016/j.bmcl.2009.12.003

    Article  CAS  PubMed  Google Scholar 

  59. Sharma MK, Murumkar PR, Barmade MA, Giridhar R, Yadav MR (2015) A comprehensive patents review on cannabinoid 1 receptor antagonists as antiobesity agents. Expert Opin Ther Pat. doi:10.1517/13543776.2105.1064898

  60. Qikprop version 3.2. Schrödinger, LLC, New york (2009)

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Acknowledgments

MKS is thankful to University Grants Commission (UGC), New Delhi for awarding Junior Research Fellowship (JRF) under the RFSMS-BSR Programme [No. F. 7-129/2007 (BSR)].

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Correspondence to Mange Ram Yadav.

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Sharma, M.K., Murumkar, P.R., Giridhar, R. et al. Exploring structural requirements for peripherally acting 1,5-diaryl pyrazole-containing cannabinoid 1 receptor antagonists for the treatment of obesity. Mol Divers 19, 871–893 (2015). https://doi.org/10.1007/s11030-015-9611-5

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