Drug design of new 5-HT6 antagonists: a QSAR study of arylsulfonamide derivatives

Abstract

Several studies underscore that the 5-hydroxytryptamine subtype 6 (5-HT6) receptor is intrinsically related to the onset of Alzheimer’s disease and its blocking significantly improve the learning and memory processes. In this manuscript, we apply quantitative structure-activity relationship (QSAR) techniques to a series of potential arylsulfonamide-derived 5-HT6 receptor antagonists aiming to design new anti-AD ligands. In order to describe physicochemical properties of the compounds, a plethora of descriptor types was calculated, and then selected by statistical techniques to build models that relate the chemical structure to antagonist activity of these studied ligands. Thereafter, structural variations were performed on the C15, C25, and C47 compounds by analyzing the steric and electrostatic fields as well as 2D maps. At last, the new compounds were submitted to the constructed QSAR models which presented promising results. It is noteworthy that the C4704 compound exhibited the highest biological activity value, surpassing even the values of the compounds used in the construction of the model. In conclusion, the robustness of the model allowed to confidently predict the biological activity values of the designed compounds.

Graphical abstract

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

References

  1. 1.

    Alzheimer's & Dementia (2017) Alzheimer’s disease facts and figures. Alzheimers Dement 13:325–373. https://doi.org/10.1016/j.jalz.2017.02.001

    Article  Google Scholar 

  2. 2.

    Alzheimer’s Disease International. A new landmark for people with dementia. http://www.alz.co.uk/media/160825 (Acessed May 15, 2019).

  3. 3.

    Sayeg N How many people suffer from Alzheimer’s disease worlwide and Brazil? AlzheimerMed-Informação & Solidariedade. http://www.alzheimermed.com.br/ (Acessed Jun 10, 2019).

  4. 4.

    The human memory- neurons & synapses. http://www.human-memory.net/brain_neurons.html (Acessed Jun 11, 2019).

  5. 5.

    Benhamú B, Fontecha MM, Villa HV, Pardo L, Rodriguez MLL (2014) Serotonin 5-HT6 receptor antagonists for the treatment of cognitive deficiency in Alzheimer’s disease. J Med Chem 57:7160–7181. https://doi.org/10.1021/jm5003952

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Upton N, Chuang TT, Hunter AJ, Virley DJ (2008) 5-HT6 antagonist as novel cognitive enhancing agent for Alzheimer’s disease. Neurotherapeutics 5:458–459. https://doi.org/10.1016/j.nurt.2008.05.008

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Karila D, Freret T, Bouet V, Boulouard M, Dallemagne P, Rochais C (2015) Therapeutic potential of 5-HT6 receptor agonists. J Med Chem 58:7901–7912. https://doi.org/10.1021/acs.jmedchem.5b00179

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Karsten W, Andreas H, Anton B (2015) Investigational drugs targeting 5-HT6 receptors for the treatment of Alzheimer's disease. Expert Opin Investig Drugs 24:1515–1528. https://doi.org/10.1517/13543784.2015.1102884

    CAS  Article  Google Scholar 

  9. 9.

    Meneses A (2001) Role of 5-HT6 receptors in memory formation. Drug News Perspect 14:396–400. https://doi.org/10.1517/13543784.2015.1102884

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Riemer C, Borroni E, Levet-Trafit B, Martin JR, Poli S, Porter RHP, Bös M (2003) Influence of the 5-HT6 receptor on acetylcholine release in the cortex: pharmacological characterization of 4-(2-bromo-6- pyrrolidin-1-ylpyridine-4-sulfonyl) phenylamine, a potent and selective 5-HT6 receptor antagonist. J Med Chem 46:1273–1276. https://doi.org/10.1021/jm021085c

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Wolley ML, Marsden CA, Sleight AJ, Fone KCF (2003) Reversal of a cholinergic-induced deficit in a rodent model of recognition memory by the selective 5-HT6 receptor antagonist, Ro 04–6790. Psychopharmacology 170:358–367. https://doi.org/10.1007/s00213-003-1552-5

    CAS  Article  Google Scholar 

  12. 12.

    Hirst WD, Abrahamsen B, Blaney FE, Calver AR, Aloj L, Price GW, Medhurst AD (2003) Differences in the central nervous system distribution and pharmacology of the mouse 5-hydroxytryptamine-6 receptor compared with rat and human receptors investigated by radioligand binding, site-directed mutagenesis, and molecular modeling. Mol Pharmacol 64:1295–1308. https://doi.org/10.1124/mol.64.6.1295

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Hirst WD, Stean TO, Rogers DC, Sunter D, Pugh P, Moss SF, Bromidge SM, Riley G, Smith DR, Bartlett S, Heidbreder CA, Atkins AR, Lacroix LP, Dawson LA, Foley AG, Regan CM, Upton N (2006) SB-399885 is a potent, selective 5-HT6 receptor antagonist with cognitive enhancing properties in aged rat water maze and novel object recognition models. Eur J Pharmacol 553:109–119. https://doi.org/10.1016/j.ejphar.2006.09.049

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    López-Rodríguez ML, Benhamú B, de la Fuente T, Sanz A, Pardo L, Campilo MA (2005) Three-dimensional pharmacophore model for 5-hydroxytryptamine6 (5-HT6) receptor antagonists. J Med Chem 48:416–4219. https://doi.org/10.1021/jm050247c

    CAS  Article  Google Scholar 

  15. 15.

    Sikazwe D, Bondarev ML, Dukat M, Rangisetty JB, Sanz A, Roth BL, Glennon RA (2006) Binding of sulfonyl-containing arylalkylamines at human 5-HT6 serotonin receptor. J Med Chem 49:5217–5225. https://doi.org/10.1021/jm060469q

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Kim HJ, Doddareddy MR, Choo H, Cho YS, No KT, Park WK, Pae AN (2005) New serotonin 5-HT6 ligands from common feature pharmacophore hypotheses. J Med Chem 48:197–206. https://doi.org/10.1021/ci700160t

    CAS  Article  Google Scholar 

  17. 17.

    De la Fuente T, Martín-Fontecha M, Sallander J, Benhamú B, Campilo L, Medina RA, Pellissier LP, Claeysen S, Dumuis A, Pardo L, López-Rodrigues ML (2010) Benzimidazole derivatives as new serotonin 5-HT6 receptor antagonists. Molecular mechanisms of receptor inactivation. J Med Chem 53:1357–1369. https://doi.org/10.1021/jm901672k

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Schwartz TW, Frimurer TM, Holst B, Rosenkilde MM, Elling CE (2006) Molecular mechanism of 7TM receptor activation - a global toggle switch model. Annu Rev Pharmacol Toxicol 46:481–519. https://doi.org/10.1146/annurev.pharmtox.46.120604.141218

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Mella J, Villegas F, Morales-Verdejo C, Lagos CF, Recabarren-Gajardo G (2017) Structure-activity relationships studies on weakly basic N-arylsulfonylindoles with an antagonistic profile in the 5-HT6 receptor. J Mol Struct 1139:362–370. https://doi.org/10.1016/j.molstruc.2017.03.067

    CAS  Article  Google Scholar 

  20. 20.

    Doddareddy MR, Cho YS, Koh HY, Pae AN (2004) CoMFA and CoMSIA 3D QSAR analysis on N1-arylsulfoylindole compounds as 5-HT6 antagonists. Bioorg Med Chem 12:3977–3985. https://doi.org/10.1016/j.bmc.2004.06.007

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Hao M, Li Y, Li H, Zhang S (2011) Investigation of the structure requirement for 5-HT6 binding affinity of arylsulfonyl derivatives: a computational study. Int J Mol Sci 12:5011–5030. https://doi.org/10.3390/ijms12085011

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Cole DC, Lennox WJ, Lombardi S, Ellingboe JW, Bernotas RC, Tawa GJ, Mazandarani H, Smith DL, Zhang G, Coupet J, Schechter LE (2005) Discovery of 5-arylsulfonamido-3-(pyrrolidine-2-ylmethy)-1H-indole derivatives as potent. Selective 5-HT6 receptor agonists and antagonists. J Med Chem 48:353–356. https://doi.org/10.1021/jm049243i

    CAS  Article  PubMed  Google Scholar 

  23. 23.

    Harder E, Wolfgang D, Maple J, Wu C, Reboul M, Xiang JY, Wang L, Lupyan D, Dahlgren MK, Knight JL, Kaus JW, Cerutti DS, Krilov G, Jorgensen WL, Abel R, Friesner RA (2015) OPLS3: a force field providing broad coverage of drug-like small molecules and proteins. J Chem Theory Comp 12:281–296. https://doi.org/10.1021/acs.jctc.5b00864

    CAS  Article  Google Scholar 

  24. 24.

    Schrödinger Release 2017–1. Small-molecule drug discovery suite. Schrödinger. LCC. New York

  25. 25.

    Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Petersson GA, Nakatsuji H, Li X, Caricato M, Marenich A, Bloino J, Janesko BG, Gomperts R, Mennucci B, Hratchian HP, Ortiz JV, Izmaylov AF, Sonnenberg JL, Williams-Young D, Ding F, Lipparini F, Egidi F, Goings J, Peng B, Petrone A, Henderson T, Ranasinghe D, Zakrzewski VG, Gao J, Rega N, Zheng G, Liang W, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Throssell K, Montgomery JA Jr, Peralta JE, Ogliaro F, Bearpark M, Heyd JJ, Brothers E, Kudin KN, Staroverov VN, Keith T, Kobayashi R, Normand J, Raghavachari K, Rendell A, Burant JC, Iyengar SS, Tomasi J, Cossi M, Millam JM, Klene M, Adamo C, Cammi R, Ochterski JW, Martin RL, Morokuma K, Farkas O, Foresman JB, Fox DJ (2016) Gaussian 09, Revision A.02. Gaussian, Inc., Wallingford CT

  26. 26.

    Tetko IV, Gasteiger J, Todeschini R, Mauri A, Livingstone D, Ertl P, Palyulin VA, Radchenko EV, Zefirov NS, Makarenko AS, Tanchuk VY, Prokopenko VV (2005) Virtual computational chemistry laboratory - design and description. J Comput Aided Mol Des 19:453–463. https://doi.org/10.1007/s10822-005-8694-y

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    De Oliveira DB, Gaudio AC (2000) BuildQSAR: a new computer program for QSAR analysis. Quant Struct -Act Relationships 19:599–601. https://doi.org/10.1002/1521-3838(200012)19:6%3C599::AID-QSAR599%3E3.0.CO;2-B

    Article  Google Scholar 

  28. 28.

    Kennard Ronald W, Stone LA (1969) Computer aided design of experiments. Technometrics 11:137–148. https://doi.org/10.2307/1266770

    Article  Google Scholar 

  29. 29.

    Martin TM, Harten P, Yong DM, Muratov EN, Golbraikg A, Zhu H, Tropsha A (2012) Does rational selection of training ant test sets improve the outcome of QSAR modeling? J Chem Inf Model 52:2570–2578. https://doi.org/10.1021/ci300338w

    CAS  Article  PubMed  Google Scholar 

  30. 30.

    SYBYL. SYBYL 8.2. St. Louis. Missouri

  31. 31.

    Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58:109–130. https://doi.org/10.1016/S0169-7439(01)00155-1

    CAS  Article  Google Scholar 

  32. 32.

    Infometrix Inc. Pirouette 3.10 (2001) Woodinville, WA

  33. 33.

    Ferreira MMC (2002) Multivariate QSAR. J Braz Chem Soc 13:742–753. https://doi.org/10.1590/S0103-50532002000600004

    CAS  Article  Google Scholar 

  34. 34.

    Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation ins the absolute essential for successful application and interpretation of QSPR model. QSAR Comb Sci 22:69–77. https://doi.org/10.1002/qsar.200390007

    CAS  Article  Google Scholar 

  35. 35.

    Kiralj R, Ferreira MMC (2009) Basic validation procedures for regression models in QSAR and QSPR studies: theory and application. J Braz Chem Soc 20:770–787. https://doi.org/10.1590/S0103-50532009000400021

    CAS  Article  Google Scholar 

  36. 36.

    Martins JPA, Ferreira MMC (2013) QSAR modeling: a new open source computational package to generate and validate QSAR models. Quim Nova 36:554–560. https://doi.org/10.1590/S0100-40422013000400013

    CAS  Article  Google Scholar 

  37. 37.

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Eriksson L, Jaworska J, Worth AP, Cronin MTD, McDowell RM, Gramatica EP (2003) Methods for reliability and uncertainty assessment for applicability evaluations of classification and regression-base QSARs. Environ Health Perspect 10:1361–1375. https://doi.org/10.1289/ehp.5758

    CAS  Article  Google Scholar 

  39. 39.

    Zare-Shahabadi V, Lotfizadeh M, Gandomani ARA, Papari MM (2013) Determination of boiling points of azeotropic mixtures using quantitative structure–property relationship (QSPR) strategy. J Mol Liq 188:222–229. https://doi.org/10.1016/j.molliq.2013.09.037

    CAS  Article  Google Scholar 

  40. 40.

    Burden FR (1989) Molecular identification number for structure searches. J Chem Inf Comput Sci 29:225–227. https://doi.org/10.1021/ci00063a011

    CAS  Article  Google Scholar 

  41. 41.

    Todeschini R, Vandycke V (2000) Handbook of molecular descriptor. Wiley-VCH, Weinheim

    Google Scholar 

  42. 42.

    Hemmer M, Steinhauer V, Gasteiger J (1999) Deriving the 3D structure of organic molecules from their infrared spectra. Vib Spectrosc 19:151–164. https://doi.org/10.1016/S0924-2031(99)00014-4

    CAS  Article  Google Scholar 

  43. 43.

    Clare BW (1995) The relationship of charge transfer complexes to frontier orbital energies in QSAR. J Mol Struct 331:63–78. https://doi.org/10.1016/0166-1280(94)03783-H

    CAS  Article  Google Scholar 

  44. 44.

    Clare BW (1995) Charge transfer complexes and frontier orbital energies in QSAR: a congeneric series of electron acceptors. J Mol Struct 337:139–150. https://doi.org/10.1016/0166-1280(95)04135-S

    CAS  Article  Google Scholar 

  45. 45.

    Heaton CA, Miller AK, Powell RL (2001) Predicting the reactivity of fluorinated compounds with copper using semi-empirical calculations. J Fluor Chem 107:9–11. https://doi.org/10.1016/S0022-1139(00)00324-9

    Article  Google Scholar 

  46. 46.

    Honório KM, Da Silva ABF (2003) An AM1 study on the electron-donating and electron-accepting character of biomolecules. Int J Quantum Chem 95:126–132. https://doi.org/10.1002/qua.10661

    CAS  Article  Google Scholar 

Download references

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brasil (CAPES) (Finance Code 001), FAPESP (2016/10118-0,2018/06680-7), and CNPq. The research carried out using the computational resources of the Center for Mathematical Sciences Applied to Industry (CeMEAI) is funded by FAPESP (grant 2013/07375-0).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Albérico B. F. da Silva.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 22 kb).

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

da Silva, A.P., de Angelo, R.M., de Paula, H. et al. Drug design of new 5-HT6 antagonists: a QSAR study of arylsulfonamide derivatives. Struct Chem 31, 1585–1597 (2020). https://doi.org/10.1007/s11224-020-01513-z

Download citation

Keywords

  • QSAR
  • PLS
  • CoMFA
  • Drug-design
  • Alzheimer