Generic placeholder image

Current Computer-Aided Drug Design


ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

Research Article

QSAR Analysis of Multimodal Antidepressants Vortioxetine Analogs Using Physicochemical Descriptors and MLR Modeling

Author(s): David M. Rajathei*, Subbiah Parthasarathy and Samuel Selvaraj

Volume 15, Issue 4, 2019

Page: [294 - 307] Pages: 14

DOI: 10.2174/1573409914666181011144810

Price: $65


Background: Vortioxetine is a multimodal antidepressant drug with combined effects on SERT as an inhibitor, 5-HT1A as agonist and 5-HT3A as an antagonist. Series of vortioxetine analogs have been reported as multi antidepressant compounds and they block serotonin transport into the neuronal cells, activate the postsynaptic 5-HT1A receptors and eliminate the low activity of 5-HT3A receptors.

Objective: To explore the important properties of vortioxetine analogs involved in antidepressant activity by developing 2D QSAR models.

Methods: Selections of significant descriptors were performed by Least Absolute Shrinkage and Selection Operator (LASSO) method and, the Multiple Linear Regression (MLR) method and All Subsets and GA algorithm included in QSARINS software were used for generating QSAR models. Further, the virtual screening was performed based on bioactivity and structure similarity using the PubChem database.

Results: The four descriptor model of complementary information content (CIC2), solubility (bcutp3), mass (bcutm8) and partial charge in van der Waals surface area (PEOEVSA7) of the molecules is obtained for SERT inhibition with the significant statistics of R2= 0.69, RMSEtr= 0.44, R2 ext= 0.62 and CCCext= 0.78. For 5-HT1A agonist, the two descriptor model of molecular shape (Kappm3) and van der Waals volume of the atoms (bcutv11) with R2= 0.78, RMSEtr= 0.33, R2 ext = 0.83, and CCCext= 0.87 is established. The three descriptor model of information content (IC3), solubility (bcutp9) and electronegativity (GATSe5) of the molecules with R2= 0.61, RMSEtr= 0.34, R2 ext= 0.69 and CCCext= 0.72 is obtained for 5-HT3A antagonist. The antidepressant activities of 16 virtual screened compounds were predicted using the developed models.

Conclusion: The developed QSAR models may be useful to predict antidepressant activity for the newly synthesized vortioxetine analogs.

Keywords: Antidepressant, vortioxetine analogs, QSAR, virtual screening, SERT, 5-HT1A, 5-HT3A.

Graphical Abstract
Hoyer, D.; Hannon, J.P.; Martin, G.R. Molecular, pharmacological and functional diversity of 5-HT receptors. Pharmacol. Biochem. Behav., 2002, 71(4), 533-554.
Woolley, D.W.; Shaw, E. A biochemical and pharmacological suggestion about certain mental disorders. Proc. Natl. Acad. Sci. USA, 1954, 40(4), 228-231.
Launay, J.M.; Schneider, B.; Loric, S.; Da Prada, M.; Kellermann, O. Serotonin transport and serotonin transporter-mediated antidepressant recognition are controlled by 5-HT2B receptor signaling in serotonergic neuronal cells. FASEB J., 2006, 20(11), 1843-1854.
Moltzen, E.K.; Bang-Andersen, B. Serotonin reuptake inhibitors: The corner stone in treatment of depression for half a century - a medicinal chemistry survey. Curr. Top. Med. Chem., 2006, 6(17), 1801-1823.
McGoey, L. Profitable failure: Antidepressant drugs and the triumph of flawed experiments. Hist. Human Sci., 2010, 23(1), 58-78.
Artigas, F.; Bortolozzi, A.; Celada, P. Can we increase speed and efficacy of antidepressant treatments? Part I: General aspects and monoamine-based strategies. Eur. Neuropsychopharmacol., 2017.
Stahl, S.M.; Lee-Zimmerman, C.; Cartwright, S.; Morrissette, D.A. Serotonergic drugs for depression and beyond. Curr. Drug Targets, 2013, 14(5), 578-585.
Blier, P.; Bergeron, R.; de Montigny, C. Selective activation of postsynaptic 5-HT1A receptors induces rapid antidepressant response. Neuropsychopharmacology, 1997, 16(5), 333-338.
Blier, P.; Ward, N.M. Is there a role for 5-HT1A agonists in the treatment of depression? Biol. Psychiatry, 2003, 53(3), 193-203.
Morales, M.; Battenberg, E.; de Lecea, L.; Bloom, F.E. The type 3 serotonin receptor is expressed in a subpopulation of GABAergic neurons in the rat neocortex and hippocampus. Brain Res., 1996, 731(1-2), 199-202.
Yan, Z. Regulation of GABAergic inhibition by serotonin signaling in prefrontal cortex: Molecular mechanisms and functional implications. Mol. Neurobiol., 2002, 26(2-3), 203-216.
Adell, A. Lu-AA21004, a multimodal serotonergic agent, for the potential treatment of depression and anxiety. IDrugs, 2010, 13(12), 900-910.
Connolly, K.R.; Thase, M.E. Vortioxetine: A new treatment for major depressive disorder. Expert Opin. Pharmacother., 2016, 17(3), 412-431.
Tritschler, L.; Felice, D.; Colle, R.; Guilloux, J.P.; Corruble, E.; Gardier, A.M.; David, D.J. Vortioxetine for the treatment of major depressive disorder. Expert Rev. Clin. Pharmacol., 2014, 49(12), 781-790.
McIntyre, R.S.; Harrison, J.; Loft, H.; Jacobson, W.; Olsen, C.K. The effects of vortioxetine on cognitive function in patients with major depressive disorder: A meta-analysis of three randomized controlled trials. Int. J. Neuropsychopharmacol., 2016, 19(10), 1-9.
Bang-Andersen, B.; Ruhland, T.; Jørgensen, M.; Smith, G.; Frederiksen, K.; Jensen, K.G.; Zhong, H.; Nielsen, S.M.; Hogg, S.; Mørk, A.; Stensbøl, T.B. Discovery of 1-[2-(2,4-dimethylphenylsulfanyl)phenyl]piperazine (Lu AA21004): A novel multimodal compound for the treatment of major depressive disorder. J. Med. Chem., 2011, 54(9), 3206-3221.
Hansch, C. Quantitative approach to biochemical structure-activity relationships. Acc. Chem. Res., 1969, 2(8), 232-239.
Dong, J.; Cao, D.S.; Miao, H.Y.; Liu, S.; Deng, B.C.; Yun, Y.H.; Wang, N.N.; Lu, A.P.; Zeng, W.B.; Chen, A.F. ChemDes: An integrated web-based platform for molecular descriptor and fingerprint computation. J. Cheminform., 2015, 7, 60.
Tibshirani, R. Regression shrinkage and regression via LASSO. J.R. Statist. Soc., (B). 1996, 58(5), 267-288.
Gramatica, P.; Chirico, N.; Papa, E.; Cassani, S.; Kovarich, S. QSARINS: A new software for the development, analysis, and validation of QSAR MLR Models. J. Comput. Chem. Soft. News and Updates, 2013, 34, 2121-2132.
Bro, R.; Kjeldahl, K.; Smilde, A.K.; Kiers, H.A. Cross-validation of component models: A critical look at current methods. Anal. Bioanal. Chem., 2008, 390(5), 1241-1251.
Filzmoser, P.; Liebmann, B.; Varmuza, K. Repeated double cross validation. J. Chem., 2009, 23(4), 160-171.
Shi, L.M.; Fang, H.; Tong, W.; Wu, J.; Perkins, R.; Blair, R.M.; Branham, W.S.; Dial, S.L.; Moland, C.L.; Sheehan, S.M. QSAR models using a large diverse set of estrogens. J. Chem. Inf. Comput. Sci., 2001, 41(1), 186-195.
Schüürmann, G.; Ebert, R.; Chen, J.; Wang, B.; Kühne, R. External validation and prediction employing the predictive squared correlation coefficients test set activity mean vs training set activity mean. J. Chem. Inf. Model., 2008, 48(11), 2140-2145.
Consonni, V.; Ballabio, D.; Todeschini, R. Comments on definition of Q2 parameter for QSAR validation. J. Chem. Inf. Model., 2009, 49(7), 1669-1678.
Consonni, V.; Ballabio, D.; Todeschini, R. Evaluation of model predictive ability by external validation techniques. J. Chemometr., 2010, 24, 194-201.
Lin, L.I. A concordance correlation coefficient to evaluate reproducibility. Biometrics, 1989, 45(1), 255-268.
Lin, L.I. Assay validation using the concordance correlation coefficient. Biometrics, 1992, 48(2), 599-604.
QSARINS 2.2.2. www.qsar.it2017
Rácz, A.; Bajusz, D.; Héberger, K. Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters. SAR QSAR Environ. Res., 2015, 26(7-9), 683-700.
Erzincan, P.; Saçan, M.T.; Yüce-Dursun, B.; Danış, Ö.; Demir, S.; Erdem, S.S.; Ogan, A. QSAR models for antioxidant activity of new coumarin derivatives. SAR QSAR Environ. Res., 2015, 26(7-9), 721-737.
Gramatica, P. Principles of QSAR models validation: Internal and external. QSAR & Comb. Sci., 2007, 26(5), 694-701.
Gramatica, P.; Cassani, S.; Roy, P.P.; Kovarich, S.; Yap, C.W.; Papa, E. QSAR modeling is not “push a button and find a correlation”: A case study of toxicity of (Benzo-)triazoles of Algae. Mol. Inform., 2012, 31(11-12), 817-835.
To’th, G.; Bodai, Z.; He’berger, K. Estimation of influential points in any data set from coefficient of determination and its leave-one-out cross-validated counterpart. J. Comput. Aided Mol. Des., 2013, 27, 837-844.

Rights & Permissions Print Export Cite as
© 2023 Bentham Science Publishers | Privacy Policy