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.