A comparative QSAR analysis and molecular docking studies of phenyl piperidine derivatives as potent dual NK1R antagonists/serotonin transporter (SERT) inhibitors. (April 2017)

• Record Type: Journal Article
• Authors: Zare, Somayeh; Fereidoonnezhad, Masood; Afshar, Davoud; Ramezani, Zahra
• Is Part Of: Computational biology and chemistry. Volume 67(2017)
• Page Start: 22
• Page End: 37
• Publication Date: 2017-04
• Subjects: QSAR -- Molecular docking -- Neurokinin 1 receptor antagonists -- Serotonin transporter (SERT) inhibitors -- in silico-screening; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2016.12.004
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: A collection of chemometrics methods such as MLR, FA-MLR, PCR, GA-PLS to make relations between structural characteristics and NK1 R antagonism/SERT inhibitory of novel phenyl piperidine derivatives. An in silico -screening study to introduce new potent lead compounds based on new structural patterns. Molecular docking studies of these compounds on both NK1 R/serotonin transporter (SERT) targets. Validated docking protocols on both targets. Abstract: Depression is a critical mood disorder that affects millions of patients. Available therapeutic antidepressant agents are associated with several undesirable side effects. Recently, it has been shown that Neurokinin 1 receptor (NK1 R) antagonists can potentiate the antidepressant effects of serotonin-selective reuptake inhibitors (SSRIs). In this study, a series of phenyl piperidine derivatives as potent dual NK1 R antagonists/serotonin transporter (SERT) inhibitors were applied to quantitative structure–activity relationship (QSAR) analysis. A collection of chemometrics methods such as multiple linear regression (MLR), factor analysis–based multiple linear regression (FA-MLR), principal component regression (PCR), and partial least squared combined with genetic algorithm for variable selection (GA-PLS) were applied to make relations between structural characteristics and NK1 R antagonism/SERT inhibitory of these compounds. The best multiple linear regression equation was obtained from GA-PLS and MLR for NK1 R and SERT, respectively. Based on the resulted model, an in silico -screening study was also conducted and new potent lead compounds based on new structural patterns were designed for both targets. Molecular docking studies of these compounds on both targets were also conducted and encouraging results were acquired. There was a good correlation between QSAR and docking results. The results obtained from validated docking studies indicate that the important amino acids inside the active site of the cavity that are responsible for essential interactions are Glu33, Asp395 and Arg26 for SERT and Ala30, Lys7, Asp31, Phe5 and Tyr82 for NK1 R receptors.