Designing of Selective γ-Secretase Inhibitory Benzenesulfonamides through Comparative In Vitro and In Silico Analysis

Author(s): Neeraj Masand*, Satya P. Gupta, Ratan Lal Khosa

Journal Name: Current Drug Discovery Technologies

Volume 15 , Issue 1 , 2018


Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Abstract:

Background: In Alzheimer’s disease (AD), the gene mutations have been identified in the amyloid precursor protein (APP), the presenilin-1 (PS1) and -2 (PS2) genes. APP is a transmembrane protein which gets cleaved by α- and β- secretase enzymes and releases Aβ peptides which forms senile plaques in brain tissue. It contributes for local inflammatory response, subsequent oxidative stress, biochemical changes and neuronal death. Targeting the development of Aβ aggregates in the senile plaques is an important strategy in the treatment of AD. To facilitate the normal processing of APP, some of the reported approaches are stimulation of α- secretase activity or the modulation/inhibition of the β- and γ-secretase complex.

Methods: The mechanism of γ–secretase inhibition is targeted based on the QSAR and molecular docking methods. The series based on 3-chloro-2-hydroxymethylbenzenesulfonamide was selected for in silico ligand-based modeling. Significant correlations, between their γ-Secretase inhibitory profile and 2D-descriptors, were obtained through multiple linear regression (MLR) computational procedure.

Results: During QSAR nalysis, calculated molar refractivity (CMR) and surface tension (ST) were found to be contributing parameters along with halogen substituent at a particular position. Applicability analysis revealed that the suggested models have acceptable predictability (rpred 2 = 0.827).

Conclusion: The inferences drawn from MLR were utilized to prepare a data set of fourteen substituted benzenesulfonamides (N1-N14). The in silico studies provides strong impetus towards systematic application of such methods during lead identification and optimization.

Keywords: γ-Secretase Inhibitors, Alzheimer's disease, Multiple linear regression (MLR) analysis, 2Ddescriptors, QSAR study, Molecular docking.

Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 15
ISSUE: 1
Year: 2018
Published on: 04 April, 2018
Page: [65 - 77]
Pages: 13
DOI: 10.2174/1570163814666170713103440
Price: $65

Article Metrics

PDF: 50
HTML: 4
PRC: 2