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

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

Volume: 15 Issue: 1

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

Affiliation:

• Department of Pharmacy, Lala Lajpat Rai Memorial Medical College, Meerut, Uttar Pradesh,India

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

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.

Cite this article as:

Masand Neeraj *, Gupta P. Satya and Khosa Lal Ratan , Designing of Selective γ-Secretase Inhibitory Benzenesulfonamides through Comparative In Vitro and In Silico Analysis, Current Drug Discovery Technologies 2018; 15 (1) . https://dx.doi.org/10.2174/1570163814666170713103440