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, Deparment of Applied Sciences, National Institute of Technical Teachers Training and Research, Bhopal, Madhya Pradesh, A.P.J. Abdul Kalam Technical University, BIT- School of Pharmacy, Lucknow, Uttar Pradesh
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.
