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Top1. Introduction
Alzheimer’s disease (AD) is a neurological disorder, characterized by degenerative changes in a variety of neurotransmitter systems (Wenk, 2003). The initial signs of the disease may be a constant decline in loss of short-term memory and intellectual functions, repeatedly accompanied by abnormal behavior such as aggression and depression (Wenk, 2003; Khachaturian, 1985). Various hypotheses have been proposed to explain AD pathogenesis such as cholinergic hypothesis, tau hypothesis and amyloid hypothesis, etc., that only describe the basic causes of disease progression (Roses, 1996). Among these hypotheses, the most prominent is the cholinergic hypothesis which states that AD is caused by a reduction of the activity of choline acetyltransferase in the cerebral cortex and hippocampus of brain area (Singh and Kaur et al., 2013 and Anand and Singh, 2013). The decreased level of the neurotransmitter causes loss of the cholinergic neurotransmission and large-scale aggregation of Aβ leading to the loss of intellectual abilities (Talesa, 2001). This hypothesis usually suggests that the cholinergic amplification will improve the perception in AD (Anand and Singh, 2013). Thus, the AChE has been proven to be the most promising therapeutic target for the symptomatic treatment of AD (Anand and Singh, 2013; Talesa, 2001). There are now five approved drugs for the treatment of cognitive symptoms of AD, four are AChE inhibitors (Tacrine, Rivastigmine, Galantamine and Donepezil) and one is non-competitive glutamate (NMDA) receptors antagonist (Memantine) (Schelterns et al., 2003). The benefit from their use is only symptomatic, and no medicine has been clearly shown to delay or halt the progress of the disease (Schelterns et al., 2003). Therefore, there is an urgent need to develop novel treatment strategies for the proper cure of AD. For this observation, in current study, we have performed 2D-QSAR, GQSAR and 3D pharmacophore modeling along with molecular docking studies to reveal the structural requirements for the AChE enzyme inhibitory activity.
In the current scenario, computational and chemoinformatic methods such as quantitative structure-activity relationships (QSARs) and molecular docking have demonstrated their great potential in designing leads for complex diseases. Among these methodologies, QSARs have been effectively used to identify the important structural features for selective biological activities. Currently, a number of different regression and pattern recognition techniques are available, which can be used for the selection of significant variables and QSAR model development. A number of computational studies have been reported (Brahmachari et al., 2015; Shen et al., 2007; de Souza et al., 2012; Goyal et al., 2014; Solomon et al., 2009; Karmakar et al., 2019; Gupta et al., 2011; Bernd et al., 2003; Saw et al., 2016; Paula et al., 2017; Planche et al., 2013; Planche et al., 2012; Francisco et al., 2012; Planche et al., 2012) so far for the designing of new agents against AD, but still we are far from finding a precise treatment strategy for AD. In the present study, we have employed a dataset of 78 (Sterling et al., 2002) structurally diverse carbamates derivatives with defined AChE enzyme inhibitory activity for the purpose QSAR model development in order to explore the key structural features that are essential for inhibitory activity against AChE enzyme. Prior to the development of final models, we have applied a multilayered variable selection approach to reduce noise in the input, and the final models was developed using the Partial Least Squares (PLS) regression technique. The QSAR models were built with the guidelines of the Organization for Economic cooperation and development (OECD) (Roy et al., 2015). The developed models have been validated taking into consideration various strict internal and external validation metrics. Moreover, we have performed pharmacophore modeling to unveil the structural requirements for the inhibitory activity and to categorize the compounds into more active and less active classes for their inhibitory potential against the AChE enzyme. Furthermore, we have implemented molecular docking studies with most active and least active compounds from the whole dataset and tried to justify the contributions of different descriptors/features as apparent in the QSAR/pharmacophore models.