In silico and in vivo neuropharmacological evaluation of two γ-amino acid isomers derived from 2,3-disubstituted benzofurans, as ligands of GluN1-GluN2A NMDA receptor

Benzofurans as Acetylcholinesterase Inhibitors for Treating Alzheimer's Disease: Synthesis, in vitro Testing, and in silico Analysis

Alzheimer's disease (AD) is a neurodegenerative disorder and the leading cause of dementia worldwide. It is characterized by a progressive decline in cholinergic neurotransmission. During the development of AD, acetylcholinesterase (AChE) binds to β-amyloid peptides to form amyloid fibrils, which aggregate into plaque deposits. Meanwhile, tau proteins are hyperphosphorylated, forming neurofibrillary tangles (NFTs) that aggregate into inclusions. These complexes are cytotoxic for the brain, causing impairment of memory, attention, and cognition. AChE inhibitors are the main treatment for AD, but their effect is only palliative. This study aimed to design and synthesize novel benzofuran derivatives and evaluate their inhibition of AChE in vitro and in silico. Results: The seven synthesized benzofuran derivatives inhibited AChE in vitro. Benzofurans hydroxy ester 4, amino ester 5, and amido ester (±)-7 had the lowest inhibition constant (Ki) values and displayed good affinity for EeAChE in molecular docking. Six derivatives showed competitive inhibition, while the best compound (5: Ki=36.53 μM) exhibited uncompetitive inhibition. The amino, hydroxyl, amide, and ester groups of the ligands favored interaction with the enzyme by hydrogen bonds. Conclusion: Three benzofurans were promising AChE inhibitors with excellent Ki values. In future research on their their application to AD, 5 will be considered as the base structure.

Discovery of GluN2A subtype-selective N-methyl-d-aspartate (NMDA) receptor ligands

The N-methyl-d-aspartate (NMDA) receptors, which belong to the ionotropic Glutamate receptors, constitute a family of ligand-gated ion channels. Within the various subtypes of NMDA receptors, the GluN1/2A subtype plays a significant role in central nervous system (CNS) disorders. The present article aims to provide a comprehensive review of ligands targeting GluN2A-containing NMDA receptors, encompassing negative allosteric modulators (NAMs), positive allosteric modulators (PAMs) and competitive antagonists. Moreover, the ligands' structure-activity relationships (SARs) and the binding models of representative ligands are also discussed, providing valuable insights for the clinical rational design of effective drugs targeting CNS diseases.

Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs

Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.