ACHE x BCHE x MAO-B

A series of rationally designed benzothiazole-derived thioacetamides was synthesized and investigated for monoamine oxidases (MAO-A and MAO-B) and cholinesterases (AChE and BChE) inhibition properties. The tested compounds 18-31 inhibited MAO-A and MAO-B in the micromolar to nanomolar range and AChE in the submicromolar range. Compound 28 was identified as the most potent MAO-A inhibitor with an IC₅₀ = 0.030 ± 0.008 µM, whereas compound 30 showed the highest potency towards MAO-B and AChE with IC₅₀ values of 0.015 ± 0.007 µM and 0.114 ± 0.003 µM, respectively. Further, compound 30 inhibited BChE at an IC₅₀ value of 4.125 ± 0.143 µM. Among all screened molecules, compound 30 emerged as the lead dual MAO-B and AChE inhibitor that blocked these enzymes in a competitive-reversible and mixed-reversible mode, respectively. Selected compounds have displayed iron-chelation and antioxidant properties. Further, computational assessment of ligand binding affinity and pharmacokinetic parameters of all new compounds and molecular dynamic simulation of compound 30 with MAO-B and AChE were carried out to understand ligand efficiency, pharmacokinetic, and virtual molecular interaction profile, respectively. The in silico ADMET prediction studies revealed a few undesired pharmacokinetic attributes of our compounds. The attempted virtual lead-based library synthesis and subsequent biological investigation produced a new benzothiazole-bearing dual MAO-B and AChE inhibitor as a prospective MTDL candidate for treating neurological disorders.

Multi-target directed ligands (MTDLs) can be an effective strategy for the treatment of multifactorial diseases like AD. The successful design of MTDLs has been achieved through the integration of multiple pharmacophores within a single molecule. In this context, innovative cinnamic acid MTDLs derived from natural products have been synthesized, targeting cholinesterases (ChEs) and monoamine oxidases (MAOs) as potential inhibitors. They were evaluated in vitro for the inhibition of AChE, BuChE, MAO-A and MAO-B. Most of the hybrids demonstrated promising and selective inhibition of AChE. Hybrid 16 demonstrated the highest inhibitory potency against AChE, BChE, MAO-A, and MAO-B, exhibiting IC₅₀ values of 4.59, 13.24, 30.78 and 32.02 μM, respectively. Furthermore, hybrid 16 demonstrated notable antioxidant activity, indicated by an IC₅₀ value of 12.49 μM. The proposed mechanism of action and the binding modes of hybrid 16 were analyzed through molecular docking studies. In silico predictions of physicochemical parameters suggest that these hybrids would remain active following oral administration and possess the capability to penetrate brain tissue. Hybrid 16 was stable within simulated gastric and intestinal contexts demonstrating its potential for absorption into the bloodstream without significant degradation. As a result, these findings enhance the prospects of hybrid 16 as a multi-target therapeutic agent for AD.

Alzheimer';s disease (AD) is a neurodegenerative disorder marked by a decline in cognitive function and memory loss, primarily resulting from cholinergic dysfunction, the accumulation of amyloid plaques, the formation of tau tangles, and the progressive degeneration of neurons. While existing treatments offer limited symptomatic relief, they do not effectively halt or reverse the underlying progression of the disease, presenting a major global challenge in Alzheimer's research. Developing therapeutic strategies for AD remains complex, largely due to the inability of current medications to significantly slow neurodegeneration. Traditional drug discovery processes are often lengthy, costly, and inefficient, further complicating the search for effective treatments. To overcome these obstacles, researchers have turned to a combination of computational approaches alongside conventional drug design techniques. These integrated methodologies help accelerate the discovery process by significantly reducing both time and costs. This review delves into the underlying physiological and pathological mechanisms of Alzheimer';s disease, while identifying potential drug targets such as acetylcholinesterase, butyrylcholinesterase, β-Secretase (BACE-1), A2A adenosine receptor, Dickkopf-1 protein, glycogen synthase kinase-3β, indoleamine 2,3-dioxygenase, monoamine oxidase-B, NMDA receptor, Wnt inhibitory factor, cyclindependent kinase-5, glutaminyl cyclase, and cathepsin-B. Furthermore, the review examines various computer-aided drug design (CADD) methodologies, including structure-based and ligandbased approaches, virtual screening, pharmacophore modeling, molecular modelling, and simulation techniques. These computational strategies are playing an increasingly important role in Alzheimer's research, particularly in drug discovery. By investigating promising drug candidates and lead molecules that target key proteins involved in Alzheimer's pathogenesis, the review highlights their binding modes with these targets and assesses the chemical properties essential for the development of effective clinical candidates. The aim is to provide researchers with critical insights and tools to design novel compounds with the necessary chemical and physical characteristics required for the successful treatment of Alzheimer's disease.