New Multifunctional Agents for Potential Alzheimer's Disease Treatment Based on Tacrine Conjugates with 2-Arylhydrazinylidene-1,3-Diketones

New insights into butyrylcholinesterase: Pharmaceutical applications, selective inhibitors and multitarget-directed ligands

Butyrylcholinesterase (BChE), also known as pseudocholinesterase and serum cholinesterase, is an isoenzyme of acetylcholinesterase (AChE). It mediates the degradation of acetylcholine, especially under pathological conditions. Proverbial pharmacological applications of BChE, its mutants and modulators consist of combating Alzheimer's disease (AD), influencing multiple sclerosis (MS), addressing cocaine addiction, detoxifying organophosphorus poisoning and reflecting the progression or prognosis of some diseases. Of interest, recent reports have shed light on the relationship between BChE and lipid metabolism. It has also been proved that BChE is going to increase abnormally as a compensator for AChE in the middle and late stages of AD, and BChE inhibitors can alleviate cognitive disorders and positively influence some pathological features in AD model animals, foreboding favorable prospects and potential applications. Herein, the selective BChE inhibitors and BChE-related multitarget-directed ligands published in the last three years were briefly summarized, along with the currently known pharmacological applications of BChE, aiming to grasp the latest research directions. Thereinto, some emerging strategies for designing BChE inhibitors are intriguing, and the modulators based on target combination of histone deacetylase and BChE against AD is unprecedented. Furthermore, the involvement of BChE in the hydrolysis of ghrelin, the inhibition of low-density lipoprotein (LDL) uptake, and the down-regulation of LDL receptor (LDLR) expression suggests its potential to influence lipid metabolism disorders. This compelling prospect likely stimulates further exploration in this promising research direction.

Design, synthesis, and evaluation of 2,2'-bipyridyl derivatives as bifunctional agents against Alzheimer's disease

Alzheimer's disease (AD) is a complex multifactorial neurodegenerative disease. Metal ion dyshomeostasis and Aβ aggregation have been proposed to contribute to AD progression. Metal ions can bind to Aβ and promote Aβ aggregation, and ultimately lead to neuronal death. Bifunctional (metal chelation and Aβ interaction) compounds are showing promise against AD. In this work, eleven new 3,3'-diamino-2,2'-bipyridine derivatives 4a-4k were synthesized, and evaluated as bifunctional agents for AD treatment. In vitro Aβ aggregation inhibition assay confirmed that most of the synthesized compounds exhibited significant self-induced Aβ1-42 aggregation inhibition. Among them, compound 4d displayed the best inhibitory potency of self-induced Aβ1-42 aggregation with IC50 value of 9.4 µM, and it could selectively chelate with Cu2+ and exhibited 66.2% inhibition of Cu2+-induced Aβ1-42 aggregation. Meanwhile, compound 4d showed strong neuroprotective activity against Aβ1-42 and Cu2+-treated Aβ1-42 induced cell damage. Moreover, compound 4d in high dose significantly reversed Aβ-induced memory impairment in mice.

A comprehensive review of multi-target directed ligands in the treatment of Alzheimer's disease

Alzheimer's disease (AD) is the most common form of dementia affecting specifically older population. AD is an irreversible neurodegenerative CNS disorder associated with complex pathophysiology. Presently, the USFDA has approved only four drugs viz. Donepezil, Rivastigmine, Memantine, and Galantamine for the treatment of AD. These drugs exhibit their neuroprotective effects either by inhibiting cholinesterase enzyme (ChE) or N-methyl-d-aspartate (NMDA) receptor. However, the conventional therapy "one target, one molecule" has failed to provide promising therapeutic effects due to the multifactorial nature of AD. This triggered the development of a novel strategy called Multi-Target Directed Ligand (MTDL) which involved designing one molecule that acts on multiple targets simultaneously. The present review discusses the detailed pathology involved in AD and the various MTDL design strategies bearing different heterocycles, in vitro and in vivo activities of the compounds, and their corresponding structure-activity relationships. This knowledge will allow us to identify and design more effective MTDLs for the treatment of AD.

Preparation and characterization of Allium cepa extract coated biochar and adsorption performance for hexavalent chromium

The elimination of hazardous metal ions from contaminated water has been an important procedure to improve the quality of the water source. Hence, this study presents the fabrication of Allium cepa extract-coated biochar for the elimination of Cr (VI) from wastewater. The synthesized biochar (SBCH) and modified biochar (BMOJ) were characterized by making use of FTIR, BET, XRD, TGA and SEM. Optimum Cr (VI) removal was achieved at solution pH 2, 0.05 g adsorbent dosage and 180 min agitation period. The adsorptive removal of Cr (VI) onto SBCH and BMOJ followed the pseudo-second-order kinetic model with a satisfactory sum of square residuals (SSR) of 3.874 and 5.245 for SBCH and BMOJ, respectively. Meanwhile, Freundlich isotherm was found to best describe the uptake of Cr (VI) SBCH and BMOJ. Experimental data showed an adsorption capacity of 37.38 and 25.77 mg g-1 and a maximum efficiency of 85.42% and 51.63% for BMOJ and SBCH, respectively. BMOJ also showed good antioxidant characteristics. Thermodynamic data revealed that the uptake of Cr (VI) onto the SBCH and BMOJ was an exothermic and endothermic (ΔH: SBCH =  - 16.22 kJ mol-1 and BMOJ = 13.74 kJ mol-1), entropy-driven (ΔS: SBCH = 40.96 J K-1 mol-1 and BMOJ = 93.26 J K-1 mol-1) and spontaneous process. Furthermore, BMOJ demonstrated excellent reusability and promising characteristics for industrial applications.

Machine Learning Prediction of Mycobacterial Cell Wall Permeability of Drugs and Drug-like Compounds

The cell wall of Mycobacterium tuberculosis and related organisms has a very complex and unusual organization that makes it much less permeable to nutrients and antibiotics, leading to the low activity of many potential antimycobacterial drugs against whole-cell mycobacteria compared to their isolated molecular biotargets. The ability to predict and optimize the cell wall permeability could greatly enhance the development of novel antitubercular agents. Using an extensive structure-permeability dataset for organic compounds derived from published experimental big data (5371 compounds including 2671 penetrating and 2700 non-penetrating compounds), we have created a predictive classification model based on fragmental descriptors and an artificial neural network of a novel architecture that provides better accuracy (cross-validated balanced accuracy 0.768, sensitivity 0.768, specificity 0.769, area under ROC curve 0.911) and applicability domain compared with the previously published results.