Combination of Donepezil and Memantine Attenuated Cognitive Impairment Induced by Mixed Endocrine-Disrupting Chemicals: an In Silico Study

In silico exploration of promising heterocyclic molecules against both acetylcholinesterase and butyrylcholinesterase enzymes

We aimed to further explore the relationship between heterocyclic molecules and their associated biological activities for acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzymes. A dataset of 36 heterocycles was used to predict the activity of AChE and BChE inhibitors (the pIC50 values ranged from 7.84 to 12.49). A quantitative structure-activity relationship (QSAR) study was generated with the help of four different models (BMA, MNLR, MLR, and ANN). Four of the models were statistically acceptable based on both internal and external validation. The descriptors used in the models were similar to the X-ray structures of the target-ligand complexes, which made it possible to predict the pIC50 for AChE and BChE enzymes. Five selected molecules (compounds 6 (C21H21F3N4O), compound 7 (C22H23F3N4O), and compound 8 (C22H23F3N4O2) belong to the oxadiazole derivative group; compound 16 (C17H13ClN2O3) is classified into the chemical structures of different N, O, and S-based heterocycle groups; and compound 25 (C19H17NO2) pertains to the pyrimidine derivative group) possessed high pIC50 values for AChE and BChE enzymes (pIC50 values for AChE and BChE ranged from 9.01 to 10.32). The range of docking scores between the AChE and BChE receptors and their respective candidates was from -8.1 to -9.2 kcal/mol. The pharmacokinetics, biological activities, and physicochemical properties of five selected compounds supported their ability to protect against AD because they are not toxic, have a cholinergic effect, can cross the blood-brain barrier, and are well absorbed by the gastrointestinal tract.Communicated by Ramaswamy H. Sarma.

The effects of different acetylcholinesterase inhibitors on EEG patterns in patients with Alzheimer's disease: A systematic review

OBJECTIVE

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common type of dementia. The early diagnosis of AD is an important factor for the control of AD progression. Electroencephalography (EEG) can be used for early diagnosis of AD. Acetylcholinesterase inhibitors (AChEIs) are also used for the amelioration of AD symptoms. In this systematic review, we reviewed the effect of different AChEIs including donepezil, rivastigmine, tacrine, physostigmine, and galantamine on EEG patterns in patients with AD.

METHODS

PubMed electronic database was searched and 122 articles were found. After removal of unrelated articles, 24 articles were selected for the present study.

RESULTS

AChEIs can decrease beta, theta, and delta frequency bands in patients with AD. However, conflicting results were found for alpha band. Some studies have shown increased alpha frequency, while others have shown decreased alpha frequency following treatment with AChEIs. The only difference was the type of drug.

CONCLUSIONS

We found that studies reporting the decreased alpha frequency used donepezil and galantamine, while studies reporting the increased alpha frequency used rivastigmine and tacrine. It was suggested that future studies should focus on the effect of different AChEIs on EEG bands, especially alpha frequency in patients with AD, to compare their effects and find the reason for their different influence on EEG patterns. Also, differences between the effects of AChEIs on oligodendrocyte differentiation and myelination may be another important factor. This is the first article investigating the effect of different AChEIs on EEG patterns in patients with AD.

In silico identification of novel heterocyclic compounds combats Alzheimer's disease through inhibition of butyrylcholinesterase enzymatic activity

Increasing evidence indicates that heterocyclic molecules possess properties against butyrylcholinesterase (BChE) enzymatic activity, which is a potential therapeutic target for Alzheimer's disease (AD). Thus, this study aimed to further evaluate the relationship between heterocyclic molecules and their biological activities. A dataset of 38 selective and potent heterocyclic compounds (-log[the half‑maximal inhibitory concentration (pIC50)]) values ranging from 8.02 to 10.05) was applied to construct a quantitative structure-activity relationship (QSAR) study, including Bayesian model average (BMA), artificial neural network (ANN), multiple nonlinear regression (MNLR), and multiple linear regression (MLR) models. Four models met statistical acceptance in internal and external validation. The ANN model was superior to other models in predicting the pIC50 of the outcome. The descriptors put into the models were found to be comparable with the target-ligand complex X-ray structures, making these models interpretable. Three selected molecules possess drug-like properties (pIC50 values ranged from 9.19 to 9.54). The docking score between candidates and the BChE receptor (RCSB ID 6EYF) ranged from -8.4 to -9.0 kcal/mol. Remarkably, the pharmacokinetics, biological activities, molecular dynamics, and physicochemical properties of compound 18 (C20H22N4O, pIC50 value = 9.33, oxadiazole derivative group) support its protective effects on AD treatment due to its non-toxic nature, non-carcinogen, cholinergic nature, capability to penetrate the blood-brain barrier, and high gastrointestinal absorption.Communicated by Ramaswamy H. Sarma.

Identification of promising inhibitory heterocyclic compounds against acetylcholinesterase using QSAR, ADMET, biological activity, and molecular docking

Heterocyclic compounds exert diverse functions, especially acetylcholinesterase (AChE) inhibition. Thus, identifying the association between their detailed structures and biological activities is important to the development of novel medications for Alzheimer's disease (AD) treatment. In this study, diverse sets of 120 potent and selective heterocyclic compounds (-log[the half‑maximal inhibitory concentration] (pIC50) values ranged from 8.01 to 12.50) were used to develop quantitative structure-activity relationship (QSAR) models using multiple linear regression (MLR), multiple nonlinear regression (MNLR), Bayesian model average (BMA), and artificial neural network (ANN) models. The models' robustness and stability have been assessed using both internal and external methodology. ANN outperforms MLR, MNLR, and BMA according to external validation. The molecular descriptors incorporated into the model were in satisfactory correlation with the AChE receptor-ligand complex X-ray structures, making the model interpretable and predictive. Three selected compounds exert drug-like characteristics (pIC50 values ranged from 11.01 to 11.17). The binding affinity between the optimal compounds and the AChE receptor (RCSB ID 3LII) ranged from - 7.4 to - 8.8 kcal/mol. Remarkably, the pharmacokinetics, physicochemical properties, and biological activities of compound 25 (C23H32N2O2, PubChem CID 118727071, pIC50 value = 11.17) were found to be consistent with its therapeutic effects in AD due to its cholinergic and non-toxic nature, non-P-glycoprotein, high gastrointestinal absorption, and capability to penetrate the blood-brain barrier.

Prognostic biomarker prediction for glioma induced by heavy metals and their mixtures: An in-silico study

Although there is an association between heavy metals and glioma, the molecular mechanisms involved in glioma development remain unclear. Therefore, this study aimed to assess the molecular mechanisms implicated in glioma development induced by heavy metals and their mixtures using various methodologies and databases (CTD, Google Scholar, PubMed, ScienceDirect, SpringerLink, miRNAsong, GeneMANIA, Metascape, MIENTURNET, UALCAN). I found that heavy metals, particularly arsenic, mercury, lead, and cadmium, as well as their mixtures, have substantial influences on the etiology of gliomas. "glioblastoma signaling pathways," "integrated cancer pathway," "central carbon metabolism in cancer," "microRNAs in cancer," "p53 signaling pathway," "chemical carcinogenesis-DNA adducts," "glioma," "TP53 network," and "MAPK signaling pathway" were the predominant molecular pathways implicated in the glioma development induced by the studied heavy metals and their mixtures. Five genes (SOD1, CAT, GSTP1, PTGS2, TNF), two miRNAs (hsa-miR-26b-5p and hsa-miR-143-3p), and transcription factors (DR1 and HNF4) were identified as key components related to combined heavy metal and glioma development. Physical interactions were found to be the most common among the heavy metals and their mixtures studied (ranging from 45.2% to 77.6%). The expression level of SOD1 was significantly lower in glioblastoma multiforma samples compared to normal samples, whereas GSTP1 and TP53 expression levels were significantly higher. Brain lower and grade glioma patients who had higher levels of TP53, hsa-miR-25, hsa-miR-34, hsa-miR-222, and hsa-miR-143 had a reduced likelihood of survival. Our findings suggest that further priority should be given to investigating the impact of specific heavy metals or their mixtures on these molecular processes.