Evaluation of Amaryllidaceae alkaloids as inhibitors of human acetylcholinesterase by QSAR analysis and molecular docking

Metabolomics Unveils Disrupted Pathways in Parkinson's Disease: Toward Biomarker-Based Diagnosis

Parkinson's disease (PD) is a neurodegenerative disorder characterized by diverse symptoms, where accurate diagnosis remains challenging. Traditional clinical observation methods often result in misdiagnosis, highlighting the need for biomarker-based diagnostic approaches. This study utilizes ultraperformance liquid chromatography coupled to an electrospray ionization source and quadrupole time-of-flight untargeted metabolomics combined with biochemometrics to identify novel serum biomarkers for PD. Analyzing a Brazilian cohort of serum samples from 39 PD patients and 15 healthy controls, we identified 15 metabolites significantly associated with PD, with 11 reported as potential biomarkers for the first time. Key disrupted metabolic pathways include caffeine metabolism, arachidonic acid metabolism, and primary bile acid biosynthesis. Our machine learning model demonstrated high accuracy, with the Rotation Forest boosting model achieving 94.1% accuracy in distinguishing PD patients from controls. It is based on three new PD biomarkers (downregulated: 1-lyso-2-arachidonoyl-phosphatidate and hypoxanthine and upregulated: ferulic acid) and surpasses the general 80% diagnostic accuracy obtained from initial clinical evaluations conducted by specialists. Besides, this machine learning model based on a decision tree allowed for visual and easy interpretability of affected metabolites in PD patients. These findings could improve the detection and monitoring of PD, paving the way for more precise diagnostics and therapeutic interventions. Our research emphasizes the critical role of metabolomics and machine learning in advancing our understanding of the chemical profile of neurodegenerative diseases.

Analysis of Marrubiin in Marrubium alysson L. Extract Using Advanced HPTLC: Chemical Profiling, Acetylcholinesterase Inhibitory Activity, and Molecular Docking

The main purpose of this work is to investigate the phytochemical composition of Marrubium alysson L. non-polar fraction. GC/MS analysis was used to evaluate the plant extract's saponifiable and unsaponifiable matter. Although M. alysson L. lipoidal matter saponification produced 30.3% of fatty acid methyl esters and 69.7% of unsaponifiable matter. Phytol was the most dominant substance in the unsaponifiable materials. Notably, marrubiin which is one of the most prominent metabolites of Marrubium alysson L. was not detected through our adopted GC/MS technique. Thus, further characterization was proceeded through simple and rapid HPTLC analysis which successfully managed to identify marrubiin. Based on the regression equation, the concentration of marrubiin in M. alysson L. extract was 14.09 mg/g of dry extract. Concerning acetylcholinesterase inhibitory activity, both the crude M. alysson L. total methanolic extract and the non-polar fraction displayed reasonable inhibitory activity against acetylcholinesterase (AChE), whereas the pure compound marrubiin was considered to be the most effective and potent AChE inhibitor, with an IC50 value of 52.66 (µM). According to the molecular docking studies, potential sites of interaction between the pure chemical marrubiin and AChE were examined. The results show that Tyr124 on AChE residue was critical to the activity of the aforementioned drug. Based on the depicted marrubin AChE inhibition activity and reported safety profile, this chemical metabolite is considered as a promising lead compound for further pre-clinical investigation as well as drug development and optimization.

New trends in the practical use of isoquinoline alkaloids as potential drugs applicated in infectious and non-infectious diseases

In the last years, traditional natural products have been the center of attention for the scientific community and exploration of their therapeutic abilities is proceeding permanently. Isoquinoline alkaloids have always attracted scientific interest due to either their positive or negative effects on human organism. The present review describes research on isoquinoline alkaloids isolated from different plant species. Alkaloids are one of the most important classes of plant derived compounds among these isoquinoline alkaloids possess varied biological activities such as anticancer, antineurodegenerative diseases, antidiabetic, antiinflammatory, antimicrobial, and many others. The use of plants against different disorders is entrenched in traditional medicine around the globe. Recent progress in modern therapeutics has stimulated the use of natural products worldwide for various ailments and diseases. The review provides a collection of information on the capabilities of some isoquinoline alkaloids, its potential for the treatment of various diseases and is designed to be a guide for future research on different biologically active isoquinoline alkaloids and plant species containing them. The authors are aware that they were not able to cover the whole area of the topic related to biological activity of isoquinoline alkaloids. This review is intended to suggest directions for further research and can also help other researchers in future studies.

Development of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer's Disease

Multi-target drug development has become an attractive strategy in the discovery of drugs to treat of Alzheimer's disease (AzD). In this study, for the first time, a rule-based machine learning (ML) approach with classification trees (CT) was applied for the rational design of novel dual-target acetylcholinesterase (AChE) and β-site amyloid-protein precursor cleaving enzyme 1 (BACE1) inhibitors. Updated data from 3524 compounds with AChE and BACE1 measurements were curated from the ChEMBL database. The best global accuracies of training/external validation for AChE and BACE1 were 0.85/0.80 and 0.83/0.81, respectively. The rules were then applied to screen dual inhibitors from the original databases. Based on the best rules obtained from each classification tree, a set of potential AChE and BACE1 inhibitors were identified, and active fragments were extracted using Murcko-type decomposition analysis. More than 250 novel inhibitors were designed in silico based on active fragments and predicted AChE and BACE1 inhibitory activity using consensus QSAR models and docking validations. The rule-based and ML approach applied in this study may be useful for the in silico design and screening of new AChE and BACE1 dual inhibitors against AzD.

Determination of Anti-Alzheimer's Disease Activity of Selected Plant Ingredients

Neurodegenerative diseases, among which one of the more common is Alzheimer’s disease, are the one of the biggest global public health challenges facing our generation because of the increasing elderly population in most countries. With the growing burden of these diseases, it is essential to discover and develop new treatment options capable of preventing and treating them. Neurodegenerative diseases, among which one of the most common is Alzheimer’s disease, are a multifactorial disease and therefore demand multiple therapeutic approaches. One of the most important therapeutic strategies is controlling the level of acetylcholine—a neurotransmitter in cholinergic synapses—by blocking the degradation of acetylcholine using acetylcholinesterase inhibitors such as tacrine, galantamine, donepezil and rivastigmine. However, these drugs can cause some adverse side effects, such as hepatotoxicity and gastrointestinal disorder. Thus, the search for new, more effective drugs is very important. In the last few years, different active constituents from plants have been tested as potential drugs in neurodegenerative disease therapy. The availability, lower price and less toxic effects of herbal medicines compared with synthetic agents make them a simple and excellent choice in the treatment of neurodegenerative diseases. The empirical approach to discovering new drugs from the systematic screening of plant extracts or plant-derived compounds is still an important strategy when it comes to finding new biologically active substances. The aim of this review is to identify new, safe and effective compounds that are potential candidates for further in vivo and clinical tests from which more effective drugs for the treatment of Alzheimer’s disease could be selected. We reviewed the methods used to determine anti-Alzheimer’s disease activity. Here, we have discussed the relevance of plant-derived compounds with in vitro activity. Various plants and phytochemical compounds have shown different activity that could be beneficial in the treatment of Alzheimer’s disorders. Most often, medicinal plants and their active components have been investigated as acetylcholinesterase and/or butyrylcholinesterase activity inhibitors, modifiers of β-amyloid processing and antioxidant agents. This study also aims to highlight species with assessed efficacy, usable plant parts and the most active plant components in order to identify species and compounds of interest for further study. Future research directions are suggested and recommendations made to expand the use of medicinal plants, their formulations and plant-derived active compounds to prevent, mitigate and treat Alzheimer’s disease.

Alkaloid Profiling and Cholinesterase Inhibitory Potential of Crinum × amabile Donn. (Amaryllidaceae) Collected in Ecuador

Natural products are one of the main sources for developing new drugs. The alkaloids obtained from the plant family Amaryllidaceae have interesting structures and biological activities, such as acetylcholinesterase inhibition potential, which is one of the mechanisms used for the palliative treatment of Alzheimer's disease symptoms. Herein we report the alkaloidal profile of bulbs and leaves extracts of Crinum × amabile collected in Ecuador and their in vitro inhibitory activity on acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) enzymes. Using Gas Chromatography coupled to Mass Spectrometry (GC-MS), we identified 12 Amaryllidaceae alkaloids out of 19 compounds detected in this species. The extracts from bulbs and leaves showed great inhibitory activity against AChE and BuChE, highlighting the potential of Amaryllidaceae family in the search of bioactive molecules.

Ranking-Oriented Quantitative Structure-Activity Relationship Modeling Combined with Assay-Wise Data Integration

In ligand-based drug design, quantitative structure-activity relationship (QSAR) models play an important role in activity prediction. One of the major end points of QSAR models is half-maximal inhibitory concentration (IC₅₀). Experimental IC₅₀ data from various research groups have been accumulated in publicly accessible databases, providing an opportunity for us to use such data in predictive QSAR models. In this study, we focused on using a ranking-oriented QSAR model as a predictive model because relative potency strength within the same assay is solid information that is not based on any mechanical assumptions. We conducted rigorous validation using the ChEMBL database and previously reported data sets. Ranking support vector machine (ranking-SVM) models trained on compounds from similar assays were as good as support vector regression (SVR) with the Tanimoto kernel trained on compounds from all the assays. As effective ways of data integration, for ranking-SVM, integrated compounds should be selected from only similar assays in terms of compounds. For SVR with the Tanimoto kernel, entire compounds from different assays can be incorporated.

Homology modeling, docking, molecular dynamics and in vitro studies to identify Rhipicephalus microplus acetylcholinesterase inhibitors

Rhipicephalus microplus is an important ectoparasite of cattle, causing considerable economical losses. Resistance to chemical acaricides has stimulated the search for new antiparasitic drugs, including natural products as an eco-friendly alternative of control. Flavonoids represent a class of natural compounds with many biological activities, such as enzyme inhibitors. Acetylcholinesterase is an essential enzyme for tick survival that stands out as an important target for the development of acaricides. This work aimed to predict this 3D structure by homology modeling and use the model to identify compound with inhibitory activity. The model of R. microplus AChE1 (RmAChE1) was constructed using MODELLER program. The optimization and molecular dynamic investigation were performed in GROMACS program. The model developed was used, by molecular docking, to evaluate the anticholinesterase activity of flavonoids (quercetin, rutin, diosmin, naringin and hesperidin) and an acaricide synthetic (eserine). Additionally, in vitro inhibition of AChE and larval immersion tests were performed. The model of RmAChE1 showed to be sterically and energetically acceptable. In molecular dynamics simulations, the 3D structure remains stable with Root Mean Square Deviation = 3.58 Å and Root Mean Square Fluctuation = 1.43 Å. In molecular docking analyses, only eserine and quercetin show affinity energy to the RmAChE (Gridscore: -52.17 and -39.44 kcal/mol, respectively). Among the flavonoids, quercetin exhibited the best in vitro inhibition of AChE activity (15.8%) and mortality of larvae tick (30.2%). The use of in silico and in vitro techniques has shown that quercetin showed promising anti-tick activity and structural requirements to interact with RmAChE1. Communicated by Ramaswamy H. Sarma.