Virtual Screening in the Search of New and Potent Anti-Alzheimer Agents

Discovery of novel acetylcholinesterase inhibitors through integration of machine learning with genetic algorithm based in silico screening approaches

Introduction

Alzheimer's disease (AD) is the most studied progressive eurodegenerative disorder, affecting 40-50 million of the global population. This progressive neurodegenerative disease is marked by gradual and irreversible declines in cognitive functions. The unavailability of therapeutic drug candidates restricting/reversing the progression of this dementia has severed the existing challenge. The development of acetylcholinesterase (AChE) inhibitors retains a great research focus for the discovery of an anti-Alzheimer drug.

Materials and methods

This study focused on finding AChE inhibitors by applying the machine learning (ML) predictive modeling approach, which is an integral part of the current drug discovery process. In this study, we have extensively utilized ML and other in silico approaches to search for an effective lead molecule against AChE.

Result and discussion

The output of this study helped us to identify some promising AChE inhibitors. The selected compounds performed well at different levels of analysis and may provide a possible pathway for the future design of potent AChE inhibitors.

Computational Studies on Acetylcholinesterase Inhibitors: From Biochemistry to Chemistry

Acetylcholinesterase inhibitors are the most promising therapeutics for Alzheimer's disease treatment as these prevent the loss of acetylcholine and slows the progression of the disease. The drugs approved for the management of Alzheimer's disease by the FDA are acetylcholinesterase inhibitors but are associated with side effects. Consistent and stringent efforts by the researchers with the help of computational methods opened new ways of developing novel molecules with good acetylcholinesterase inhibitory activity. In this manuscript, we reviewed the studies that identified the essential structural features of acetylcholinesterase inhibitors at the molecular level as well as the techniques like molecular docking, molecular dynamics, quantitative structure-activity relationship, virtual screening, and pharmacophore modelling that were used in designing these inhibitors.

In silico Exploration of Bioactive Phytochemicals Against Neurodegenerative Diseases Via Inhibition of Cholinesterases

Neurodegenerative disorders are estimated to become the second leading cause of death worldwide by 2040. Despite the widespread use of diverse allopathic drugs, these brain-associated disorders can only be partially addressed and long term treatment is often linked with dependency and other unwanted side effects. Nature, believed to be an arsenal of remedies for any illness, presents an interesting avenue for the development of novel neuroprotective agents. Interestingly, inhibition of cholinesterases, involved in the breakdown of acetylcholine in the synaptic cleft, has been proposed to be neuroprotective. This review therefore aims to provide additional insight via docking studies of previously studied compounds that have shown potent activity against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) in vitro. Indeed, the determination of potent plant-based ligands for this purpose through in silico methods enables the elimination of lengthy and costly traditional methods of drug discovery. Herein, a literature search was conducted to identify active phytochemicals which are cholinesterase inhibitors. Following which in silico docking methods were applied to obtain docking scores. Compound structures were extracted from online ZINC database and optimized using AM1 implemented in gaussian09 software. Noteworthy ligands against AChE highlighted in this study include: 19,20-dihydroervahanine A and 19, 20-dihydrotabernamine. Regarding BChE inhibition, the best ligands were found to be 8-Clavandurylkaempferol, Na-methylepipachysamine D; ebeiedinone; and dictyophlebine. Thus, ligand optimization between such phytochemicals and cholinesterases coupled with in vitro, in vivo studies and randomized clinical trials can lead to the development of novel drugs against neurodegenerative disorders.

Molecular interaction of investigational ligands with human brain acetylcholinesterase

Alzheimer's disease, a neurodegenerative disorder continues to be an area of investigation by the international researchers' fraternity. Despite all the ongoing efforts, the effective set of promising cholinesterase inhibitors available in the market for patients' use is limited. Furthermore, the currently available drugs could provide only a palliative type of treatment instead of providing a complete cure or foolproof prevention. Hence, design/discovery of fresh drug molecules as acetylcholinesterase (AChE) inhibitors still remains an urgent requirement. The drug discovery platform, MCULE in the "structure-based virtual screening" (SBVS) mode was used for high throughput ligand screening of over five million structures targeted against the AChE catalytic site. A stepwise query was made for the SBVS input. The number of hits was narrowed down in consecutive succession via varied filtration criteria as AutoDock-Vina rankings, MCULE toxicity filtration, exclusion of ligands having less than four H-bond acceptors, filtration by ΔG cutoff, rule-of-five violation and SWISS ADME profiling. This was followed by holistic analysis of all the results, thereby leading to one promising ligand. The screened out drug molecule, MCULE-5872671137-0-1 exhibited a robust interaction with the AChE catalytic site involving 20 amino acid residues, an acceptable binding free energy of -10.2 kcal/mol in addition to a favorable SWISS ADME-profie showing no harmful effects on the human body. It can be carefully stated that the molecule, MCULE-5872671137-0-1, which is chemically (3S)-N-{4-[(4-chlorophenyl)sulfanyl]phenyl}-3-hydroxypyrrolidine-1-carboxamide could function as a significant "seed" ligand for future design of potent AChE inhibitors and/or novel neuro drugs built upon the seed-scaffold.

Novel hits for acetylcholinesterase inhibition derived by docking-based screening on ZINC database

The inhibition of the enzyme acetylcholinesterase (AChE) increases the levels of the neurotransmitter acetylcholine and symptomatically improves the affected cognitive function. In the present study, we searched for novel AChE inhibitors by docking-based virtual screening of the standard lead-like set of ZINC database containing more than 6 million small molecules using GOLD software. The top 10 best-scored hits were tested in vitro for AChE affinity, neurotoxicity, GIT and BBB permeability. The main pharmacokinetic parameters like volume of distribution, free fraction in plasma, total clearance, and half-life were predicted by previously derived models. Nine of the compounds bind to the enzyme with affinities from 0.517 to 0.735 µM, eight of them are non-toxic. All hits permeate GIT and BBB and bind extensively to plasma proteins. Most of them are low-clearance compounds. In total, seven of the 10 hits are promising for further lead optimisation. These are structures with ZINC IDs: 00220177, 44455618, 66142300, 71804814, 72065926, 96007907, and 97159977.