Encompassing receptor flexibility in virtual screening using ensemble docking-based hybrid QSAR: discovery of novel phytochemicals for BACE1 inhibition

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.

Phytotherapeutics Against Alzheimer's Disease: Mechanism, Molecular Targets and Challenges for Drug Development

Alzheimer's disease is inflating worldwide and is combatted by only a few approved drugs. At best, these drugs treat symptomatic conditions by targeting cholinesterase and N-methyl-D-aspartate receptors. Most of the clinical trials in progress are focused to develop disease-modifying agents that aim single targets. The 'one drug-one target' approach is failing in the case of Alzheimer's disease due to its labyrinth etiopathogenesis. Traditional medicinal systems like ayurveda uses a holistic approach encompassing legion of medicinal plants exhibiting multimodal activity. Recent advances in high-throughput technologies have catapulted the research in the arena of ayurveda, specifically in identifying plants with potent anti-Alzheimer's disease properties and their phytochemical characterization. Nonetheless, clinical trials of very few herbal medicines are in progress. This review is a compendium of Indian plants and ayurvedic medicines against Alzheimer's disease and their paraphernalia. A record of 230 plants that are found in India with anti-Alzheimer's disease potential and about 500 phytochemicals from medicinal plants has been solicited with the hope of exploring the unexplored. Further, the molecular targets of phytochemicals isolated from commonly used medicinal plants such as Acorus calamus, Bacopa monnieri, Convolvulus pluricaulis, Tinospora cordifolia and Withania somnifera have been reviewed with respect to their multidimensional property such as antioxidant, anti-inflammation, anti-aggregation, synaptic plasticity modulation, cognition and memory enhancing activity. In addition, the strengths, and challenges in ayurvedic medicine that limit its use as mainstream therapy is discussed and a framework for the development of herbal medicine has been proposed.

Alzheimer's Disease and β-Secretase Inhibition: An Update With a Focus on Computer-Aided Inhibitor Design

INTRODUCTION

Alzheimer's disease (AD) is an intensifying neurodegenerative illness due to its irreversible nature. Identification of β-site amyloid precursor protein (APP) cleaving enzyme1 (BACE1) has been a significant medicinal focus towards AD treatment, and this has opened ground for several investigations. Despite the numerous works in this direction, no BACE1 inhibitor has made it to the final approval stage as an anti-AD drug.

METHOD

We provide an introductory background of the subject with a general overview of the pathogenesis of AD. The review features BACE1 inhibitor design and development with a focus on some clinical trials and discontinued drugs. Using the topical keywords BACE1, inhibitor design, and computational/theoretical study in the Web of Science and Scopus database, we retrieved over 49 relevant articles. The search years are from 2010 and 2020, with analysis conducted from May 2020 to March 2021.

RESULTS AND DISCUSSION

Researchers have employed computational methodologies to unravel potential BACE1 inhibitors with a significant outcome. The most used computer-aided approach in BACE1 inhibitor design and binding/interaction studies are pharmacophore development, quantitative structure-activity relationship (QSAR), virtual screening, docking, and molecular dynamics (MD) simulations. These methods, plus more advanced ones including quantum mechanics/molecular mechanics (QM/MM) and QM, have proven substantial in the computational framework for BACE1 inhibitor design. Computational chemists have embraced the incorporation of in vitro assay to provide insight into the inhibition performance of identified molecules with potential inhibition towards BACE1. Significant IC₅₀ values up to 50 nM, better than clinical trial compounds, are available in the literature.

CONCLUSION

The continuous failure of potent BACE1 inhibitors at clinical trials is attracting many queries prompting researchers to investigate newer concepts necessary for effective inhibitor design. The considered properties for efficient BACE1 inhibitor design seem enormous and require thorough scrutiny. Lately, researchers noticed that besides appreciable binding affinity and blood-brain barrier (BBB) permeation, BACE1 inhibitor must show low or no affinity for permeability-glycoprotein. Computational modeling methods have profound applications in drug discovery strategy. With the volume of recent in silico studies on BACE1 inhibition, the prospect of identifying potent molecules that would reach the approved level is feasible. Investigators should try pushing many of the identified BACE1 compounds with significant anti-AD properties to preclinical and clinical trial stages. We also advise computational research on allosteric inhibitor design, exosite modeling, and multisite inhibition of BACE1. These alternatives might be a solution to BACE1 drug discovery in AD therapy.

Scope of β-Secretase (BACE1)-Targeted Therapy in Alzheimer's Disease: Emphasizing the Flavonoid Based Natural Scaffold for BACE1 Inhibition

Alzheimer's disease (AD) is a progressive neurodegenerative disease and the most common form of dementia in the world. Studies report the presence of extracellular amyloid plaques consisting of β-amyloid peptide and intracellular tangles consisting of hyperphosphorylated tau proteins as the histopathological indicators of AD. The process of β-amyloid peptide generation by sequential cleavage of amyloid precursor protein by β-secretase (BACE1) and γ-secretase, followed by its aggregation to form amyloid plaques, is the mechanistic basis of the amyloid hypothesis. Other popular hypotheses related to the pathogenesis of AD include the tau hypothesis and the oxidative stress hypothesis. Various targets of the amyloid cascade are now in prime focus to develop drugs for AD. Many BACE1 inhibitors, β-amyloid aggregation inhibitors, and Aβ clearance strategies using monoclonal antibodies are in various stages of clinical trials. This review provides an in-depth evaluation of the role of BACE1 in disease pathogenesis and also highlights the therapeutic approaches developed to find more potent but less toxic inhibitors for BACE1, particularly emphasizing the natural scaffold as a nontoxic lead for BACE1 inhibition. Cellular targets and signaling cascades involving BACE1 have been highlighted to understand the physiological role of BACE1. This knowledge is extremely crucial to understand the toxicity evaluations for BACE1-targeted therapy. We have particularly highlighted the scope of flavonoids as a new generation of nontoxic BACE1 inhibitory scaffolds. The structure-activity relationship of BACE1 inhibition for this group of compounds has been highlighted to provide a guideline to design more selective highly potent inhibitors. The review aims to provide a holistic overview of BACE1-targeted therapy for AD that paves the way for future drug development.

In-silico Studies and Biological Activity of Potential BACE-1 Inhibitors

BACKGROUND

Alzheimer's disease is a neurological condition causing cognitive inability and dementia. The pathological lesions and neuronal damage in the brain are caused by self-aggregated fragments of mutated Amyloidal precursor protein (APP).

OBJECTIVE

The controlled APP processing by inhibition of secretase is the strategy to reduce Aβ load to treat Alzheimer's disease.

METHODS

A QSAR study was performed on 55 Pyrrolidine based ligands as BACE-1 inhibitors with an activity magnitude greater than 4 of compounds.

RESULTS

In the advent of designing new BACE-1 inhibitors, the pharmacophore model with correlation (r = 0.90) and root mean square deviation (RMSD) of 0.87 was developed and validated. Further, the hits retrieved by the in-silico approach were evaluated by docking interactions.

CONCLUSION

Two structurally diverse compounds exhibited Asp32 and Thr232 binding with the BACE-1 receptor. The aryl-substituted carbamate compound exhibited the highest fit value and docking score. The biological activity evaluation by in-vitro assay was found to be >0.1μM.

Computer-Aided Drug Design of β-Secretase, γ-Secretase and Anti-Tau Inhibitors for the Discovery of Novel Alzheimer's Therapeutics

Aging-associated neurodegenerative diseases, which are characterized by progressive neuronal death and synapses loss in human brain, are rapidly growing affecting millions of people globally. Alzheimer's is the most common neurodegenerative disease and it can be caused by genetic and environmental risk factors. This review describes the amyloid-β and Tau hypotheses leading to amyloid plaques and neurofibrillary tangles, respectively which are the predominant pathways for the development of anti-Alzheimer's small molecule inhibitors. The function and structure of the druggable targets of these two pathways including β-secretase, γ-secretase, and Tau are discussed in this review article. Computer-Aided Drug Design including computational structure-based design and ligand-based design have been employed successfully to develop inhibitors for biomolecular targets involved in Alzheimer's. The application of computational molecular modeling for the discovery of small molecule inhibitors and modulators for β-secretase and γ-secretase is summarized. Examples of computational approaches employed for the development of anti-amyloid aggregation and anti-Tau phosphorylation, proteolysis and aggregation inhibitors are also reported.

QSAR Classification Models for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer's Disease

Alzheimer’s disease is one of the most common neurodegenerative disorders in elder population. The β-site amyloid cleavage enzyme 1 (BACE1) is the major constituent of amyloid plaques and plays a central role in this brain pathogenesis, thus it constitutes an auspicious pharmacological target for its treatment. In this paper, a QSAR model for identification of potential inhibitors of BACE1 protein is designed by using classification methods. For building this model, a database with 215 molecules collected from different sources has been assembled. This dataset contains diverse compounds with different scaffolds and physical-chemical properties, covering a wide chemical space in the drug-like range. The most distinctive aspect of the applied QSAR strategy is the combination of hybridization with backward elimination of models, which contributes to improve the quality of the final QSAR model. Another relevant step is the visual analysis of the molecular descriptors that allows guaranteeing the absence of information redundancy in the model. The QSAR model performances have been assessed by traditional metrics, and the final proposed model has low cardinality, and reaches a high percentage of chemical compounds correctly classified.

Investigation of naphthofuran moiety as potential dual inhibitor against BACE-1 and GSK-3β: molecular dynamics simulations, binding energy, and network analysis to identify first-in-class dual inhibitors against Alzheimer's disease

BACE-1 and GSK-3β are potential therapeutic drug targets for Alzheimer's disease. Recently, both the targets received attention for designing dual inhibitors for Alzheimer's disease. Until now, only two-scaffold triazinone and curcumin have been reported as BACE-1 and GSK-3β dual inhibitors. Docking, molecular dynamics, clustering, binding energy, and network analysis of triazinone derivatives with BACE-1 and GSK-3β was performed to get molecular insight into the first reported dual inhibitor. Further, we designed and evaluated a naphthofuran series for its ability to inhibit BACE-1 and GSK-3β with the computational approaches. Docking study of naphthofuran series showed a good binding affinity towards both the targets. Molecular dynamics, binding energy, and network analysis were performed to compare their binding with the targets and amino acids responsible for binding. Naphthofuran series derivatives showed good interaction within the active site residues of both of the targets. Hydrogen bond occupancy and binding energy suggested strong binding with the targets. Dual-inhibitor binding was mostly governed by the hydrophobic interactions for both of the targets. Per residue energy decomposition and network analysis identified the key residues involved in the binding and inhibiting BACE-1 and GSK-3β. The results indicated that naphthofuran series derivative 11 may be a promising first-in-class dual inhibitor against BACE-1 and GSK-3β. This naphthofuran series may be further explored to design better dual inhibitors. Graphical abstract Naphthofuran derivative as a dual inhibitor for BACE-1 and GSK-3β.