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

Sulfonamido, amido heterocyclic adducts of tetrazole derivatives as BACE1 inhibitors: in silico exploration

Alzheimer's disease is a neurodegenerative disorder accounting for 60-80% of dementia cases and is accompanied by a high mortality rate in patients above 70 years of age. The formation of senile plaques composed of amyloid-β protein is a hallmark of Alzheimer's disease. Beta-site APP cleaving enzyme 1 (BACE1) is a proteolytic enzyme involved in the degradation of amyloid precursor protein, which further degrades to form toxic amyloid-β fragments. Hence, inhibition of BACE1 was stated to be an effective strategy for Alzheimer's therapeutics. Keeping in mind the structures of different BACE1 inhibitors that had reached the clinical trials, we designed a library of compounds (total 164) based on a substituted 5-amino tetrazole scaffold which was an isosteric replacement of the cyclic amidine moiety, a common component of the BACE1 inhibitors which reached the clinical trials. The scaffold was linked to different structural moieties with the aid of an amide or sulfonamide bond to design some novel molecules. Molecular docking was initially performed and the top 5 molecules were selected based on docking scores and protein-ligand interactions. Furthermore, molecular dynamic simulations were performed for these molecules (3g, 7k, 8n, 9d, 9g) for 100 ns and MM-GBSA calculations were performed for each of these complexes. After critical evaluation of the obtained results, three potential molecules (9d, 8n, and 7k) were forwarded for prolonged stability studies by performing molecular dynamic simulations for 250 ns and simultaneous MM-GBSA calculations. It was observed that the compounds (9d, 8n, and 7k) were forming good interactions with the amino acid residues of the catalytic site of the enzyme with multiple non-covalent interactions. In MD simulations, the compounds have shown better stability and better binding energy throughout the runtime.

Secretase promotes AD progression: simultaneously cleave Notch and APP

Alzheimer's disease (AD) involves complex pathological mechanisms. Secretases include membrane protein extracellular structural domain proteases and intramembrane proteases that cleave the topology to type I or type II. Secretases can effectively regulate the activation of Notch and amyloid precursor protein (APP), key factors in the progression of AD and cancer. This article systematically summarizes the intracellular localization, cleavage sites and products, and biological functions of six subtypes of secretases (α-secretase, β-secretase, γ-secretase, δ-secretase, ε-secretase, and η-secretase), and for the first time, elucidates the commonalities and differences between these subtypes of secretases. We found that each subtype of secretase primarily cleaves APP and Notch as substrates, regulating Aβ levels through APP cleavage to impact the progression of AD, while also cleaving Notch receptors to affect cancer progression. Finally, we review the chemical structures, indications, and research stages of various secretase inhibitors, emphasizing the promising development of secretase inhibitors in the fields of cancer and AD.

Cholinergic Inhibition and Antioxidant Potential of Gongronema latifolium Benth Leaf in Neurodegeneration: Experimental and In Silico Study

The use of Gongronema latifolium for the management of various forms of neurological disorders has generated a lot of interest in the need to further investigate its neurotherapeutic constituents. This work, therefore, focused on assessing the inhibitory potential of selected bioactive components derived from G. latifolium against key neurotherapeutic targets and oxidant species associated with neurodegeneration using in vitro analysis and biomolecular modelling. G. latifolium methanol extract (GLME), solvent partition, chromatographic fractions (A-F) of GLME and pregnane compounds (Iloneoside and marsectohexol) derived from fraction-B with the highest activity were investigated for in vitro acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and monoamine oxidase (MAO) inhibition in addition to their in vitro antioxidant activities. The interactions of iloneoside, marsectohexol, and reference drugs with human acetylcholinesterase, butyrylcholinesterase, and β-secretase (BACE-1) were further assessed using molecular docking, binding free energy calculations, cluster analysis, and molecular dynamics simulations. The GLME and fractions inhibited the activities of both acetylcholinesterase and butyrylcholinesterase in a dose-dependent manner. Iloneoside and marsectohexol exhibited in vitro concentration-dependent inhibitory activities against acetylcholinesterase (IC50 = 19.28, 184.9 µM, respectively) and butyrylcholinesterase (IC50 = 30.75, 43.4 µM, respectively). These compounds also possess ferric ion-reducing, hydroxyl, and superoxide radical-scavenging activities. Iloneoside had the highest docking scores of -9.8, -9.9 -9.4 Kcal for AChE, BChE, and BACE1, respectively. The stability of the interaction of the bioactive compounds with the catalytic residues of the protein targets was preserved in a 100 ns molecular dynamics simulation. Iloneoside, a rare pregnane glycoside, was identified as a neurotherapeutic constituent of G. latifolium leaf. Further studies are suggested to investigate the neurotherapeutic potential in animal models.

Identification of potential inhibitors of cholinergic and β-secretase enzymes from phytochemicals derived from Gongronema latifolium Benth leaf: an integrated computational analysis

Neurodegenerative disorders (NDDs) are associated with increased activities of the brain acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and β-secretase enzyme (BACE1). Inhibition of these enzymes affords therapeutic option for managing NDDs such as Alzheimer's disease (AD) and Parkinson's disease (PD). Although, Gongronema latifolium Benth (GL) has been widely documented in ethnopharmacological and scientific reports for the management of NDDs, there is paucity of information on its underlying mechanism and neurotherapeutic constituents. Herein, 152 previously reported Gongronema latifolium derived-phytochemicals (GLDP) were screened against hAChE, hBChE and hBACE-1 using molecular docking, molecular dynamics (MD) simulations, free energy of binding calculations and cluster analysis. The result of the computational analysis identified silymarin, alpha-amyrin and teraxeron with the highest binding energies (-12.3, -11.2, -10.5 Kcal/mol) for hAChE, hBChE and hBACE-1 respectively as compared with those of the reference inhibitors (-12.3, -9.8 and - 9.4 for donepezil, propidium and aminoquinoline compound respectively). These best docked phytochemicals were found to be orientated in the hydrophobic gorge where they interacted with the choline-binding pocket in the A-site and P-site of the cholinesterase and subsites S1, S3, S3' and flip (67-75) residues of the pocket of the BACE-1. The best docked phytochemicals complexed with the target proteins were stable in a 100 ns molecular dynamic simulation. The interactions with the catalytic residues were preserved during the simulation as observed from the MMGBSA decomposition and cluster analyses. The presence of these phytocompounds most notably silymarin, which demonstrated dual high binding tendencies to both cholinesterases, were identified as potential neurotherapeutics subject to further investigation.

Determining Structural Changes for Ligand Recognition between Human and Rat Phosphorylated BACE1 in Silico and Its Phosphorylation by GSK3β at Thr252 by in Vitro Studies

Alzheimer's disease (AD) is a neurodegenerative disease affecting older adults. AD pathogenesis involves the production of the highly neurotoxic amyloid-β peptide 1-42 (Aβ1-42) from β-site amyloid precursor protein cleaving enzyme 1 (BACE1). The phosphorylation of BACE1 at Thr252 increases its enzymatic activity. This study examined the phosphorylation of BACE1 from human and rat BACE1 in silico through phosphorylation predictors. Besides, we explored how phosphorylation at various sites affected the BACE1 structure and its affinity with amyloid precursor protein (APP) and six BACE1 inhibitors. Additionally, we evaluated the phosphorylation of Thr252-BACE1 by glycogen synthase kinase 3 β (GSK3β) in vitro. The phosphorylation predictors showed that Thr252, Ser59, Tyr76, Ser71, and Ser83 could be phosphorylated. Also, Ser127 in rat BACE1 can be phosphorylated, but human BACE1 has a Gly at this position. Molecular dynamics simulations showed that Ser127 plays an important role in the open and closed BACE1 conformational structures. Docking studies and the molecular mechanics generalized Born surface area (MMGBSA) approach showed that human BACE1 phosphorylated at Thr252 and rat BACE1 phosphorylated at Ser71 have the best binding and free energy with APP, forming hydrogen bonds with Asp672. Importantly, inhibitors have a higher affinity for the phosphorylated rat BACE1 than for its human counterpart, which could explain their failure during clinical trials. Finally, in vitro experiments showed that GSK3β could phosphorylate BACE1. In conclusion, BACE1 phosphorylation influences the BACE1 conformation and its recognition of ligands and substrates. Thus, these features should be carefully considered in the design of BACE1 inhibitors.

Structure-based design, and development of amidinyl, amidoximyl and hydroxamic acid based organic molecules as novel antimalarial drug candidates

Malaria remains a significant global health concern causing numerous fatalities and the emergence of antimalarial drug resistance highlights the urgent need for novel therapeutic options with innovative mechanisms of action and targets. This study aimed to design potential inhibitors of Plasmodium falciparum 6-pyruvoyltetrahydropterin synthase (PfPTPS), synthesize them, and experimentally validate their efficacy as antimalarial agents. A structure-based approach was employed to design a series of novel derivatives, including amidinyl, amidoximyl and hydroxamic acid analogs (1c, 1d, 2b, and 3b), with a focus on their ability to bind to the Zn2+ present in the active site of PfPTPS. The syntheses of these compounds were accomplished through various multi-step synthetic pathways and their structural identities were confirmed using 1H and 13C NMR spectra, mass spectra, and elemental analysis. The compounds were screened for their antiplasmodial activity against the NF54 strain of P. falciparum and in vitro cytotoxicity testing was performed using L-6 cells. The in vivo acute toxicity of the compounds was evaluated in mice. Docking studies of the compounds with the 3D structure of PfPTPS revealed their strong binding affinities, with compound 3b exhibiting notable metal-acceptor interaction with the Zn2+ in the protein binding pocket thereby positioning it as a lead compound for PfPTPS inhibition. The in vitro antiplasmodial studies revealed moderate efficacies against the Pf NF54 strain, particularly compounds 1d and 3b which displayed IC50 < 0.2 μM. No significant cytotoxicity was noted on the L-6 rat cell line. Moreover, in vivo studies suggested that compound 3b exhibited both safety and efficacy in treating rodent malaria. The identified lead compound in this study represents a possible candidate for antimalarial drug development and can be further explored in the search for alternative antifolate drugs to combat the malaria menace.

Potential of Tryptamine Derivatives as Multi-Target Directed Ligands for Alzheimer's Disease: AChE, MAO-B, and COX-2 as Molecular Targets

Extensive research has been dedicated to develop compounds that can target multiple aspects of Alzheimer's disease (AD) treatment due to a growing understanding of AD's complex multifaceted nature and various interconnected pathological pathways. In the present study, a series of biological assays were performed to evaluate the potential of the tryptamine analogues synthesized earlier in our lab as multi-target-directed ligands (MTDLs) for AD. To assess the inhibitory effects of the compounds, various in vitro assays were employed. Three compounds, SR42, SR25, and SR10, displayed significant AChE inhibitory activity, with IC50 values of 0.70 µM, 0.17 µM, and 1.00 µM, respectively. These values superseded the standard drug donepezil (1.96 µM). In the MAO-B inhibition assay, SR42 (IC50 = 43.21 µM) demonstrated superior inhibitory effects as compared to tryptamine and other derivatives. Moreover, SR22 (84.08%), SR24 (79.30%), and SR42 (75.16%) exhibited notable percent inhibition against the COX-2 enzyme at a tested concentration of 100 µM. To gain insights into their binding mode and to validate the biological results, molecular docking studies were conducted. Overall, the results suggest that SR42, a 4,5 nitro-benzoyl derivative of tryptamine, exhibited significant potential as a MTDL and warrants further investigation for the development of anti-Alzheimer agents.

Advances in Developing Small Molecule Drugs for Alzheimer's Disease

Alzheimer's disease (AD) is the most common type of dementia among middle-aged and elderly individuals. Accelerating the prevention and treatment of AD has become an urgent problem. New technology including Computer-aided drug design (CADD) can effectively reduce the medication cost for patients with AD, reduce the cost of living, and improve the quality of life of patients, providing new ideas for treating AD. This paper reviews the pathogenesis of AD, the latest developments in CADD and other small-molecule docking technologies for drug discovery and development; the current research status of small-molecule compounds for AD at home and abroad from the perspective of drug action targets; the future of AD drug development.

Protective effects of L-arginine on Alzheimer's disease: Modulating hippocampal nitric oxide levels and memory deficits in aluminum chloride-induced rat model

There is evidence that high daily intake of aluminum (Al) is associated with an increased risk of dementia or cognitive decline. We injected L-arginine into the dorsal hippocampus (DH) of an AlCl3-induced Alzheimer's model and studied memory deficit, β-amyloid (βA) accumulation, neurodegeneration, and molecular changes. Male Wistar rats were cannulated unilaterally in the DH under a stereotaxic apparatus and a dose of AlCl3 (1-200 μg/rat) was injected into the CA1. After recovery, L-arginine and L-NAME (0.05-25 μg/rat) were injected into CA1 and animals were tested in novelty seeking task. One group received βA (2 μg/rat, intra CA1) as a reference group. Control groups received saline (1 μL/rat, intra-CA1) and galantamine (25 μg/rat, intra-CA1), respectively. Finally, rats were anesthetized and hippocampal tissues were isolated on ice. Levels of neuronal NO synthase (nNOS), β-secretase and soluble guanylyl cyclase (sGC) were measured by western blotting. βA formation and the number of CA1 neurons were assessed by Congo red and Nissl staining. NOS activation by NADPH-diaphorase (NADPH-d) was investigated. All data were analyzed using analysis of variance (ANOVA) at α = 0.05 level. Like βA, AlCl3 (25 μg/rat) caused accumulation of βA in the DH and increased stopping of the animal on the novel side (indicating a recall deficit). CA1 neurons decreased, and nNOS and β-secretase, but not sGC, showed a change consistent with Alzheimer's. However, prophylactic intervention of L-arginine at 3-9 μg/rat was protective, probably by nNOS stimulation in DH, as shown by NADPH-d assay. L-arginine may protect against Alzheimer's by increasing hippocampal NO levels.

Advancement of Computational Design Drug Delivery System in COVID-19: Current Updates and Future Crosstalk- A Critical update

Positive strides have been achieved in developing vaccines to combat the coronavirus-2019 infection (COVID-19) pandemic. Still, the outline of variations, particularly the most current delta divergent, has posed significant health encounters for people. Therefore, developing strong treatment strategies, such as an anti-COVID-19 medicine plan, may help deal with the pandemic more effectively. During the COVID-19 pandemic, some drug design techniques were effectively used to develop and substantiate relevant critical medications. Extensive research, both experimental and computational, has been dedicated to comprehending and characterizing the devastating COVID-19 disease. The urgency of the situation has led to the publication of over 130,000 COVID-19-related research papers in peer-reviewed journals and preprint servers. A significant focus of these efforts has been the identification of novel drug candidates and the repurposing of existing drugs to combat the virus. Many projects have utilized computational or computer-aided approaches to facilitate their studies. In this overview, we will explore the key computational methods and their applications in the discovery of small-molecule therapeutics for COVID-19, as reported in the research literature. We believe that the true effectiveness of computational tools lies in their ability to provide actionable and experimentally testable hypotheses, which in turn facilitate the discovery of new drugs and combinations thereof. Additionally, we recognize that open science and the rapid sharing of research findings are vital in expediting the development of much-needed therapeutics for COVID-19.

Insights into the Structural Conformations of the Tau Protein in Different Aggregation Status

Tau is a protein characterized by large structural portions displaying extended conformational changes. Unfortunately, the accumulation of this protein into toxic aggregates in neuronal cells leads to a number of severe pathologies, collectively named tauopathies. In the last decade, significant research advancements were achieved, including a better understanding of Tau structures and their implication in different tauopathies. Interestingly, Tau is characterized by a high structural variability depending on the type of disease, the crystallization conditions, and the formation of pathologic aggregates obtained from in vitro versus ex vivo samples. In this review, we reported an up-to-date and comprehensive overview of Tau structures reported in the Protein Data Bank, with a special focus on discussing the connections between structural features, different tauopathies, different crystallization conditions, and the use of in vitro or ex vivo samples. The information reported in this article highlights very interesting links between all these aspects, which we believe may be of particular relevance for a more informed structure-based design of compounds able to modulate Tau aggregation.

A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation

The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on the drug development pipeline. Numerous challenges have been addressed with the growing exploitation of drug-related data and the advancement of deep learning technology. Several model frameworks have been proposed to enhance the performance of deep learning algorithms in molecular design. However, only a few have had an immediate impact on drug development since computational results may not be confirmed experimentally. This systematic review aims to summarize the different deep learning architectures used in the drug discovery process and are validated with further in vivo experiments. For each presented study, the proposed molecule or peptide that has been generated or identified by the deep learning model has been biologically evaluated in animal models. These state-of-the-art studies highlight that even if artificial intelligence in drug discovery is still in its infancy, it has great potential to accelerate the drug discovery cycle, reduce the required costs, and contribute to the integration of the 3R (Replacement, Reduction, Refinement) principles. Out of all the reviewed scientific articles, seven algorithms were identified: recurrent neural networks, specifically, long short-term memory (LSTM-RNNs), Autoencoders (AEs) and their Wasserstein Autoencoders (WAEs) and Variational Autoencoders (VAEs) variants; Convolutional Neural Networks (CNNs); Direct Message Passing Neural Networks (D-MPNNs); and Multitask Deep Neural Networks (MTDNNs). LSTM-RNNs were the most used architectures with molecules or peptide sequences as inputs.

Regulation of beta-amyloid for the treatment of Alzheimer's disease: Research progress of therapeutic strategies and bioactive compounds

Alzheimer's disease (AD) is a progressive neurodegenerative disease that is difficult to treat. Extracellular amyloid is the principal pathological criterion for the diagnosis of AD. Amyloid β (Aβ) interacts with various receptor molecules on the plasma membrane and mediates a series of signaling pathways that play a vital role in the occurrence and development of AD. Research on receptors that interact with Aβ is currently ongoing. Overall, there are no effective medications to treat AD. In this review, we first discuss the importance of Aβ in the pathogenesis of AD, then summarize the latest progress of Aβ-related targets and compounds. Finally, we put forward the challenges and opportunities in the development of effective AD therapies.

Unmasking BACE1 in aging and age-related diseases

The beta-site amyloid precursor protein (APP)-cleaving enzyme 1 (BACE1) has long been considered a conventional target for Alzheimer's disease (AD). Unfortunately, AD clinical trials of most BACE1 inhibitors were discontinued due to ineffective cognitive improvement or safety challenges. Recent studies investigating the involvement of BACE1 in metabolic, vascular, and immune functions have indicated a role in aging, diabetes, hypertension, and cancer. These novel BACE1 functions have helped to identify new 'druggable' targets for BACE1 against aging comorbidities. In this review, we discuss BACE1 regulation during aging, and then provide recent insights into its enzymatic and nonenzymatic involvement in aging and age-related diseases. Our study not only proposes the perspective of BACE1's actions in various systems, but also provides new directions for using BACE1 inhibitors and modulators to delay aging and to treat age-related diseases.

Novel Strategy for Alzheimer’s Disease Treatment through Oral Vaccine Therapy with Amyloid Beta

Alzheimer’s disease (AD) is a neuropathology characterized by progressive cognitive impairment and dementia. The disease is attributed to senile plaques, which are aggregates of amyloid beta (Aβ) outside nerve cells; neurofibrillary tangles, which are filamentous accumulations of phosphorylated tau in nerve cells; and loss of neurons in the brain tissue. Immunization of an AD mouse model with Aβ-eliminated pre-existing senile plaque amyloids and prevented new accumulation. Furthermore, its effect showed that cognitive function can be improved by passive immunity without side effects, such as lymphocyte infiltration in AD model mice treated with vaccine therapy, indicating the possibility of vaccine therapy for AD. Further, considering the possibility of side effects due to direct administration of Aβ, the practical use of the safe oral vaccine, which expressed Aβ in plants, is expected. Indeed, administration of this oral vaccine to Alzheimer’s model mice reduced Aβ accumulation in the brain. Moreover, almost no expression of inflammatory IgG was observed. Therefore, vaccination prior to Aβ accumulation or at an early stage of accumulation may prevent Aβ from causing AD.

Modern drug discovery for inflammatory bowel disease: The role of computational methods

Inflammatory bowel diseases (IBDs) comprising ulcerative colitis, Crohn’s disease and microscopic colitis are characterized by chronic inflammation of the gastrointestinal tract. IBD has spread around the world and is becoming more prevalent at an alarming rate in developing countries whose societies have become more westernized. Cell therapy, intestinal microecology, apheresis therapy, exosome therapy and small molecules are emerging therapeutic options for IBD. Currently, it is thought that low-molecular-mass substances with good oral bio-availability and the ability to permeate the cell membrane to regulate the action of elements of the inflammatory signaling pathway are effective therapeutic options for the treatment of IBD. Several small molecule inhibitors are being developed as a promising alternative for IBD therapy. The use of highly efficient and time-saving techniques, such as computational methods, is still a viable option for the development of these small molecule drugs. The computer-aided (in silico) discovery approach is one drug development technique that has mostly proven efficacy. Computational approaches when combined with traditional drug development methodology dramatically boost the likelihood of drug discovery in a sustainable and cost-effective manner. This review focuses on the modern drug discovery approaches for the design of novel IBD drugs with an emphasis on the role of computational methods. Some computational approaches to IBD genomic studies, target identification, and virtual screening for the discovery of new drugs and in the repurposing of existing drugs are discussed.

Ligand and structure-based approaches for the exploration of structure-activity relationships of fusidic acid derivatives as antibacterial agents

Introduction: Fusidic acid (FA) has been widely applied in the clinical prevention and treatment of bacterial infections. Nonetheless, its clinical application has been limited due to its narrow antimicrobial spectrum and some side effects. Purpose: Therefore, it is necessary to explore the structure-activity relationships of FA derivatives as antibacterial agents to develop novel ones possessing a broad antimicrobial spectrum. Methods and result: First, a pharmacophore model was established on the nineteen FA derivatives with remarkable antibacterial activities reported in previous studies. The common structural characteristics of the pharmacophore emerging from the FA derivatives were determined as those of six hydrophobic centers, two atom centers of the hydrogen bond acceptor, and a negative electron center around the C-21 field. Then, seven FA derivatives have been designed according to the reported structure-activity relationships and the pharmacophore characteristics. The designed FA derivatives were mapped on the pharmacophore model, and the Qfit values of all FA derivatives were over 50 and FA-8 possessed the highest value of 82.66. The molecular docking studies of the partial target compounds were conducted with the elongation factor G (EF-G) of S. aureus. Furthermore, the designed FA derivatives have been prepared and their antibacterial activities were evaluated by the inhibition zone test and the minimum inhibitory concentration (MIC) test. The derivative FA-7 with a chlorine group as the substituent group at C-25 of FA displayed the best antibacterial property with an MIC of 3.125 µM. Subsequently, 3D-QSAR was carried on all the derivatives by using the CoMSIA mode of SYBYL-X 2.0. Conclusion: Hence, a computer-aided drug design model was developed for FA, which can be further used to optimize FA derivatives as highly potent antibacterial agents.

Computational Studies Applied to Linalool and Citronellal Derivatives Against Alzheimer's and Parkinson's Disorders: A Review with Experimental Approach

Alzheimer's and Parkinson's are neurodegenerative disorders that affect a great number of people around the world, seriously compromising the quality of life of individuals, due to motor and cognitive damage. In these diseases, pharmacological treatment is used only to alleviate symptoms. This emphasizes the need to discover alternative molecules for use in prevention. Using Molecular Docking, this review aimed to evaluate the anti-Alzheimer's and anti-Parkinson's activity of linalool and citronellal, as well as their derivatives. Before performing Molecular Docking simulations, the compounds' pharmacokinetic characteristics were evaluated. For Molecular Docking, 7 chemical compounds derived from citronellal, and 10 compounds derived from linalool, and molecular targets involved in Alzheimer's and Parkinson's pathophysiology were selected. According to the Lipinski rules, the compounds under study presented good oral absorption and bioavailability. For toxicity, some tissue irritability was observed. For Parkinson-related targets, the citronellal and linalool derived compounds revealed excellent energetic affinity for α-Synuclein, Adenosine Receptors, Monoamine Oxidase (MAO), and Dopamine D1 receptor proteins. For Alzheimer disease targets, only linalool and its derivatives presented promise against BACE enzyme activity. The compounds studied presented high probability of modulatory activity against the disease targets under study, and are potential candidates for future drugs.

Amylin and Secretases in the Pathology and Treatment of Alzheimer's Disease

Alzheimer's disease remains a prevailing neurodegenerative condition which has an array physical, emotional, and financial consequences to patients and society. In the past decade, there has been a greater degree of investigation on therapeutic small peptides. This group of biomolecules have a profile of fundamentally sound characteristics which make them an intriguing area for drug development. Among these biomolecules, there are four modulatory mechanisms of interest in this review: alpha-, beta-, gamma-secretases, and amylin. These protease-based biomolecules all have a contributory role in the amyloid cascade hypothesis. Moreover, the involvement of various biochemical pathways intertwines these peptides to have shared regulators (i.e., retinoids). Further clinical and translational investigation must occur to gain a greater understanding of its potential application in patient care. The aim of this narrative review is to evaluate the contemporary literature on these protease biomolecule modulators and determine its utility in the treatment of Alzheimer's disease.

Allostery Inhibition of BACE1 by Psychotic and Meroterpenoid Drugs in Alzheimer's Disease Therapy

In over a century since its discovery, Alzheimer's disease (AD) has continued to be a global health concern due to its incurable nature and overwhelming increase among older people. In this paper, we give an overview of the efforts of researchers towards identifying potent BACE1 exosite-binding antibodies and allosteric inhibitors. Herein, we apply computer-aided drug design (CADD) methods to unravel the interactions of some proposed psychotic and meroterpenoid BACE1 allosteric site inhibitors. This study is aimed at validating the allosteric potentials of these selected compounds targeted at BACE1 inhibition. Molecular docking, molecular dynamic (MD) simulations, and post-MD analyses are carried out on these selected compounds, which have been experimentally proven to exhibit allosteric inhibition on BACE1. The SwissDock software enabled us to identify more than five druggable pockets on the BACE1 structural surface using docking. Besides the active site region, a melatonin derivative (compound 1) previously proposed as a BACE1 allostery inhibitor showed appreciable stability at eight different subsites on BACE1. Refinement with molecular dynamic (MD) simulations shows that the identified non-catalytic sites are potential allostery sites for compound 1. The allostery and binding mechanism of the selected potent inhibitors show that the smaller the molecule, the easier the attachment to several enzyme regions. This finding hereby establishes that most of these selected compounds failed to exhibit strong allosteric binding with BACE1 except for compound 1. We hereby suggest that further studies and additional identification/validation of other BACE1 allosteric compounds be done. Furthermore, this additional allosteric site investigation will help in reducing the associated challenges with designing BACE1 inhibitors while exploring the opportunities in the design of allosteric BACE1 inhibitors.

Computational anti-COVID-19 drug design: progress and challenges

Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.

Concatenation of molecular docking and molecular simulation of BACE-1, γ-secretase targeted ligands: in pursuit of Alzheimer's treatment

INTRODUCTION

Alzheimer's disease (AD), the most predominant cause of dementia, has evolved tremendously with an escalating frequency, mainly affecting the elderly population. An effective means of delaying, preventing, or treating AD is yet to be achieved. The failure rate of dementia drug trials has been relatively higher than in other disease-related clinical trials. Hence, multi-targeted therapeutic approaches are gaining attention in pharmacological developments.

AIMS

As an extension of our earlier reports, we have performed docking and molecular dynamic (MD) simulation studies for the same 13 potential ligands against beta-site APP cleaving enzyme 1 (BACE-1) and γ-secretase as a therapeutic target for AD. The In-silico screening of these ligands as potential inhibitors of BACE-1 and γ-secretase was performed using AutoDock enabled PyRx v-0.8. The protein-ligand interactions were analyzed in Discovery Studio 2020 (BIOVIA). The stability of the most promising ligand against BACE-1 and γ-secretase was evaluated by MD simulation using Desmond-2018 (Schrodinger, LLC, NY, USA).

RESULTS

The computational screening revealed that the docking energy values for each of the ligands against both the target enzymes were in the range of -7.0 to -10.1 kcal/mol. Among the 13 ligands, 8 (55E, 6Z2, 6Z5, BRW, F1B, GVP, IQ6, and X37) showed binding energies of ≤-8 kcal/mol against BACE-1 and γ-secretase. For the selected enzyme targets, BACE-1 and γ-secretase, 6Z5 displayed the lowest binding energy of -10.1 and -9.8 kcal/mol, respectively. The MD simulation study confirmed the stability of BACE-6Z5 and γ-secretase-6Z5 complexes and highlighted the formation of a stable complex between 6Z5 and target enzymes.

CONCLUSION

The virtual screening, molecular docking, and molecular dynamics simulation studies revealed the potential of these multi-enzyme targeted ligands. Among the studied ligands, 6Z5 seems to have the best binding potential and forms a stable complex with BACE-1 and γ-secretase. We recommend the synthesis of 6Z5 for future in-vitro and in-vivo studies.

ABCpred: a webserver for the discovery of acetyl- and butyryl-cholinesterase inhibitors

Alzheimer's disease (AD) is one of the most common forms of dementia and is associated with a decline in cognitive function and language ability. The deficiency of the cholinergic neurotransmitter known as acetylcholine (ACh) is associated with AD. Acetylcholinesterase (AChE) hydrolyses ACh and inhibits the cholinergic transmission. Furthermore, both AChE and butyrylcholinesterase (BChE) plays important roles in early and late stages of AD. Therefore, the inhibition of either or both cholinesterase enzymes represent a promising therapeutic route for treating AD. In this study, a large-scale classification structure-activity relationship model was developed to predict cholinesterase inhibitory activities as well as revealing important substructures governing their activities. Herein, a non-redundant dataset constituting 985 and 1056 compounds for AChE and BChE, respectively, was obtained from the ChEMBL database. These inhibitors were described by 12 sets of molecular fingerprints and predictive models were developed using the random forest algorithm. Evaluation of the model performance by means of Matthews correlation coefficient and consideration of the model's interpretability indicated that the SubstructureCount fingerprint was the most robust with five-fold cross-validated MCC of [0.76, 0.82] for AChE and BChE, respectively, and test MCC of [0.73, 0.97]. Feature interpretation revealed that the aromatic ring system, heterocyclic nitrogen containing compounds and amines are important for cholinesterase inhibition. Finally, the model was deployed as a publicly available webserver called the ABCpred at http://codes.bio/abcpred/ .

TRIOMPHE: Transcriptome-Based Inference and Generation of Molecules with Desired Phenotypes by Machine Learning

One of the most challenging tasks in the drug-discovery process is the efficient identification of small molecules with desired phenotypes. In this study, we propose a novel computational method for omics-based de novo drug design, which we call TRIOMPHE (transcriptome-based inference and generation of molecules with desired phenotypes). We investigated the correlation between chemically induced transcriptome profiles (reflecting cellular responses to compound treatment) and genetically perturbed transcriptome profiles (reflecting cellular responses to gene knock-down or gene overexpression of target proteins) in terms of ligand-target interactions. Subsequently, we developed novel machine learning methods to generate the chemical structures of new molecules with desired transcriptome profiles in the framework of a variational autoencoder. The use of desired transcriptome profiles enables the automatic design of molecules that are likely to have bioactivities for target proteins of interest. We showed that our methods can generate chemically valid molecules that are likely to have biological activities on 10 target proteins; moreover, they can outperform previous methods that had the same objective. Our omics-based structure generator is expected to be useful for the de novo design of drugs for a variety of target proteins.

In Silico Identification and Evaluation of Natural Products as Potential Tumor Necrosis Factor Function Inhibitors Using Advanced Enalos Asclepios KNIME Nodes

Tumor necrosis factor (TNF) is a regulator of several chronic inflammatory diseases, such as rheumatoid arthritis. Although anti-TNF biologics have been used in clinic, they render several drawbacks, such as patients' progressive immunodeficiency and loss of response, high cost, and intravenous administration. In order to find new potential anti-TNF small molecule inhibitors, we employed an in silico approach, aiming to find natural products, analogs of Ampelopsin H, a compound that blocks the formation of TNF active trimer. Two out of nine commercially available compounds tested, Nepalensinol B and Miyabenol A, efficiently reduced TNF-induced cytotoxicity in L929 cells and production of chemokines in mice joints' synovial fibroblasts, while Nepalensinol B also abolished TNF-TNFR1 binding in non-toxic concentrations. The binding mode of the compounds was further investigated by molecular dynamics and free energy calculation studies, using and advancing the Enalos Asclepios pipeline. Conclusively, we propose that Nepalensinol B, characterized by the lowest free energy of binding and by a higher number of hydrogen bonds with TNF, qualifies as a potential lead compound for TNF inhibitors' drug development. Finally, the upgraded Enalos Asclepios pipeline can be used for improved identification of new therapeutics against TNF-mediated chronic inflammatory diseases, providing state-of-the-art insight on their binding mode.

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.

Insulin Resistance Exacerbates Alzheimer Disease via Multiple Mechanisms

Alzheimer disease (AD) is a chronic neurodegenerative disease that accounts for 60–70% of dementia and is the sixth leading cause of death in the United States. The pathogenesis of this debilitating disorder is still not completely understood. New insights into the pathogenesis of AD are needed in order to develop novel pharmacologic approaches. In recent years, numerous studies have shown that insulin resistance plays a significant role in the development of AD. Over 80% of patients with AD have type II diabetes (T2DM) or abnormal serum glucose, suggesting that the pathogenic mechanisms of insulin resistance and AD likely overlap. Insulin resistance increases neuroinflammation, which promotes both amyloid β-protein deposition and aberrant tau phosphorylation. By increasing production of reactive oxygen species, insulin resistance triggers amyloid β-protein accumulation. Oxidative stress associated with insulin resistance also dysregulates glycogen synthase kinase 3-β (GSK-3β), which leads to increased tau phosphorylation. Both insulin and amyloid β-protein are metabolized by insulin degrading enzyme (IDE). Defects in this enzyme are the basis for a strong association between T2DM and AD. This review highlights multiple pathogenic mechanisms induced by insulin resistance that are implicated in AD. Several pharmacologic approaches to AD associated with insulin resistance are presented.

An Overview of β-Amyloid Cleaving Enzyme 1 (Bace1) in Alzheimer's Disease Therapy Elucidating its Exosite-Binding Antibody and Allosteric Inhibitor

Over decades of its identification, numerous past and ongoing researches have focused on the therapeutic roles of β-amyloid cleaving enzyme 1 (BACE1) as a target in treating Alzheimer's disease (AD). Although the initial BACE1 inhibitors at phase-3 clinical trials tremendously reduced β-amyloid-associated plaques in patients with AD, the researchers eventually discontinued the tests due to the lack of potency. This discontinuation has resulted in limited drug development and discovery targeted at BACE1, despite the high demand for dementia and AD therapies. It is, therefore, imperative to describe the detailed underlying biological basis of the BACE1 therapeutic option in neurological diseases. Herein, we highlight BACE1 bioactivity, genetic properties, and role in neurodegenerative therapy. We review research contributions to BACE1 exosite-binding antibody and allosteric inhibitor development as AD therapies. The review also covers BACE1 biological function, the disease-associated mechanisms, and the enzyme conditions for amyloid precursor protein sites splitting. Based on the present review, we suggest further studies on anti-BACE1 exosite antibodies and BACE1 allosteric inhibitors. Non-active site inhibition might be the way forward to BACE1 therapy in Alzheimer's neurological disorder.

Advances in Applying Computer-Aided Drug Design for Neurodegenerative Diseases

Neurodegenerative diseases (NDs) including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease are incurable and affect millions of people worldwide. The development of treatments for this unmet clinical need is a major global research challenge. Computer-aided drug design (CADD) methods minimize the huge number of ligands that could be screened in biological assays, reducing the cost, time, and effort required to develop new drugs. In this review, we provide an introduction to CADD and examine the progress in applying CADD and other molecular docking studies to NDs. We provide an updated overview of potential therapeutic targets for various NDs and discuss some of the advantages and disadvantages of these tools.

Current Quest in Natural Bioactive Compounds for Alzheimer's Disease: Multi-Targeted-Designed-Ligand Based Approach with Preclinical and Clinical Based Evidence

Alzheimer's disease is a common and most chronic neurological disorder (NDs) associated with cognitive dysfunction. Pathologically, Alzheimer's disease (AD) is characterized by the presence of β-amyloid (Aβ) plaques, hyper-phosphorylated tau proteins, and neurofibrillary tangles, however, persistence oxidative-nitrative stress, endoplasmic reticulum stress, mitochondrial dysfunction, inflammatory cytokines, pro-apoptotic proteins along with altered neurotransmitters level are common etiological attributes in its pathogenesis. Rivastigmine, memantine, galantamine, and donepezil are FDA approved drugs for symptomatic management of AD, whereas tacrine has been withdrawn because of hepatotoxic profile. These approved drugs only exert symptomatic relief and exhibit poor patient compliance. In the current scenario, the number of published evidence shows the neuroprotective potential of naturally occurring bioactive molecules via their antioxidant, anti-inflammatory, antiapoptotic and neurotransmitter modulatory properties. Despite their potent therapeutic implications, concerns have arisen in context to their efficacy and probable clinical outcome. Thus, to overcome these glitches, many heterocyclic and cyclic hydrocarbon compounds inspired by natural sources have been synthesized and showed improved therapeutic activity. Computational studies (molecular docking) have been used to predict the binding affinity of these natural bioactive as well as synthetic compounds derived from natural sources for the acetylcholine esterase, α/β secretase Nuclear Factor kappa- light-chain-enhancer of activated B cells (NF-kB), Nuclear factor erythroid 2-related factor 2(Nrf2) and other neurological targets. Thus, in this review, we have discussed the molecular etiology of AD, focused on the pharmacotherapeutics of natural products, chemical and pharmacological aspects and multi-targeted designed ligands (MTDLs) of synthetic and semisynthetic molecules derived from the natural sources along with some important on-going clinical trials.

The structure and phase of tau: from monomer to amyloid filament

Tau is a microtubule-associated protein involved in regulation of assembly and spatial organization of microtubule in neurons. However, in pathological conditions, tau monomers assemble into amyloid filaments characterized by the cross-β structures in a number of neurodegenerative diseases known as tauopathies. In this review, we summarize recent progression on the characterization of structures of tau monomer and filament, as well as the dynamic liquid droplet assembly. Our aim is to reveal how post-translational modifications, amino acid mutations, and interacting molecules modulate the conformational ensemble of tau monomer, and how they accelerate or inhibit tau assembly into aggregates. Structure-based aggregation inhibitor design is also discussed in the context of dynamics and heterogeneity of tau structures.

Advances in de Novo Drug Design: From Conventional to Machine Learning Methods

. De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and highlights hot topics for further development.

Tau Filament Self-Assembly and Structure: Tau as a Therapeutic Target

Tau plays an important pathological role in a group of neurodegenerative diseases called tauopathies, including Alzheimer's disease, Pick's disease, chronic traumatic encephalopathy and corticobasal degeneration. In each disease, tau self-assembles abnormally to form filaments that deposit in the brain. Tau is a natively unfolded protein that can adopt distinct structures in different pathological disorders. Cryo-electron microscopy has recently provided a series of structures for the core of the filaments purified from brain tissue from patients with different tauopathies and revealed that they share a common core region, while differing in their specific conformation. This structurally resolvable part of the core is contained within a proteolytically stable core region from the repeat domain initially isolated from AD tau filaments. Tau has recently become an important target for therapy. Recent work has suggested that the prevention of tau self-assembly may be effective in slowing the progression of Alzheimer's disease and other tauopathies. Here we review the work that explores the importance of tau filament structures and tau self-assembly mechanisms, as well as examining model systems that permit the exploration of the mode of action of potential inhibitors.

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 modeling for dual inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) enzymes in Alzheimer's disease

In this research, we have implemented two-dimensional quantitative structure-activity relationship (2D-QSAR) modeling using two different datasets, namely, acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) enzyme inhibitors. A third dataset has been derived based on their selectivity and used for the development of partial least squares (PLS) based regression models. The developed models were extensively validated using various internal and external validation parameters. The features appearing in the model against AChE enzyme suggest that a small ring size, higher number of -CH2- groups, higher number of secondary aromatic amines and higher number of aromatic ketone groups may contribute to the inhibitory activity. The features obtained from the model against BuChE enzyme suggest that the sum of topological distances between two nitrogen atoms, higher number of fragments X-C(=X)-X, higher number of secondary aromatic amides, fragment R--CR-X may be more favorable for inhibition. The features obtained from selectivity based model suggest that the number of aromatic ethers, unsaturation content relative to the molecular size and molecular shape may be more specific for the inhibition of the AChE enzyme in comparison to the BuChE enzyme. Moreover, we have implemented the molecular docking studies using the most and least active molecules from the datasets in order to identify the binding pattern between ligand and target enzyme. The obtained information is then correlated with the essential structural features associated with the 2D-QSAR models.

Lipidomics-based assays coupled with computational approaches can identify novel phospholipase A2 inhibitors

Phospholipase A₂ (PLA₂) enzymes play a major role in many diseases including the inflammatory cascade and specific potent small molecule inhibitors could be useful in studying their physiological role as well as for the development of drugs. In order to discover novel small molecule inhibitor platforms for members of the PLA₂ superfamily of enzymes, we have applied computational approaches to determine the binding mode of potent inhibitors specific for particular PLA₂s to the screening of chemical libraries. This has including the U.S. National Institutes of Health (NIH) National Cancer Institute (NCI) Diversity Set V and the ChemBridge commercial compound libraries. We have then subjected identified inhibitor structures to recently developed lipidomics based screening assays to determine the X_I(50) and specificity of the identified compounds for specific PLA₂s. Herein we review this approach and report the identity of initial hits for both the Group IVA cytosolic PLA₂ and the Group VIA calcium-independent PLA₂ that are worthy of further structural modification to develop novel platforms for inhibitor development.