Alzheimer's Disease Drug Discovery Research Articles

TCnet: A Novel Strategy to Predict Target Combination of Alzheimer's Disease via Network-Based Methods.

Alzheimer's disease (AD) is a complex neurodegenerative disorder with an unclear pathogenesis; the traditional ″single gene-single target-single drug″ strategy is insufficient for effective treatment. This study explores a novel strategy for the multitarget therapy of AD by integrating multiomics data and employing network analysis. Different from conventional single-target methods, TCnet adopts a mechanism-driven strategy, utilizing multiomics data to decompose disease mechanisms, construct potential target combinations, and prioritize the optimal combinations using a scoring function. TCnet not only advances our understanding of disease mechanisms but also facilitates large-scale drug screening. This approach was further employed to screen active compounds from Huang-Lian-Jie-Du-Tang (HLJDT), identifying quercetin as a candidate targeting GSK3β and ADAM17. Subsequent in vitro experiments confirmed the neuroprotective and anti-inflammatory effects of quercetin. Overall, TCnet offers a promising approach for predicting target combinations and provides new insights and directions for drug discovery in AD.

HDAC3 as an Emerging Therapeutic Target for Alzheimer's Disease and other Neurological Disorders.

Alzheimer's disease (AD) is the most common cause of dementia in the aged population. Histone acetylation is a major epigenetic mechanism linked to memory formation and cognitive function. Histone deacetylases (HDACs) are responsible for the deacetylation of lysine residues in histone proteins. Although pan-HDAC inhibitors are effective in ameliorating AD phenotypes in preclinical models, they are associated with potential unfavorable adverse effects and barely translated into clinical trials. Therefore, the development of novel HDAC inhibitors with a well isoform-selectivity has been desired in AD drug discovery. Among various HDAC isoforms, HDAC3 is highly expressed in neurons and exhibits detrimental effects on synaptic plasticity and cognitive function. Moreover, HDAC3 provokes neuroinflammation and neurotoxicity and contributes to AD pathogenesis. In this review, we highlight HDAC3 as an attractive therapeutic target for disease-modifying therapy in AD. In addition, we discuss the therapeutic potential of HDAC3 inhibitors in other neurological disorders.

Utilization of precision medicine digital twins for drug discovery in Alzheimer's disease.

Utilization of precision medicine digital twins for drug discovery in Alzheimer's disease.

Screening for Anti-Aβ Aggregation Activity of Marine Fungal Natural Products Based on a Gold Nanoparticle Method.

Screening Aβ aggregation inhibitors (AAIs) is important for Alzheimer's disease drug discovery. However, common cellular or biochemical methods are not suitable for high-throughput natural product screening. A gold nanoparticle (GNP) screening method was employed in this study to screen marine fungal crude extracts and pure compounds for quick AAI discovering. The anti-Aβ aggregation activity was further inspected using transmission electron microscopic (TEM) observation, and the interaction between active molecules and different Aβ species was revealed by molecular docking. The results indicated that the fungal extracts DLS2008001(M), BM3T2(M), DLEN2008005(M), TBG1-16(P), and TBG1-13(P) showed activity comparable to the positive control human serum albumin at the concentration of 500µg/mL; 10 pure compounds also displayed moderate anti-aggregation activity, particularly nidulin, aspergillusidone G, and butyrolactone I. The inspection of anti-Aβ aggregation effect through TEM further demonstrated that extracts TBG1-16(P), DLS2008001(M), and BM3T2(M) dramatically inhibited the formation of Aβ aggregates. Molecular docking displayed low binding energies and key interactions of nidulin, aspergillusidone G, and butyrolactone I, with nine types of Aβ peptides. These findings indicate that the GNP method is efficient in screening AAIs and reveal marine fungal natural products as valuable sources of small molecular AAIs.

Exploring the mechanism of sunflower seed oil against Alzheimer's disease through experimental and network pharmacology studies.

With the prevalence of Alzheimer's disease (AD) increasing exponentially, there has been a shift in the focus of drug discovery for AD from treating the symptoms to preventing the development of the disease. Several natural compounds are extensively studied as neuroprotectives in preventing disease progression. Helianthus annuus seed oil (HA) is widely used as cooking oil and is abundant in antioxidant activity. Therefore, we evaluated the effect of HA in mice model of scopolamine-induced amnesia and explored the potential underlying mechanisms. Twenty-four male mice were administered orally with either distilled water (control and scopolamine groups) or treatment groups (HA 100 and HA 200 mg/kg) for 8 consecutive days. All groups, except the control group, received an intraperitoneal injection of scopolamine at a dose of 1 mg/kg. Subsequently, novel object recognition task for cognition assessment and open field tests for locomotory activity were performed. In addition, network analysis was performed to identify the key bioactives and targets of HA against AD. Further, the binding affinity of HA bioactives to the key targets was verified by molecular docking analysis. HA (100 mg/kg and 200 mg/kg) significantly ameliorated recognition memory compared to the scopolamine group, suggesting the protective effect of HA against cognitive impairment. Network analysis indicated that the key bioactives of HA, chlorogenic acid, and oleic acid act through multiple targets and pathways, particularly the mitogen-activated protein kinase (MAPK) pathway, to ameliorate AD symptoms. Importantly, chlorogenic acid showed good binding affinity with MAPKs, TP53, and EP300. HA has therapeutic benefits in AD acting through the MAPK pathway. However, further studies need to be done to confirm the results derived and translate the potential use of HA as a dietary supplement for preventing AD.

Predicting biological activity and design of 5-HT6 antagonists through assessment of ANN-QSAR models in the context of Alzheimer's disease.

Alzheimer's disease (AD) is the leading cause of dementia around the world, totaling about 55 million cases, with an estimated growth to 74.7 million cases in 2030, which makes its treatment widely desired. Several studies and strategies are being developed considering the main theories regarding its origin since it is not yet fully understood. Among these strategies, the 5-HT6 receptor antagonism emerges as an auspicious and viable symptomatic treatment approach for AD. The 5-HT6 receptor belongs to the G protein-coupled receptor (GPCR) family and is closely implicated in memory loss processes. As a serotonin receptor, it plays an important role in cognitive function. Consequently, targeting this receptor presents a compelling therapeutic opportunity. By employing antagonists to block its activity, the 5-HT6 receptor's functions can be effectively modulated, leading to potential improvements in cognition and memory. Addressing this challenge, our research explored a promising avenue in drug discovery for AD, employing Artificial Neural Networks-Quantitative Structure-Activity Relationship (ANN-QSAR) models. These models have demonstrated great potential in predicting the biological activity of compounds based on their molecular structures. By harnessing the capabilities of machine learning and computational chemistry, we aimed to create a systematic approach for analyzing and forecasting the activity of potential drug candidates, thus streamlining the drug discovery process. We assembled a diverse set of compounds targeting this receptor and utilized density functional theory (DFT) calculations to extract essential molecular descriptors, effectively representing the structural features of the compounds. Subsequently, these molecular descriptors served as input for training the ANN-QSAR models alongside corresponding biological activity data, enabling us to predict the potential efficacy of novel compounds as 5-hydroxytryptamine receptor 6 (5-HT6) antagonists. Through extensive analysis and validation of ANN-QSAR models, we identified eight new promising compounds with therapeutic potential against AD.

Naturally Inspired Coumarin Derivatives in Alzheimer's Disease Drug Discovery: Latest Advances and Current Challenges.

The main feature of neurodegenerative diseases, including Alzheimer's disease, is the network of complex and not fully recognized neuronal pathways and targets involved in their onset and progression. The therapeutic treatment, at present mainly symptomatic, could benefit from a polypharmacological approach based on the development of a single molecular entity designed to simultaneously modulate different validated biological targets. This strategy is principally based on molecular hybridization, obtained by linking or merging different chemical moieties acting with synergistic and/or complementary mechanisms. The coumarin core, widely found in nature, endowed with a recognized broad spectrum of pharmacological activities, large synthetic accessibility and favourable pharmacokinetic properties, appears as a valuable, privileged scaffold to be properly modified in order to obtain compounds able to engage different selected targets. The scientific literature has long been interested in the multifaceted profiles of coumarin derivatives, and in this review, a survey of the most important results of the last four years, on both natural and synthetic coumarin-based compounds, regarding the development of anti-Alzheimer's compounds is reported.

Comprehensive Overview of Alzheimer's Disease: Etiological Insights and Degradation Strategies.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and affects millions of individuals globally. AD is associated with cognitive decline and memory loss that worsens with aging. A statistical report using U.S. data on AD estimates that approximately 6.9 million individuals suffer from AD, a number projected to surge to 13.8 million by 2060. Thus, there is a critical imperative to pinpoint and address AD and its hallmark tau protein aggregation early to prevent and manage its debilitating effects. Amyloid-β and tau proteins are primarily associated with the formation of plaques and neurofibril tangles in the brain. Current research efforts focus on degrading amyloid-β and tau or inhibiting their synthesis, particularly targeting APP processing and tau hyperphosphorylation, aiming to develop effective clinical interventions. However, navigating this intricate landscape requires ongoing studies and clinical trials to develop treatments that truly make a difference. Genome-wide association studies (GWASs) across various cohorts identified 40 loci and over 300 genes associated with AD. Despite this wealth of genetic data, much remains to be understood about the functions of these genes and their role in the disease process, prompting continued investigation. By delving deeper into these genetic associations, novel targets such as kinases, proteases, cytokines, and degradation pathways, offer new directions for drug discovery and therapeutic intervention in AD. This review delves into the intricate biological pathways disrupted in AD and identifies how genetic variations within these pathways could serve as potential targets for drug discovery and treatment strategies. Through a comprehensive understanding of the molecular underpinnings of AD, researchers aim to pave the way for more effective therapies that can alleviate the burden of this devastating disease.

Deep learning-based drug screening for the discovery of potential therapeutic agents for Alzheimer's disease

Deep learning-based drug screening for the discovery of potential therapeutic agents for Alzheimer's disease

Aluminum chloride and D-galactose induced a zebrafish model of Alzheimer's disease with cognitive deficits and aging

Aluminum chloride and D-galactose induced a zebrafish model of Alzheimer's disease with cognitive deficits and aging

Anti-inflammatory and Neuroprotective α-Pyrones from a Marine-Derived Strain of the Fungus Arthrinium arundinis and Their Heterologous Expression.

Fungal linear polyketides, such as α-pyrones with a 6-alkenyl chain, have been a rich source of biologically active compounds. Two new (1 and 2) and four known (3-6) 6-alkenylpyrone polyketides were isolated from a marine-derived strain of the fungus Arthrinium arundinis. Their structures were determined based on extensive spectroscopic analysis. The biosynthetic gene cluster (alt) for alternapyrones was identified from A. arundinis ZSDS-F3 and validated by heterologous expression in Aspergillus nidulans A1145 ΔSTΔEM, which revealed that the cytochrome P450 monooxygenase Alt2' could convert the methyl group 26-CH3 to a carboxyl group to produce 4 from 3. Another cytochrome P450 monooxygenase, Alt3', catalyzed successive hydroxylation, epoxidation, and oxidation steps to produce 1, 2, 5, and 6 from 4. Alternapyrone G (1) not only suppressed M1 polarization in lipopolysaccharide (LPS)-stimulated BV2 microglia but also stimulated dendrite regeneration and neuronal survival after Aβ treatment, suggesting alternapyrone G may be utilized as a privileged scaffold for Alzheimer's disease drug discovery.

The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research.

As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease's etiology and response to drugs. We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics. We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base. AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones. AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.

Classifying Beta-Secretase 1 Inhibitor Activity for Alzheimer’s Drug Discovery with LightGBM

This study explores the utilization of LightGBM, a gradient-boosting framework, to classify the inhibitory activity of beta-secretase 1 inhibitors, addressing the challenges of Alzheimer's disease drug discovery. The study aims to enhance classification performance by focusing on overcoming the limitations of traditional statistical models and conventional machine-learning techniques in handling complex molecular datasets. By sourcing a dataset of 7298 compounds from the ChEMBL database and calculating molecular descriptors for each compound as features, we employed LightGBM in conjunction with a set of carefully selected molecular descriptors to achieve a nuanced analysis of compound activities. The model's efficiency was benchmarked against traditional machine-learning algorithms, revealing LightGBM's superior accuracy (84.93%), precision (87.14%), sensitivity (89.93%), specificity (77.63%), and F1-score (88.17%) in classifying beta-secretase 1 inhibitor activity. The study underscores the critical role of molecular descriptors in understanding drug efficacy, highlighting LightGBM's potential in streamlining the virtual screening process. Conclusively, the findings advocate for LightGBM's adoption in computational drug discovery, offering a promising avenue for advancing Alzheimer's disease therapeutic development by facilitating the identification of potential drug candidates with enhanced precision and reliability.

A Toxicogenic Interaction between Intracellular Amyloid-β and Apolipoprotein-E.

Alzheimer's disease (AD) is associated with the aggregation of amyloid β (Aβ) and tau proteins. Why ApoE variants are significant genetic risk factors remains a major unsolved puzzle in understanding AD, although intracellular interactions with ApoE are suspected to play a role. Here, we show that specific changes in the fluorescence lifetime of fluorescently tagged small Aβ oligomers in rat brain cells correlate with the cellular ApoE content. An inhibitor of the Aβ-ApoE interaction suppresses these changes and concomitantly reduces Aβ toxicity in a dose-dependent manner. Single-molecule techniques show changes both in the conformation and in the stoichiometry of the oligomers. Neural stem cells derived from hiPSCs of Alzheimer's patients also exhibit these fluorescence lifetime changes. We infer that intracellular interaction with ApoE modifies the N-terminus of the Aβ oligomers, inducing changes in their stoichiometry, membrane affinity, and toxicity. These changes can be directly imaged in live cells and can potentially be used as a rapid and quantitative cellular assay for AD drug discovery.

PET Imaging of Rho‐Associated Protein Kinase 2 in A Mouse Model of Alzheimer’s Disease

PET Imaging of Rho‐Associated Protein Kinase 2 in A Mouse Model of Alzheimer’s Disease

PET Imaging of Rho‐Associated Protein Kinase 2 in A Mouse Model of Alzheimer’s Disease

PET Imaging of Rho‐Associated Protein Kinase 2 in A Mouse Model of Alzheimer’s Disease

Brain-wide genome-wide colocalization study for integrating genetics, transcriptomics and brain morphometry in Alzheimer's disease

Brain-wide genome-wide colocalization study for integrating genetics, transcriptomics and brain morphometry in Alzheimer's disease

Failure, Success, and Future Direction of Alzheimer Drugs Targeting Amyloid-β Cascade: Pros and Cons of Chemical and Biological Modalities.

Alzheimer's disease (AD) is the most prevalent cause of dementia and has become a health concern worldwide urging for an effective therapeutic. The amyloid hypothesis, currently the most pursued basis of AD drug discovery, points the cause of AD to abnormal production and ineffective removal of pathogenic aggregated amyloid-β (Aβ). AD therapeutic research has been focused on targeting different species of Aβ in the amyloidogenic process to control Aβ content and recover cognitive decline. Among the different processes targeted, the clearance mechanism has been found to be the most effective, supported by the recent clinical approval of an Aβ-targeting immunotherapeutic drug which significantly slowed cognitive decline. Although the current AD drug discovery field is extensively researching immunotherapeutic drugs, there are numerous properties of immunotherapy in need of improvements that could be overcome by an equally performing chemical drug. Here, we review chemical and immunotherapy drug candidates, based on their mechanism of modulating the amyloid cascade, selected from the AlzForum database. Through this review, we aim to summarize and evaluate the prospect of Aβ-targeting chemical drugs.

Mechanistic and Etiological Similarities in Diabetes Mellitus and Alzheimer's Disease: Antidiabetic Drugs as Optimistic Therapeutics in Alzheimer's Disease.

Diabetes mellitus and Alzheimer's disease are two common diseases that majorly affect the elderly population. Patients in both cases are increasing day by day. They are considered two independent diseases, but recent evidence suggests that they have a lot in common. In this review, we focused on the connection between Alzheimer's disease and diabetes and highlighted the importance of antidiabetic drugs against Alzheimer's disease. Common pathways such as obesity, vascular diseases, oxidative stress, mitochondrial dysfunction, mutation of the ApoE4 gene, and Sirtuin gene were found to manipulate both diseases. Antidiabetic drugs are found to have promising effects on Alzheimer's disease, acting by reducing insulin resistance, neuronal protection, and reducing amyloid-beta plaques. Some anti-diabetic drugs have shown promising results in vivo and in vitro studies. No review present focuses on the structural features of the antidiabetic molecules against Alzheimer's disease, their crosslinking pathophysiology, the role of natural bioactive molecules, in silico advancements followed by preclinical and clinical studies, and current advancements. Hence, we concentrated on the factors mentioned in the objectives. Alzheimer's disease can be considered a form of 'type-3 diabetes,' and repurposing the anti-diabetic drug will open up new paths of research in the field of Alzheimer's disease drug discovery.

Bayesian bi-level variable selection for genome-wide survival study.

Mild cognitive impairment (MCI) is a clinical syndrome characterized by the onset and evolution of cognitive impairments, often considered a transitional stage to Alzheimer's disease (AD). The genetic traits of MCI patients who experience a rapid progression to AD can enhance early diagnosis capabilities and facilitate drug discovery for AD. While a genome-wide association study (GWAS) is a standard tool for identifying single nucleotide polymorphisms (SNPs) related to a disease, it fails to detect SNPs with small effect sizes due to stringent control for multiple testing. Additionally, the method does not consider the group structures of SNPs, such as genes or linkage disequilibrium blocks, which can provide valuable insights into the genetic architecture. To address the limitations, we propose a Bayesian bi-level variable selection method that detects SNPs associated with time of conversion from MCI to AD. Our approach integrates group inclusion indicators into an accelerated failure time model to identify important SNP groups. Additionally, we employ data augmentation techniques to impute censored time values using a predictive posterior. We adapt Dirichlet-Laplace shrinkage priors to incorporate the group structure for SNP-level variable selection. In the simulation study, our method outperformed other competing methods regarding variable selection. The analysis of Alzheimer's Disease Neuroimaging Initiative (ADNI) data revealed several genes directly or indirectly related to AD, whereas a classical GWAS did not identify any significant SNPs.