Alzheimer's Disease Drug Discovery Research Articles

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

Alzheimer’s disease (AD) is gradually increasing in prevalence and the complexity of its pathogenesis has led to a lengthy process of developing therapeutic drugs with limited success. Faced with this challenge, we proposed using a state-of-the-art drug screening algorithm to identify potential therapeutic compounds for AD from traditional Chinese medicine formulas with strong empirical support. We developed four deep neural network models for AD drugs screening at the disease and target levels. The AD model was trained with compounds labeled for AD activity to predict active compounds at the disease level, while the acetylcholinesterase (AChE), monoamine oxidase-A (MAO-A), and 5-hydroxytryptamine receptor 6 models were trained for specific AD targets. All four models performed excellently and were used to identify potential AD agents in the Kaixinsan formula. High-scoring compounds underwent experimental validation at the enzyme, cellular, and animal levels. Compounds like 2,4-di-tert-butylphenol and elemicin showed significant binding and inhibitory effects on AChE and MAO-A. Additionally, 13 compounds, including α-asarone, penetrated the blood-brain barrier, indicating potential brain target binding, and eight compounds enhanced microglial β-amyloid phagocytosis, aiding in clearing AD pathological substances. Our results demonstrate the effectiveness of deep learning models in developing AD therapies and provide a strong platform for AD drug discovery.

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

Alzheimer’s disease (AD) is an age-related neurodegenerative disorder. Transgenic and pharmacological AD models are extensively studied to understand AD mechanisms and drug discovery. However, they are time-consuming and relatively costly, which hinders the discovery of potential anti-AD therapeutics. Here, we established a new model of AD in larval zebrafish by co-treatment with aluminum chloride (AlCl3) and D-galactose (D-gal) for 72 h. In particular, exposure to 150 μM AlCl3 + 40 mg/mL D-gal, 200 μM AlCl3 + 30 mg/mL D-gal, or 200 μM AlCl3 + 40 mg/mL D-gal successfully induced AD-like symptoms and aging features. Co-treatment with AlCl3 and D-gal caused significant learning and memory deficits, as well as impaired response ability and locomotor capacity in the plus-maze and light/dark test. Moreover, increased acetylcholinesterase and β-galactosidase activities, β-amyloid 1–42 deposition, reduced telomerase activity, elevated interleukin 1 beta mRNA expression, and enhanced reactive oxygen species production were also observed. In conclusion, our zebrafish model is simple, rapid, effective and affordable, incorporating key features of AD and aging, thus may become a unique and powerful tool for high-throughput screening of anti-AD compounds in vivo.

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

AbstractBackgroundRho‐associated protein kinase 2 (ROCK2) activity is implicated in Alzheimer’s disease (AD) and other central nervous system (CNS) disorders 1. In this study, we investigated the relative expression levels of ROCK2 in APP/PS1 transgenic AD mice and age‐matched wild‐type (WT) C57BL/6J mice using the ROCK2‐specific PET radioligand, [11C]ROCK201.MethodsThe ROCK2 tracer [11C]ROCK201 was synthesized according to the previously published procedure 2. PET scans were performed in a Focus‐220 scanner on 12‐month‐old APP/PS1 transgenic mice (n = 8) and WT controls (n = 5). Self‐blocking scans were performed 10 min post injection (p.i.) of the unlabeled ROCK201 (i.v., 3.3 mg/kg). Regions of interest (ROIs) were extracted from the Ma‐Benveniste‐Mirrione mouse brain atlas and regional time‐activity curves (TACs) were obtained by applying template ROIs to the averaged PET images (0 to 60 min p.i.). Distribution volume ratio (DVR) values were generated using the simplified reference tissue model 2 (SRTM2) on the 60‐min dynamic PET imaging data, with cerebellum (CB) as the reference region. ROCK2 protein expression levels were assessed immunohistochemically in brain tissues of 12‐month‐old APP/PS1 (N = 3) and WT (N = 3) mice.ResultsUptake of [11C]ROCK201 was observed throughout the mouse brain, with peak SUV of 2.6. TACs were consistently higher in the hippocampus of APP/PS1 mice than controls, within 20 min p.i. (Fig. 1a). Reduced uptake was observed in self‐blocking studies, indicating specific binding of [11C]ROCK201 in the rodent brain (Fig. 1a). TACs were well fitted with SRTM2. Compared with age‐matched WT controls, the DVR(CB) in the hippocampus of APP/PS1 mice was 9.3% higher than that of WT mice (p = 0.03) (Fig. 1b). Consistent with PET imaging results, ROCK2 protein expression is elevated by 49±2% in the CA1 of APP/PS1 mice compared to that of WT mice (Fig. 1c).ConclusionsWe demonstrated the uptake and detection sensitivity of the radioligand [11C]ROCK201 in the mouse brain, with higher tracer uptake in the hippocampi of APP/PS1 mice as compared to WT mice, supporting the use of [11C]ROCK201 in preclinical AD drug discovery and development.

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

AbstractBackgroundRho‐associated protein kinase 2 (ROCK2) activity is implicated in Alzheimer’s disease (AD) and other central nervous system (CNS) disorders 1. In this study, we investigated the relative expression levels of ROCK2 in APP/PS1 transgenic AD mice and age‐matched wild‐type (WT) C57BL/6J mice using the ROCK2‐specific PET radioligand, [11C]ROCK201.MethodThe ROCK2 tracer [11C]ROCK201 was synthesized according to the previously published procedure 2. PET scans were performed in a Focus‐220 scanner on 12‐month‐old APP/PS1 transgenic mice (n = 8) and WT controls (n = 5). Self‐blocking scans were performed 10 min post injection (p.i.) of the unlabeled ROCK201 (i.v., 3.3 mg/kg). Regions of interest (ROIs) were extracted from the Ma‐Benveniste‐Mirrione mouse brain atlas and regional time‐activity curves (TACs) were obtained by applying template ROIs to the averaged PET images (0 to 60 min p.i.). Distribution volume ratio (DVR) values were generated using the simplified reference tissue model 2 (SRTM2) on the 60‐min dynamic PET imaging data, with cerebellum (CB) as the reference region. ROCK2 protein expression levels were assessed immunohistochemically in brain tissues of 12‐month‐old APP/PS1 (N = 3) and WT (N = 3) mice.ResultUptake of [11C]ROCK201 was observed throughout the mouse brain, with peak SUV of 2.6. TACs were consistently higher in the hippocampus of APP/PS1 mice than controls, within 20 min p.i. (Fig. 1a). Reduced uptake was observed in self‐blocking studies, indicating specific binding of [11C]ROCK201 in the rodent brain (Fig. 1a). TACs were well fitted with SRTM2. Compared with age‐matched WT controls, the DVR(CB) in the hippocampus of APP/PS1 mice was 9.3% higher than that of WT mice (p = 0.03) (Fig. 1b). Consistent with PET imaging results, ROCK2 protein expression is elevated by 49±2% in the CA1 of APP/PS1 mice compared to that of WT mice (Fig. 1c).ConclusionWe demonstrated the uptake and detection sensitivity of the radioligand [11C]ROCK201 in the mouse brain, with higher tracer uptake in the hippocampi of APP/PS1 mice as compared to WT mice, supporting the use of [11C]ROCK201 in preclinical AD drug discovery and development.

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

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. However, the AD mechanism has not yet been fully elucidated to date, hindering the development of effective therapies. In our work, we perform a brain imaging genomics study to link genetics, single-cell gene expression data, tissue-specific gene expression data, brain imaging-derived volumetric endophenotypes, and disease diagnosis to discover potential underlying neurobiological pathways for AD. To do so, we perform brain-wide genome-wide colocalization analyses to integrate multidimensional imaging genomic biobank data. Specifically, we use (1) the individual-level imputed genotyping data and magnetic resonance imaging (MRI) data from the UK Biobank, (2) the summary statistics of the genome-wide association study (GWAS) from multiple European ancestry cohorts, and (3) the tissue-specific cis-expression quantitative trait loci (cis-eQTL) summary statistics from the GTEx project. We apply a Bayes factor colocalization framework and mediation analysis to these multi-modal imaging genomic data. As a result, we derive the brain regional level GWAS summary statistics for 145 brain regions with 482,831 single nucleotide polymorphisms (SNPs) followed by posthoc functional annotations. Our analysis yields the discovery of a potential AD causal pathway from a systems biology perspective: the SNP chr10:124165615:G>A (rs6585827) mutation upregulates the expression of BTBD16 gene in oligodendrocytes, a specialized glial cells, in the brain cortex, leading to a reduced risk of volumetric loss in the entorhinal cortex, resulting in the protective effect on AD. We substantiate our findings with multiple evidence from existing imaging, genetic and genomic studies in AD literature. Our study connects genetics, molecular and cellular signatures, regional brain morphologic endophenotypes, and AD diagnosis, providing new insights into the mechanistic understanding of the disease. Our findings can provide valuable guidance for subsequent therapeutic target identification and drug discovery in AD.

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.

Inhibition of CXXC5 function rescues Alzheimer’s disease phenotypes by restoring Wnt/β-catenin signaling pathway

Alzheimer’s disease (AD) is the most prevalent type of dementia and is characterized by cognitive deficits and accumulation of pathological plaques. Owing to the complexity of AD development, paradigms for AD research and drug discovery have shifted to target factors that mediate multiple pathogenesis in AD. Increasing evidence suggests that the suppression of the Wnt/β-catenin signaling pathway plays substantial roles in AD progression. However, the underlying mechanism for the suppression of Wnt/β-catenin pathway associated with AD pathogenesis remains unexplored. In this study, we identified that CXXC5, a negative feedback regulator of the Wnt/β-catenin pathway, was overexpressed in the tissues of AD patients and 5xFAD transgenic mice paired with the suppression of Wnt/β-catenin pathway and its target genes related to AD. The level of CXXC5 was upregulated, upon aging of 5xFAD mice. AD characteristics including cognitive deficits, amyloid-β (Aβ) plaques, neuronal inflammation, and age-dependent increment of AD-related markers were rescued in Cxxc5-/-/5xFAD mice. 5-methoxyindirubin-3'-oxime (KY19334), a small molecule that restores the suppressed Wnt/β-catenin pathway via interference of the CXXC5-Dvl interaction, significantly improved the overall pathogenic phenotypes of 5xFAD mice. Collectively, our findings revealed that CXXC5 plays a key role in AD pathogenesis and suggest inhibition of CXXC5-Dvl interaction as a new therapeutic approach for AD.

QSAR Classification of Beta-Secretase 1 Inhibitor Activity in Alzheimer's Disease Using Ensemble Machine Learning Algorithms

This study focuses on the development of a machine learning ensemble approach for the classification of Beta-Secretase 1 (BACE1) inhibitors in Quantitative Structure-Activity Relationship (QSAR) analysis. BACE1 is an enzyme linked to the production of amyloid beta peptide, a significant component of Alzheimer's disease plaques. The discovery of effective BACE1 inhibitors is difficult, but QSAR modeling offers a cost-effective alternative by predicting the activity of compounds based on their chemical structures. This study evaluates the performance of four machine learning models (Random Forest, AdaBoost, Gradient Boosting, and Extra Trees) in predicting BACE1 inhibitor activity. Random Forest achieved the highest performance, with a training accuracy of 98.65% and a testing accuracy of 82.53%. In addition, it exhibited superior precision, recall, and F1-score. Random Forest's superior performance was a result of its ability to capture a wide variety of patterns and its randomized ensemble approach. Overall, this study demonstrates the efficacy of ensemble machine learning models, specifically Random Forest, in predicting the activity of BACE1 inhibitors. The findings contribute to ongoing efforts in Alzheimer's disease drug discovery research by providing a cost-effective and efficient strategy for screening and prioritizing potential BACE1 inhibitors.

Real-Time Observation for Dynamic Oscillation during the Self-Assembly and Clearance of Aβ42.

The dynamic oscillation implicated in structural heterogeneity during the self-assembly of amyloid peptide 1-42 (Aβ42) may play a crucial role in eliciting cellular responses. We developed a real-time monitoring platform to observe an oscillatory non-equilibrium interaction that dominated the Aβ42 clearance by neuronal cells during interplay with an oscillator (lipopolysaccharide, LPS). Molecular dynamics studies indicated that the electrostatic and hydrophobic segments of LPS involved in the temporary heteromolecular association and slightly decelerated the intrinsic thermally-induced protein dynamics of Aβ42. A bait-specific intervention strategy could temporarily slow down the self-propagation of Aβ42 to extend the lifetime of autonomous oscillation and augment the Aβ42 clearance of neuronal cells. The lifetime increment of dynamic oscillation shows a bait concentration-dependent manner to reflect the non-equilibrium binding strength. This relationship may serve as a drug-screening predictor for Alzheimer's disease drug discovery.

Expression, purification and micelle reconstruction of the transmembrane domain of the human amyloid precursor protein for NMR studies

The multiple-step cleavage of amyloid precursor protein (APP) generates amyloid-β peptides (Aβ), highly toxic molecules causing Alzheimer's disease (AD). The nonspecific cleavage between the transmembrane region of APP (APPTM) and γ-secretase is the key step of Aβ generation. Reconstituting APPTM under physiologically-relevant conditions is crucial to investigate how it interacts with γ-secretase and for future AD drug discovery. Although producing recombinant APPTM was reported before, the large scale purification was hindered by the use of biological protease in the presence of membrane protein. Here, we expressed recombinant APPTM in Escherichia coli using the pMM-LR6 vector and recovered the fusion protein from inclusion bodies. By combining Ni-NTA chromatography, cyanogen bromide cleavage, and reverse phase high performance liquid chromatography (RP-HPLC), isotopically-labeled APPTM was obtained in high yield and high purity. The reconstitution of APPTM into dodecylphosphocholine (DPC) micelle generated mono dispersed 2D 15N-1H HSQC spectra in high quality. We successfully established an efficient and reliable method for the expression, purification and reconstruction of APPTM, which may facilitate future investigation of APPTM and its complex in more native like membrane mimetics such as bicelle and nanodiscs.

Recent Development in the Understanding of Molecular and Cellular Mechanisms Underlying the Etiopathogenesis of Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that leads to dementia and patient death. AD is characterized by intracellular neurofibrillary tangles, extracellular amyloid beta (Aβ) plaque deposition, and neurodegeneration. Diverse alterations have been associated with AD progression, including genetic mutations, neuroinflammation, blood-brain barrier (BBB) impairment, mitochondrial dysfunction, oxidative stress, and metal ion imbalance.Additionally, recent studies have shown an association between altered heme metabolism and AD. Unfortunately, decades of research and drug development have not produced any effective treatments for AD. Therefore, understanding the cellular and molecular mechanisms underlying AD pathology and identifying potential therapeutic targets are crucial for AD drug development. This review discusses the most common alterations associated with AD and promising therapeutic targets for AD drug discovery. Furthermore, it highlights the role of heme in AD development and summarizes mathematical models of AD, including a stochastic mathematical model of AD and mathematical models of the effect of Aβ on AD. We also summarize the potential treatment strategies that these models can offer in clinical trials.

Open drug discovery in Alzheimer's disease.

Alzheimer's disease (AD) drug discovery has focused on a set of highly studied therapeutic hypotheses, with limited success. The heterogeneous nature of AD processes suggests that a more diverse, systems-integrated strategy may identify new therapeutic hypotheses. Although many target hypotheses have arisen from systems-level modeling of human disease, in practice and for many reasons, it has proven challenging to translate them into drug discovery pipelines. First, many hypotheses implicate protein targets and/or biological mechanisms that are under-studied, meaning there is a paucity of evidence to inform experimental strategies as well as high-quality reagents to perform them. Second, systems-level targets are predicted to act in concert, requiring adaptations in how we characterize new drug targets. Here we posit that the development and open distribution of high-quality experimental reagents and informatic outputs-termed target enabling packages (TEPs)-will catalyze rapid evaluation of emerging systems-integrated targets in AD by enabling parallel, independent, and unencumbered research.

In silico and in vitro analyses of a novel FoxO1 agonist reducing Aβ levels via downregulation of BACE1.

FoxO1 is an important target in the treatment of Alzheimer's disease (AD). However, FoxO1-specific agonists and their effects on AD have not yet been reported. This study aimed to identify small molecules that upregulate the activity of FoxO1 to attenuate the symptoms of AD. FoxO1 agonists were identified by in silico screening and molecular dynamics simulation. Western blotting and reverse transcription-quantitative polymerase chain reaction assays were used to assess protein and gene expression levels of P21, BIM, and PPARγ downstream of FoxO1 in SH-SY5Y cells, respectively. Western blotting and enzyme-linked immunoassays were performed to explore the effect of FoxO1 agonists on APP metabolism. N-(3-methylisothiazol-5-yl)-2-(2-oxobenzo[d]oxazol-3(2H)-yl) acetamide (compound D) had the highest affinity for FoxO1. Compound D activated FoxO1 and regulated the expression of its downstream target genes, P21, BIM, and PPARγ. In SH-SY5Y cells treated with compound D, BACE1 expression levels were downregulated, and the levels of Aβ1-40 and Aβ1-42 were also reduced. We present a novel small-molecule FoxO1 agonist with good anti-AD effects. This study highlights a promising strategy for new drug discovery for AD.