Recent advances in Alzheimer’s disease: Mechanisms, clinical trials and new drug development strategies

Signal Transduction and Targeted Therapy, Journal Year: 2024, Volume and Issue: 9(1)

Published: Aug. 23, 2024

Abstract Alzheimer’s disease (AD) stands as the predominant form of dementia, presenting significant and escalating global challenges. Its etiology is intricate diverse, stemming from a combination factors such aging, genetics, environment. Our current understanding AD pathologies involves various hypotheses, cholinergic, amyloid, tau protein, inflammatory, oxidative stress, metal ion, glutamate excitotoxicity, microbiota-gut-brain axis, abnormal autophagy. Nonetheless, unraveling interplay among these pathological aspects pinpointing primary initiators require further elucidation validation. In past decades, most clinical drugs have been discontinued due to limited effectiveness or adverse effects. Presently, available primarily offer symptomatic relief often accompanied by undesirable side However, recent approvals aducanumab ( 1 ) lecanemab 2 Food Drug Administration (FDA) present potential in disrease-modifying Nevertheless, long-term efficacy safety need Consequently, quest for safer more effective persists formidable pressing task. This review discusses pathogenesis, advances diagnostic biomarkers, latest updates trials, emerging technologies drug development. We highlight progress discovery selective inhibitors, dual-target allosteric modulators, covalent proteolysis-targeting chimeras (PROTACs), protein-protein interaction (PPI) modulators. goal provide insights into prospective development application novel drugs.

Language: Английский

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Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis

International Journal of Surgery, Journal Year: 2024, Volume and Issue: unknown

Published: March 19, 2024

Computer-aided drug design (CADD) is a technique for computing ligand-receptor interactions and involved in various stages of development. To better grasp the frontiers hotspots CADD, we conducted review analysis through bibliometrics.

Language: Английский

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Advanced AI Applications for Drug Discovery

Advances in medical technologies and clinical practice book series, Journal Year: 2024, Volume and Issue: unknown, P. 42 - 86

Published: April 26, 2024

Addressing the critical challenge of lengthy and costly drug development, this chapter illuminates transformative role advanced artificial intelligence (AI) in discovery. It aims to dissect impact AI methodologies streamlining these traditionally complex processes. This begins by highlighting inefficiencies conventional discovery methods, emphasizing their resource-intensive nature. An in-depth discussion how technologies are revolutionizing identification novel targets, optimizing molecular structures candidates, accurately predicting efficacy toxicity is needed. exploration underscores AI's dual advantages: significantly reducing development timelines expenses while simultaneously enhancing precision predictions, leading safer more effective drugs. concludes with a vision future where AI-driven methods fully integrated personalized medicine genomics, signaling onset new era healthcare therapeutic innovation.

Language: Английский

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dyphAI dynamic pharmacophore modeling with AI: a tool for efficient screening of new acetylcholinesterase inhibitors

Frontiers in Chemistry, Journal Year: 2025, Volume and Issue: 13

Published: Feb. 4, 2025

Therapeutic strategies for Alzheimer’s disease (AD) often involve inhibiting acetylcholinesterase (AChE), underscoring the need novel inhibitors with high selectivity and minimal side effects. A detailed analysis of protein-ligand pharmacophore dynamics can facilitate this. In this study, we developed employed dyphAI , an innovative approach integrating machine learning models, ligand-based complex-based models into a model ensemble. This ensemble captures key interactions, including π-cation interactions Trp-86 several π-π residues Tyr-341, Tyr-337, Tyr-124, Tyr-72. The protocol identified 18 molecules from ZINC database binding energy values ranging −62 to −115 kJ/mol, suggesting their strong potential as AChE inhibitors. To further validate predictions, nine were acquired tested inhibitory activity against human AChE. Experimental results revealed that molecules, 4 (P-1894047), its complex multi-ring structure numerous hydrogen bond acceptors, 7 (P-2652815), characterized by flexible, polar framework ten donors exhibited IC₅₀ lower than or equal control (galantamine), indicating potent activity. Similarly, 5 (P-1205609), 6 (P-1206762), 8 (P-2026435), 9 (P-533735) also demonstrated inhibition. contrast, molecule 3 (P-617769798) showed higher IC 50 value, 1 (P-14421887) 2 (P-25746649) yielded inconsistent results, likely due solubility issues in experimental setup. These findings underscore value computational predictions validation, enhancing reliability virtual screening discovery enzyme

Language: Английский

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Topological influence of immediate-early genes in brain genetic networks and their link to Alzheimer's disease

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 190, P. 110043 - 110043

Published: March 30, 2025

Language: Английский

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ML-Driven Metabolomics Predicts Anti-Inflammatory and Antioxidant Activities in D. officinale Leaves

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 18, 2025

Background Dendrobium officinale (D. officinale) leaves, rich in bioactive compounds comparable to those stems, remain underutilized as agricultural byproducts. Purpose This study aims establish an ML (machine learning)-driven metabolomic framework evaluate seasonal variations within D. identify germplasm-specific pharmacological activities, and determine core components driving anti-inflammatory antioxidant effects. Methods An integrated approach combining dynamic profiling (UHPLC-QTOF-MS, RP-HPLC, UPLC-QqQ-MS), vitro bioassays (TNF-α/IL-6 suppression assays ABTS radical scavenging assay), modeling was employed. Results Phenolics, flavonoids, terpenes, B-vitamins peaked October–November, while amino acids accumulated until December. Despite this, July-harvested leaves exhibited maximum activity. Random Forest Regression (RFR) models identified vanillic acid 4-β-D-glucoside, schaftoside, rutin key contributors, validated experimentally. Conclusion ML-enhanced strategy advances the quality assessment germplasm optimization of by linking phytochemical profiles bioactivity. The identification July optimal harvest period critical underscores approach’s utility nutraceutical pharmaceutical applications, promoting sustainable utilization

Language: Английский

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Plasma proteomics for risk prediction of Alzheimer's disease in the general population

Aging Cell, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 9, 2024

Abstract We aimed to develop and validate a protein risk score for predicting Alzheimer's disease (AD) compare its performance with validated clinical model (Cognitive Health Dementia Risk Index AD [CogDrisk‐AD]) apolipoprotein E (APOE) genotypes. The development cohort, consisting of 35,547 participants from England in the UK Biobank, was randomly divided into 7:3 training–testing ratio. validation cohort included 4667 Scotland Wales Biobank. In training set, an constructed using 31 proteins out 2911 proteins. testing had C‐index 0.867 (95% CI, 0.828, 0.906) prediction, followed by CogDrisk‐AD factors (C‐index, 0.856; 95% 0.823, 0.889), APOE genotypes 0.705; 0.660, 0.750). Adding (C‐index increase, 0.050; 0.008, 0.093) significantly improved predictive AD. However, adding 0.040; −0.007, 0.086) or 0.000; −0.054, 0.055) did not improve top 10 highest coefficients contributed most power risk. These results were verified external cohort. EGFR, GFAP, CHGA identified as key within network. Our result suggests that demonstrated good

Language: Английский

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Integrated multi-omics analysis revealed the molecular networks and potential targets of cellular senescence in Alzheimer’s disease

Human Molecular Genetics, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 18, 2024

Cellular senescence (CS) is a hallmark of Alzheimer's disease (AD). However, the mechanisms through which CS contributes to AD pathogenesis remain poorly understood. We found that level in was higher compared with healthy control group. Transcriptome-based differential expression analysis identified 113 CS-related genes blood and 410 brain tissue as potential candidate involved AD. To further explore causal role these genes, an integrative mendelian randomization conducted, combining genome-wide association study summary statistics quantitative trait loci (eQTL) DNA methylation (mQTL) data from samples, five putative AD-causal (CENPW, EXOSC9, HSPB11, SLC44A2, SLFN12) 18 corresponding probes. Additionally, between eQTLs mQTLs uncovered two 12 regulatory elements Furthermore, (CDKN2B ITGAV) were prioritized validated vitro experiments. The multi-omics integration revealed underlying biological driven by genetic predisposition This contributed fundamental understanding facilitated identification therapeutic targets for prevention treatment.

Language: Английский

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Detailing the biomedical aspects of geroscience by molecular data and large-scale “deep” bioinformatics analyses

Zeitschrift für Gerontologie und Geriatrie, Journal Year: 2024, Volume and Issue: 57(5), P. 355 - 360

Published: Aug. 1, 2024

Language: Английский

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The integration of machine learning into traditional Chinese medicine

Journal of Pharmaceutical Analysis, Journal Year: 2024, Volume and Issue: unknown, P. 101157 - 101157

Published: Dec. 1, 2024

Language: Английский

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