Harnessing omics data for drug discovery and development in ovarian aging

Human Reproduction Update, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 20, 2025

Ovarian aging occurs earlier than the of many other organs and has a lasting impact on women's overall health well-being. However, effective interventions to slow ovarian remain limited, primarily due an incomplete understanding underlying molecular mechanisms drug targets. Recent advances in omics data resources, combined with innovative computational tools, are offering deeper insight into complexities aging, paving way for new opportunities discovery development. This review aims synthesize expanding multi-omics data, spanning genome, transcriptome, proteome, metabolome, microbiome, related from both tissue-level single-cell perspectives. We will specially explore how analysis these emerging datasets can be leveraged identify novel targets guide therapeutic strategies slowing reversing aging. conducted comprehensive literature search PubMed database using range relevant keywords: age at natural menopause, premature insufficiency (POI), diminished reserve (DOR), genomics, transcriptomics, epigenomics, DNA methylation, RNA modification, histone proteomics, metabolomics, lipidomics, single-cell, genome-wide association studies (GWAS), whole-exome sequencing, phenome-wide (PheWAS), Mendelian randomization (MR), epigenetic target, machine learning, artificial intelligence (AI), deep multi-omics. The was restricted English-language articles published up September 2024. Multi-omics have uncovered key driving including damage repair deficiencies, inflammatory immune responses, mitochondrial dysfunction, cell death. By integrating researchers critical regulatory factors across various biological levels, leading potential Notable examples include genetic such as BRCA2 TERT, like Tet FTO, metabolic sirtuins CD38+, protein BIN2 PDGF-BB, transcription FOXP1. advent cutting-edge technologies, especially technologies spatial provided valuable insights guiding treatment decisions become powerful tool aimed mitigating or As technology advances, integration AI models holds more accurately predict candidate convergence offers promising avenues personalized medicine precision therapies, tailored Not applicable.

Language: Английский

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Clinical Importance of Amyloid Beta Implication in the Detection and Treatment of Alzheimer’s Disease

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(5), P. 1935 - 1935

Published: Feb. 24, 2025

The role of amyloid beta peptide (Aβ) in memory regulation has been a subject substantial interest and debate neuroscience, because both physiological clinical issues. Understanding the dual nature Aβ is crucial for developing effective treatments Alzheimer's disease (AD). Moreover, accurate detection quantification methods isoforms have tested diagnostic purposes therapeutic interventions. This review provides insight into current knowledge about vivo vitro by fluid tests brain imaging (PET), which allow preclinical recognition disease. Currently, priority development new therapies given to potential changes progression In light increasing amounts data, this was focused on employment

Language: Английский

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Instruction multi-constraint molecular generation using a teacher-student large language model

BMC Biology, Journal Year: 2025, Volume and Issue: 23(1)

Published: April 23, 2025

While various models and computational tools have been proposed for structure property analysis of molecules, generating molecules that conform to all desired structures properties remains a challenge. We introduce multi-constraint molecular generation large language model, TSMMG, which, akin student, incorporates knowledge from small tools, namely, the "teachers." To train we construct set text-molecule pairs by extracting these "teachers," enabling it generate novel descriptions through text prompts. experimentally show TSMMG remarkably performs in meet complex requirements described natural across two-, three-, four-constraint tasks, with an average validity over 99% success ratio 82.58%, 68.03%, 67.48%, respectively. The model also exhibits adaptability zero-shot testing, creating satisfy combinations not encountered. It can comprehend inputs styles, extending beyond confines outlined presents effective using language. This framework is only applicable drug discovery but serves as reference other related fields.

Language: Английский

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From past to future: Digital approaches to success of clinical drug trials for Parkinson's disease

Journal of Parkinson s Disease, Journal Year: 2025, Volume and Issue: unknown

Published: April 27, 2025

Recent years have seen successes in symptomatic drugs for Parkinson's disease, but the development of treatments stopping disease progression continues to fail clinical drug trials, largely due lack efficacy drugs. This may be related limited understanding mechanisms, data heterogeneity, poor target screening and candidate selection, challenges determining optimal dosage levels, reliance on animal models, insufficient patient participation, adherence trials. Most recent applications digital health technologies artificial intelligence (AI)-based tools focused mainly stages before used AI-based algorithms or models discover novel targets, inhibitors indications, recommend candidates dosage, promote remote collection. paper reviews state literature highlights strengths limitations approaches discovery from 2021 2024, offers recommendations future research practice success

Language: Английский

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Uncovering New Therapeutic Targets for Amyotrophic Lateral Sclerosis and Neurological Diseases Using Real‐World Data

Clinical Pharmacology & Therapeutics, Journal Year: 2025, Volume and Issue: unknown

Published: May 1, 2025

Although attractive for relevance to real-world scenarios, data (RWD) is typically used drug repurposing and not therapeutic target discovery. Repurposing studies have identified few effective options in neurological diseases such as the rare disease, amyotrophic lateral sclerosis (ALS), which has no disease-modifying treatments available. We previously reclassified drugs by their simulated effects on proteins downstream of targets observed class-level EHR, implicating protein source effect. Here, we developed a novel ALS-focused network medicine model using from patient samples, public domain, consortia. With this model, ALS measured class overall survival retrospective EHR studies. an increased but non-significant risk death patients taking with complement system experimentally validated activation. repeated six classes, three which, including multiple chemokine receptors, were associated significantly death, suggesting that targeting CXCR5, CXCR3, signaling generally, or neuropeptide Y (NPY) could be advantageous these patients. expanded our analysis neuroinflammatory condition, myasthenia gravis, neurodegenerative Parkinson's, recovered similar effect sizes. demonstrated utility testing RWD believe approach may accelerate discovery diseases, addressing critical need new options.

Language: Английский

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Recent advances in the potential of Phyllanthus emblica L. and its related foods for combating metabolic diseases through methylglyoxal trapping

Food Research International, Journal Year: 2024, Volume and Issue: 194, P. 114907 - 114907

Published: Aug. 11, 2024

Language: Английский

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Use of Artificial Intelligence in Imaging Dementia

Cells, Journal Year: 2024, Volume and Issue: 13(23), P. 1965 - 1965

Published: Nov. 27, 2024

Alzheimer’s disease is the most common cause of dementia in elderly population (aged 65 years and over), followed by vascular dementia, Lewy body rare types neurodegenerative diseases, including frontotemporal dementia. There an unmet need to improve diagnosis prognosis for patients with as cycles misdiagnosis diagnostic delays are challenging scenarios diseases. Neuroimaging routinely used clinical practice support Clinical neuroimaging amenable errors owing varying human judgement imaging data complex multidimensional. Artificial intelligence algorithms (machine learning deep learning) enable automation interpretation may reduce potential bias ameliorate decision-making. Graph convolutional network-based frameworks implicitly provide multimodal sparse interpretability detection its prodromal stage, mild cognitive impairment. In amyloid-related abnormalities, radiologists had significantly better performances both ARIA-E (sensitivity higher assisted/deep method [87%] compared unassisted [71%]) ARIA-H signs was assisted [79%] [69%]). A neural network developed, external validation predicted final diagnoses disease, bodies, impairment due or cognitively normal FDG-PET. The translation artificial plagued technical, disease-related, institutional challenges. implementation methods has transform treatment landscape patient health outcomes.

Language: Английский

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DeepDrug: An Expert-led Domain-specific AI-Driven Drug-Repurposing Mechanism for Selecting the Lead Combination of Drugs for Alzheimer’s Disease

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: July 7, 2024

Abstract Alzheimer’s Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, effective AD drugs remain to be identified. Here, we propose a novel AI-driven drug-repurposing method, DeepDrug, identify lead combination treat patients. DeepDrug advances methodology in four aspects. Firstly, it incorporates expert knowledge extend candidate targets include long genes, immunological aging pathways, somatic mutation markers that are associated with AD. Secondly, signed directed heterogeneous biomedical graph encompassing rich set nodes edges, node/edge weighting capture crucial pathways Thirdly, encodes the weighted through Graph Neural Network into new embedding space granular relationships across different nodes. Fourthly, systematically selects high-order drug combinations via diminishing return-based thresholds. A five-drug combination, consisting Tofacitinib, Niraparib, Baricitinib, Empagliflozin, Doxercalciferol, selected from top candidates based on scores achieve maximum synergistic effect. These five target neuroinflammation, mitochondrial dysfunction, glucose metabolism, which all related pathology. offers AI-and-big-data, expert-guided mechanism for discovery other neuro-degenerative diseases, immediate clinical applications.

Language: Английский

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Advancing Alzheimer's Disease Detection With Big Data and Machine Learning

Advances in bioinformatics and biomedical engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 241 - 264

Published: Nov. 1, 2024

Alzheimer's disease (AD) detection and diagnosis face challenges due to its complexity. This study explores the fusion of advanced machine learning algorithms big data methods improve accuracy. In addition commonly used like Random Forest Support Vector Machines, introduces Gradient Boosting Decision Trees (GBDT) for AD prediction. GBDT combines strength multiple weak learners enhance predictive performance. Furthermore, implements techniques such as parallelization distributed computing handle large-scale datasets efficiently. By leveraging these methods, achieves a significant improvement in computational efficiency, enabling timely analysis extensive AD-related data. Results show that algorithm outperforms traditional achieving an accuracy 85% predicting onset progression. When combined with techniques, overall further increases 88%.

Language: Английский

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Letter to the Editor: “AI and ML in Alzheimer's disease: Transforming early detection and drug development”

Brain and Spine, Journal Year: 2024, Volume and Issue: 4, P. 102847 - 102847

Published: Jan. 1, 2024

Language: Английский

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