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

Signal Transduction and Targeted Therapy, Год журнала: 2024, Номер 9(1)

Опубликована: Авг. 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.

Язык: Английский

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AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring

Biosensors, Год журнала: 2024, Номер 14(7), С. 356 - 356

Опубликована: Июль 22, 2024

The steady progress in consumer electronics, together with improvement microflow techniques, nanotechnology, and data processing, has led to implementation of cost-effective, user-friendly portable devices, which play the role not only gadgets but also diagnostic tools. Moreover, numerous smart devices monitor patients' health, some them are applied point-of-care (PoC) tests as a reliable source evaluation patient's condition. Current practices still based on laboratory tests, preceded by collection biological samples, then tested clinical conditions trained personnel specialistic equipment. In practice, collecting passive/active physiological behavioral from patients real time feeding artificial intelligence (AI) models can significantly improve decision process regarding diagnosis treatment procedures via omission conventional sampling while excluding pathologists. A combination novel methods digital traditional biomarker detection portable, autonomous, miniaturized revolutionize medical diagnostics coming years. This article focuses comparison modern techniques AI machine learning (ML). presented technologies will bypass laboratories start being commercialized, should lead or substitution current Their application PoC settings technology accessible every patient appears be possibility. Research this field is expected intensify Technological advancements sensors biosensors anticipated enable continuous real-time analysis various omics fields, fostering early disease intervention strategies. integration health platforms would predictive personalized healthcare, emphasizing importance interdisciplinary collaboration related scientific fields.

Язык: Английский

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AI-Driven Innovations in Alzheimer's Disease: Integrating Early Diagnosis, Personalized Treatment, and Prognostic Modelling

Ageing Research Reviews, Год журнала: 2024, Номер unknown, С. 102497 - 102497

Опубликована: Сен. 1, 2024

Язык: Английский

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Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications

Journal of Clinical Medicine, Год журнала: 2025, Номер 14(2), С. 550 - 550

Опубликована: Янв. 16, 2025

The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding the brain, unlocking new possibilities in research, diagnosis, therapy. This review explores how AI’s cutting-edge algorithms—ranging from deep learning to neuromorphic computing—are revolutionizing by enabling analysis complex neural datasets, neuroimaging electrophysiology genomic profiling. These advancements are transforming early detection neurological disorders, enhancing brain–computer interfaces, driving personalized medicine, paving way for more precise adaptive treatments. Beyond applications, itself has inspired AI innovations, with architectures brain-like processes shaping advances algorithms explainable models. bidirectional exchange fueled breakthroughs such as dynamic connectivity mapping, real-time decoding, closed-loop systems that adaptively respond states. However, challenges persist, including issues data integration, ethical considerations, “black-box” nature many systems, underscoring need transparent, equitable, interdisciplinary approaches. By synthesizing latest identifying future opportunities, this charts a path forward integration neuroscience. From harnessing multimodal cognitive augmentation, fusion these fields not just brain science, it reimagining human potential. partnership promises where mysteries unlocked, offering unprecedented healthcare, technology, beyond.

Язык: Английский

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Artificial intelligence for drug discovery and development in Alzheimer's disease

Current Opinion in Structural Biology, Год журнала: 2024, Номер 85, С. 102776 - 102776

Опубликована: Фев. 8, 2024

The complex molecular mechanism and pathophysiology of Alzheimer's disease (AD) limits the development effective therapeutics or prevention strategies. Artificial Intelligence (AI)-guided drug discovery combined with genetics/multi-omics (genomics, epigenomics, transcriptomics, proteomics, metabolomics) analysis contributes to understanding precision medicine disease, including AD AD-related dementia. In this review, we summarize AI-driven methodologies for AD-agnostic development, de novo design, virtual screening, prediction drug-target interactions, all which have shown potentials. particular, AI-based repurposing emerges as a compelling strategy identify new indications existing drugs AD. We provide several emerging targets from human genetics multi-omics findings highlight recent technologies their applications in using prototypical example. closing, discuss future challenges directions other neurodegenerative diseases.

Язык: Английский

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Advances in blood biomarkers for Alzheimer disease (AD): A review

The Kaohsiung Journal of Medical Sciences, Год журнала: 2024, Номер 40(8), С. 692 - 698

Опубликована: Июнь 18, 2024

Abstract Alzheimer disease (AD) and Disease Related Dementias (AD/ADRD) are growing public health challenges globally affecting millions of older adults, necessitating concerted efforts to advance our understanding management these conditions. AD is a progressive neurodegenerative disorder characterized pathologically by amyloid plaques tau neurofibrillary tangles that the primary cause dementia in individuals. Early accurate diagnosis crucial for effective intervention treatment but has proven challenging accomplish. Although testing brain pathology with cerebrospinal fluid (CSF) or positron emission tomography (PET) been available over 2 decades, most patients never underwent this because inaccessibility, high out‐of‐pocket costs, perceived risks, lack AD‐specific treatments. However, recent years, rapid progress made developing blood biomarkers AD/ADRD. Consequently, have emerged as promising tools non‐invasive cost‐effective diagnosis, prognosis, monitoring progression. This review presents evolving landscape AD/ADRD explores their potential applications clinical practice early detection, therapeutic interventions. It covers advances biomarkers, including beta (Aβ) peptides, protein, neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP). also discusses diagnostic prognostic utility while addressing associated limitations. Future research directions rapidly field proposed.

Язык: Английский

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Monitoring synaptic pathology in Alzheimer’s disease through fluid and PET imaging biomarkers: a comprehensive review and future perspectives

Molecular Psychiatry, Год журнала: 2024, Номер 29(3), С. 847 - 857

Опубликована: Янв. 16, 2024

Язык: Английский

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Artificial intelligence for neurodegenerative experimental models

Alzheimer s & Dementia, Год журнала: 2023, Номер 19(12), С. 5970 - 5987

Опубликована: Сен. 28, 2023

Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered model systems has proven immensely challenging, marred by high failure rates human clinical trials.

Язык: Английский

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Artificial intelligence for dementia prevention

Alzheimer s & Dementia, Год журнала: 2023, Номер 19(12), С. 5952 - 5969

Опубликована: Окт. 14, 2023

Abstract INTRODUCTION A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these factors, possible interactions between them or with genetic risk, and causality, how they can help in clinical trial recruitment drug development. Artificial intelligence (AI) machine learning (ML) may refine understanding. METHODS ML approaches are being developed prevention. We discuss exemplar uses evaluate the current applications limitations prevention field. RESULTS Risk‐profiling tools identify high‐risk populations trials; however, their performance needs improvement. New risk‐profiling trial‐recruitment underpinned by models be effective reducing costs improving future trials. inform drug‐repurposing efforts prioritization disease‐modifying therapeutics. DISCUSSION is not yet widely used but has considerable potential to enhance precision Highlights practice. Causal insights needed understand over lifespan. AI will personalize risk‐management could target specific patient groups that benefit most

Язык: Английский

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Predicting conversion in cognitively normal and mild cognitive impairment individuals with machine learning: Is the CSF status still relevant?

Alzheimer s & Dementia, Год журнала: 2025, Номер unknown

Опубликована: Янв. 30, 2025

Abstract INTRODUCTION Machine learning (ML) helps diagnose the mild cognitive impairment–Alzheimer's disease (MCI‐AD) spectrum. However, ML is fed with data unavailable in standard clinical practice. Thus, we tested a novel multi‐step approach to predict worsening. METHODS We selected cognitively normal and MCI participants from Alzheimer's Disease Neuroimaging Initiative dataset categorized them on total tau/amyloid beta 1‐42 ratios. was applied 3‐year conversion (SCD), assess model's accuracy, identify role of cerebrospinal fluid (CSF) biomarkers this approach. Shapley Additive Explanations (SHAP) analysis carried out explore automated decisional process. RESULTS The model achieved 84% accuracy across entire cohort, 86% patients negative CSF, 88% individuals AD‐like CSF. SHAP identified differences between CSF‐positive ‐negative predictors cut‐offs. CONCLUSIONS yielded good prediction using SCD. CSF‐based categorizations are needed improve predictive accuracy. Highlights algorithms can decline routinely used data. Classification according enhances Different cut‐offs could be neuropsychological tests conversion.

Язык: Английский

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