Volume : 12, Issue : 06, June – 2025
Title:
THE EPIGENETIC FRONTIER IN ALZHEIMERS THERAPY: HARNESSING ARTIFICIAL INTELLIGENCE FOR PERSONALISED NEUROTHERAPEUTICS
Authors :
Sana Begum, Yeshashree Gunjette, Reynaa Mary Neeredu, Narender Boggula*
Abstract :
Alzheimer’s disease (AD) is a devastating neurodegenerative condition that is marked by progressive memory loss and cognitive impairment. In spite of tremendous progress in the understanding of its pathology, treatments today are still largely symptomatic, with no cure in sight. Recent discoveries in molecular neuroscience have shown that epigenetic mechanisms, such as DNA methylation, histone modifications, and non-coding RNAs, play a key role in the onset and progression of Alzheimer’s. These mechanisms can regulate gene expression without changing the DNA sequence and are influenced by factors like aging, environment, diet, and stress. Importantly, epigenetic changes may be reversible, making them appealing targets for treatment. At the same time, the growth of artificial intelligence (AI) has opened new possibilities in precision medicine. Machine learning and deep learning algorithms can analyze large, complex biological datasets, including those from epigenomic studies. In the case of Alzheimer’s, AI can help identify disease-specific epigenetic signatures, find hidden biomarkers, categorize patient subtypes, and predict individual responses to treatments. Combining AI with epigenetics allows a shift from general therapies to personalized neurotherapeutics, where treatments are tailored to an individual’s specific molecular profile. This article looks at the combined potential of epigenetics and AI in reshaping Alzheimer’s treatment. It discusses what we currently know about epigenetic changes in AD, reviews new epigenetic treatments, and shows how AI technologies can speed up target discovery, improve drug development, and support early, accurate diagnoses. The merging of these two advanced fields offers not only hope but also a plan for more effective, personalized, and data-driven solutions for managing Alzheimer’s disease in the future.
Key words: Alzheimer’s disease, epigenetics, DNA methylation, histone modification, artificial intelligence, personalized medicine.
Cite This Article:
Please cite this article in press Narender Boggula al., The Epigenetic Frontier In Alzheimers Therapy: Harnessing Artificial Intelligence For Personalised Neurotherapeutics, Indo Am. J. P. Sci, 2025; 12(06).
Number of Downloads : 10
References:
1. Liu C, Zhang L, Xu J, Deng Y. Epigenome editing as a new frontier for Alzheimer’s therapy. Front Neurosci. 2022; 16:842091.
2. Gatz M, Reynolds CA, Fratiglioni L, et al. Role of genes and environments for explaining Alzheimer disease. Arch Gen Psychiatry. 2006; 63(2):168–174.
3. Lunnon K, Smith RV, Hannon E, et al. Molecular signatures of human brain aging and Alzheimer’s disease revealed by quantitative DNA methylation analysis. Nat Neurosci. 2020; 23(5):708–718.
4. Ott AV, Tureckova M, Peichl L, et al. Epigenetic dysregulation of enhancer–promoter interactions in Alzheimer’s disease. Neurobiol Aging. 2022; 108:12–23.
5. Li B, Horvath S, et al. A machine learning classifier identifies disease-associated CpG sites in Alzheimer’s. Nat Commun. 2021; 12:6712.
6. Wang H, Huang C, Zhao L, et al. The role of AI in diagnosing and managing Alzheimer’s disease: an overview. Front Aging Neurosci. 2020; 12:577699.
7. Rudroff T, Rainio O, Klén R. AI for early prediction of Alzheimer’s from neuroimaging biomarkers—a narrative review. arXiv. 2024 Jun.
8. Park H, Shin J, Kim Y, et al. AAV-delivered CRISPR/dCas9-KRAB epigenome editing targeting APOE in Alzheimer models. Nat Commun. 2024; 15:50515.
9. Divya Vedhartham, Narender Boggula, Vasudha Bakshi. Analysis of Prescriptions for Various Effects of Poly Pharmacy in Tertiary Hospital Care. Indo American Journal of Pharmaceutical Sciences. 2018; 05(06):5969-5983.
10. Munafò MR, Davey Smith G. Robust research needs many lines of evidence. Nature. 2018;553:399‐401.
11. Lyall DM, Kormilitzin A, Lancaster C, Sousa J, Petermann-Rocha F, Buckley C, Harshfield EL, Iveson MH, Madan CR, McArdle R, Newby D, Orgeta V, Tang E, Tamburin S, Thakur LS, Lourida I; Deep Dementia Phenotyping (DEMON) Network; Llewellyn DJ, Ranson JM. Artificial intelligence for dementia-Applied models and digital health. Alzheimers Dement. 2023 Dec;19(12):5872-5884.
12. Petersen RC, Aisen PS, Beckett LA, et al. Alzheimer’s disease neuroimaging initiative (ADNI): clinical characterization. Neurology. 2010;74:201.