THE EPIGENETIC FRONTIER IN ALZHEIMERS THERAPY: HARNESSING ARTIFICIAL INTELLIGENCE FOR PERSONALISED NEUROTHERAPEUTICS

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.