REPURPOSING ANTICANCER DRUGS ALZHEIMER’S DISEASE: A COMPUTATIONAL INTELLIGEN PERSPECTIVE

Abstract

Aim:- Alzheimer’s disease remains an urgent as well as unsolved neurological disorder of our time. It continues to be a worldwide health challenges as it is a progressive neurodegenerative disease that predominantly affects memory and cognitive function, marked by neronal death and tissue loss that span throughout the brain. Discovering therapeutic drugs for this disease is often complex due to it’s intricate mechanism and rapid progression over patient's lifespan. Traditionally developing pharmaceuticals for AD often take prolonged development process, excessive cost, have high failure rate, off atrget delivery. Thus, drug repurposing, a promising approach to accelerating drug development is the inferring new therapeutic uses for existing drugs, which are already approved for incorporation into medication. Especially the role of, anticancer drugs and their potential in modulating several overlapping molecular mechanisms implicated in both cancer and AD. This paper covers the recent advancement in AI, ML algorithms that accelerate the drug discovery and repositioning process to combat Alzheimer’s Disease and identifying novel therapeutic target. In addition to improving early-stage drug development, these technologies also make it possible to repurpose existing drugs, such as anticancer agents, for Alzheimer's treatment. Result: -As computational frameworks advance with revolutionary invention of “Artificail Intelligence”(AI) and “Machine Learning”(ML), the process of identifying, prioritizing, and validating such repurposable candidates has been revolutionized. This review highlights the transformative role of AI/ML in mining multi-omics data, predicting drug-disease associations, and evaluating therapeutic efficacy in silico. Key computational platforms, models, and case studies are discussed, with a focus on anticancer agents repositioned for AD. The findings underscore the synergistic potential of integrating computational intelligence with biomedical insights to both diseases provide an opportunity to somehow invent novel, precise and accurate therapies against them. Conclusion:- The convergence of oncology and neurodegeneration has opened a promising frontier in the search for therapies for Alzheimer’s disease (AD). Drugs such as Palbociclib, Tomoxifan, Dastainib, Niraparib, and Tofacitinib, etc originally developed for treating malignancies, have demonstrated potential neuroprotective function in AD models. With the advancement of computational frameworks, AI, ML and many other deep learning models have proved to be a saviour in hastening the drug developement process.