Alzheimer and AI - New Challenges
- BumbleeHealth
- 22 set 2023
- Tempo di lettura: 4 min
In one of medicine's most intricate puzzles, Alzheimer's disease, a neurodegenerative condition characterized by symptoms such as dementia, memory loss, and personality changes, has posed a formidable challenge to research efforts in finding a cure. A team of scientists, led by Dr. Rui Chang, an associate professor of neurology at the University of Arizona College of Medicine in Tucson, in collaboration with researchers from Harvard University, has harnessed artificial intelligence to uncover the underlying causes of Alzheimer's disease. They delved deep into the human brain to map the molecular changes occurring in healthy neurons as the disease progresses.
Dr. Chang highlights the complexity of Alzheimer's disease, emphasizing that it involves multiple pathways within cells that trigger changes in the body. Historically, researchers focused on amyloid plaques and tau tangles as key factors, but drugs targeting these structures have failed in clinical trials. This suggests that plaques and tangles are likely consequences rather than causes of Alzheimer's. Dr. Chang likens the disease's progression to a river, where these abnormalities occur downstream in response to issues upstream, driven by genetic mutations in earlier pathways. He asserts that effective treatment should target the disease's upstream origins, making it crucial to understand the entire landscape of Alzheimer's.
Dr. Chang is utilizing AI to chart the intricate landscape of Alzheimer's disease. His AI algorithm, drawing from a vast repository of data from over 2,000 Alzheimer's brain tissue samples, has generated a comprehensive computational network model of the human brain. This model reveals collaborative gene maps across the entire genome and tracks the sequential shifts in these gene relationships as Alzheimer's progresses, shedding light on the disease's origins and tracing the molecular journey from health to disease.
The AI approach is groundbreaking, as it disentangles massive data to offer a clear view of upstream events, identifying the critical upstream genes that influence downstream genes responsible for amyloid plaques and tau tangles. These upstream genes could serve as more promising targets for potential therapies.
Traditionally, studying these genes individually in a lab would be time-consuming, but the scientists are harnessing high-powered computing and AI to select the most promising targets for precision medicine. This innovative approach involves examining 6,000 targets simultaneously, potentially accelerating drug development and discovery significantly. It represents a milestone, demonstrating that AI and big data-driven methods may pave the way for Alzheimer's treatment by targeting new pathways or combinations of pathways
Dr. Chang's use of AI has led to the identification of 19 specific genetic points along the Alzheimer's pathway that appear to play a crucial role in pushing neurons closer to a disease state. Collaborators at Harvard validated these genes by deactivating them in stem cell-derived neurons, observing their impact on the production of amyloid plaques and tau tangles. Ten of these genes were found to influence the formation of plaques and tangles, making them potential targets for Alzheimer's drug treatments.
Once these gene targets are pinpointed, the next challenge is finding drugs that can effectively target them. To expedite this process, the team leveraged high-throughput computational power, replacing time-consuming laboratory experiments. They used 3D computer models to assess whether existing molecules and drugs could fit into these potential drug targets, akin to a lock and key mechanism.
These virtual experiments allowed the team to screen millions of compounds, including FDA-approved drugs, natural products, and small molecules, against over 6,000 targets. This screening process narrowed down the field to approximately 3,000 promising drug candidates. Additionally, the team secured a National Institutes of Health grant to conduct clinical trials on three of these compounds, with human trials expected to commence soon
Dr. Chang's work begins with mathematics and data, where he designs mathematical algorithms to analyze and process extensive data. These algorithms are used to guide research from its mathematical foundation through to clinical studies involving patients. Dr. Chang finds the process of observing compounds progress into clinical trials and ultimately benefiting patients to be a deeply fascinating and rewarding journey
Dr. Rui Chang has harnessed the power of artificial intelligence (AI) to address various health challenges, extending his research to conditions like melanoma, diabetes, and cardiovascular disease. His team is pioneering the use of AI to identify genetic targets in a wide range of diseases, aiming to accelerate precision medicine research. Dr. Chang envisions AI as a potential game-changer, expressing hope for more clinical trials on Alzheimer's treatment drugs within five years and the possibility of FDA-approved drugs to halt or reverse the disease's progression within a decade.
Dr. Chang, whose background is in computer science with expertise in AI and machine learning, has been applying his skills to genomic data for over 15 years. He finds the intersection of computation and biology to be highly rewarding, particularly in the context of studying the brain, which he describes as a fascinating organ with many uncharted functions yet to be discovered.
This research was made possible through the use of a database provided by the Accelerating Medicines Partnership® Program for Alzheimer's Disease (AMP® AD), a collaboration between various organizations, including the National Institutes of Health (NIH), the U.S. Food and Drug Administration (FDA), biopharmaceutical companies, and nonprofit organizations. Funding was provided by NIH, including the National Institute on Aging and the National Institute of Neurological Disorders and Stroke, as well as support from the UArizona Center for Innovation in Brain Science.
