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Approved research

Alzheimer's Disease Model: A Hybrid Quantitative Systems Pharmacology (QSP) & AI-Driven Evidence Platform for R&D

Principal Investigator: Dr Alexander Knight
Approved Research ID: 58339
Approval date: June 17th 2020

Lay summary

Alzheimer's disease (AD) is the most common form of dementia, affecting patients' ability to remember and to perform everyday tasks. One million people in the UK will have dementia by 2025, and this will increase to two million by 2050. 60% of these dementia cases are caused by Alzheimer's disease. The current cost of dementia in the UK is £26bn and this is predicted to rise to £55bn by 2040. Globally, there are 50 million dementia sufferers, and again, this is predicted to rise dramatically, to 152m by 2050. AD has proved hard to treat; the last 57 trials of new drugs have been unsuccessful. One of the reasons for this seems to be that scientists do not fully understand the way that the disease progresses in the brain. Many different types of data have been collected, including brain scans and blood tests, from many patients and from healthy people. This data probably contains many useful clues to understanding the disease, and how best to treat it. But the data sets are big, complex and hard for humans to interpret. New methods in 'machine learning' and 'artificial intelligence' are a potential solution. They enable a computer to look for patterns in the data that a human might miss. We have previously used these tools to build a model of how AD progresses in the brain and affects the brain's function. The aim of the project we are proposing is to use the data from the UK Biobank to improve our model so that we can make more accurate predictions and improve our understanding of how the disease can be treated. We anticipate that this work will take approximately 2 years because of the complexity of the data sets. We will then make our model available to companies developing new drugs and treatments for AD. In the long run, the hope is that new treatments will be available to slow or stop the progression of the disease, and possibly even to prevent it.