A new study has demonstrated that machine learning can accurately predict subtypes of Parkinson’s disease using images of patient-derived stem cells, paving the way for personalised medicine and targeted drug discovery.
The work, by the Francis Crick Institute, UCL Queen Square Institute of Neurology, and technology company Faculty AI, has shown that computer models can accurately classify four subtypes of Parkinson’s disease, with one reaching an accuracy of 95%.
Parkinson’s disease symptoms and disease progression vary from person to person due to differences in the underlying mechanisms causing the disease. Until now there hasn’t been a way to accurately differentiate subtypes, which means patients don’t always have access to targeted treatments.
Sonia Gandhi, Assistant Research Director and Group Leader of the Neurodegeneration Biology Laboratory at the Crick, said: “We don’t currently have treatments which make a huge difference in the progression of Parkinson’s disease. Using a model of the patient’s own neurons, and combining this with large numbers of images, we generated an algorithm to classify certain subtypes – a powerful approach that could open the door to identifying disease subtypes in life.
“Taking this one step further, our platform would allow us to first test drugs in stem cell models, and predict whether a patient’s brain cells would be likely to respond to a drug, before enrolling into clinical trials. The hope is that one day this could lead to fundamental changes in how we deliver personalised medicine.”
Edited by Diana Spencer, Senior Digital Content Editor, Drug Discovery World