AI is being harnessed by a partnership between IBM and US healthcare group Cleveland Clinic to help identify non-opioid chronic pain treatments.
Globally, it is
Their deep-learning framework has identified multiple gut microbiome-derived metabolites and FDA-approved drugs that can be repurposed to select non-addictive, non-opioid options to treat chronic pain.
Recent evidence has shown that drugging a specific subset of pain receptors in a protein class called G protein-coupled receptors (GPCRs) can provide non-addictive, non-opioid pain relief.
The challenge posed by the researchers involved in the project is how to target these receptors
Instead of inventing new molecules, they investigated whether they could apply research methods they had already developed for finding preexisting FDA-approved drugs for potential pain indication. Part of this process involves mapping out gut metabolites to spot drug targets.
“Even with the help of current computational methods, combining the amount of data we need for our predictive analyses is extremely complex and time-consuming,” researchers Dr Feixiong Cheng, director of Cleveland Clinic’s genome centre, says.
“AI can rapidly make full use of both compound and protein data gained from imaging, evolutionary and chemical experiments to predict which compound has the best chance of influencing our pain receptors in the right way.”
The team have developed a research tool, called LISA-CPI, which uses deep learning to predict whether a molecule can bind to a specific pain receptor; and where on the receptor a molecule will physically attach.
It can also determine how strongly the molecule will attach to that receptor and whether binding a molecule to a receptor will turn signalling effects turn on or off.
LISA-CPI was used to predict how 369 gut microbial metabolites and 2,308 FDA- approved drugs would interact with 13 pain-associated receptors.
The AI framework identified several compounds that could be repurposed to treat pain. Studies are underway to validate these compounds in the lab.
“This algorithm’s predictions can lessen the experimental burden researchers must overcome to even come up with a list of candidate drugs for further testing,” first author and computational scientist Dr Yuxin Yang says.
“We can use this tool to test even more drugs, metabolites, GPCRs and other receptors to find therapeutics that treat diseases beyond pain, like Alzheimer’s disease.”
Dr. Cheng adds: “We believe that these foundation models will offer powerful AI technologies to rapidly develop therapeutics for multiple challenging human health issues.”
