By Morgan Nwanguma
Multiple gut metabolites and American FDA-approved drugs that can be repurposed as non-addictive including non-opioid pain medications were identified by the artificial intelligence algorithm.
Approximately one out of five Americans lives with persistent and enduring pain, and existing alternative treatments call for concern. Currently, researchers are adoptingartificial intelligence (AI) for drug discovery in advanced pain management. The researchers’ deep-learning framework identified multiple gut microbiome-derived metabolites as well as FDA-approved medications that can be repurposed to pick non- addictive, non-opioid options to treat chronic pain.
This is the outcome of a recent study led by Feixiong Cheng, PhD, Director of Cleveland Clinic’s Genome Center, and IBM who are currently making use of artificial intelligence (AI) for drug discovery in advanced pain management. The discoveries are published in Cell Reports Methods.
Meanwhile it is still problematic treating chronic pain with opioids owing to the possibility of severe side effects and dependency, says co-first author Yunguang Qiu, PhD, a postdoctoral fellow in Dr. Cheng’s lab whose research program pays more attention on creating therapeutics for nervous system disorders. Empirical data of late has revealed that drugging a particular subset of pain receptors in a protein class known as G protein-coupled receptors (GPCRs) is capable making available non-addictive, non- opioid pain reprieve. Dr. Qiu explains that the problem remains how to target those receptors.
Instead of coming up with new molecules from the very beginning, the researchers wondered whether it is possible for them to apply research methods they had previously developed for discovering FDA-approved drugs that have been in existence for probable pain suggestion. An aspect of this procedure requires mapping out gut
metabolites to point out drug targets.
In order to spot these molecules, the head of the team and computational scientist
Yuxin Yang, PhD, a former Kent State University graduate student. Dr. Yang completed his thesis research in Dr. Cheng’s lab and has remained there, working as a data scientist. Drs. Yang and Qiu led a team to bring up to date an earlier drug discovery AI algorithm the Cheng Lab had designed. Their partners from IBM assisted in writing and editing the manuscript.
“Our IBM collaborators gave us valuable advice and perspective to develop advanced computational techniques,” Dr. Yang says. “I’m happy for the opportunity to work with and learn from peers in the industry sector.”
To establish if a molecule will work as a drug or not, scientists are required to forecast how it will physically interact with and affect proteins in our body i.e. our pain receptors. In carrying out this action, the study team needs a 3D comprehension of the two molecules based on wide-ranging 2D data regarding their physical, structural and chemical make-ups.
“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,” Dr. Cheng
explains. “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 researchers’ tool, known as LISA-CPI (Ligand Image- and receptor’s three- dimensional (3D) Structures-Aware framework to predict Compound-Protein Interactions) makes use of a form of artificial intelligence known as deep learning to forecast:
if a molecule can attach itself to a particular pain receptor
position on the receptor a molecule will bodily bind to
how powerfully the molecule will bind to that receptor
if attaching molecule to a receptor will make signalling effects to go on or off
The researchers applied LISA-CPI in forecasting how 369 gut microbial metabolites and 2,308 American FDA-approved medications would relate with 13 pain-associated receptors. The AI framework was able to spot more than a few compounds that will possibly be repurposed for the treatment of pain. Research is on going to authenticate these compounds in the laboratory.
“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,” Dr. 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.”
Furthermore, Dr. Cheng stated that this is merely an example of how the researchers are working together with IBM to come up with small molecule foundation models for
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