Chronic pain affects about one in five Americans, and many current treatment options, like opioids, can have serious side effects and lead to dependency. However, researchers at the Cleveland Clinic and IBM are using artificial intelligence (AI) to discover better pain management solutions.
What’s the New Approach?
Dr. Feixiong Cheng and his team have developed a deep-learning framework that helps identify potential non-addictive, non-opioid treatments for chronic pain. This innovative tool looks at metabolites from our gut microbiome and existing FDA-approved drugs that can be repurposed for pain relief. The research was recently published in the journal Cell Reports Methods.
Why is This Important?
Many people rely on opioids for chronic pain relief, but these drugs come with risks. Dr. Yunguang Qiu, a researcher in Dr. Cheng’s lab, points out that targeting specific pain receptors in our bodies can provide effective pain relief without the addictive qualities of opioids. The challenge is figuring out how to target these receptors effectively.
Using AI for Drug Discovery
Instead of creating new drugs from scratch, the research team wondered if they could find existing FDA-approved medications that could help with pain. They mapped out gut metabolites to discover drug targets. This involved updating a previous AI algorithm that could predict how drugs might interact with pain receptors.
Dr. Yuxin Yang, a computational scientist and the study’s first author, explained that their tool, named LISA-CPI (Ligand Image- and receptor’s three-dimensional Structures-Aware framework to predict Compound-Protein Interactions), uses deep learning to predict several factors:
- If a molecule can bind to a specific pain receptor
- Where it will attach to the receptor
- How strongly it will bind
- Whether binding will turn signaling on or off
The team tested 369 gut microbial metabolites and 2,308 FDA-approved drugs to see how they would interact with 13 pain-associated receptors. Their findings identified several compounds that could be repurposed for pain relief, and they plan to validate these results through further lab studies.
Future Implications
Dr. Yang noted that their AI tool can reduce the experimental workload for researchers, allowing them to quickly compile a list of candidate drugs for testing. They believe this technology can be applied to other diseases, such as Alzheimer’s.
Dr. Cheng added that this collaboration with IBM is just the beginning. They are working on developing small molecule foundation models for various health issues, aiming to create powerful AI tools for drug development.
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