Chronic pain, a condition where pain persists or recurs over a long period of time, is common in the US, affecting an estimated 1 in 5 adults. Experiencing chronic pain significantly impacts patients’ lives by interfering with day-to-day activities and increasing risk of depression and substance abuse. There are different causes of pain, including pain that arises from inflammation, nervous system injury, or tissue damage. The way the body senses these different types of pain varies. Despite this variety, current pain relief medications focus on relatively few drug targets (defined as molecules in the body that interact with or are modified by a drug). As a result, current medications may not be effective at treating some types of chronic pain.

A major goal of the Common Fund’s Illuminating the Druggable Genome program is to discover new targets for medications. In research supported by the Illuminating the Druggable Genome program, Dr. Avi Ma’ayan and colleagues identified new drug targets for pain treatment. The researchers used machine learning, a type of artificial intelligence where computer algorithms make predictions from data. By combining data on genes, proteins, and RNA molecules from 14 databases and publications, the computer algorithms prioritized targets for human genes associated with 17 unique types of pain. Using this approach, the researchers identified 13 potential drug targets for migraine drug development and four for rheumatoid arthritis. This work has the potential to accelerate research on the identified drug targets, paving the way for more treatment options for chronic pain.

Reference

Prioritizing Pain-Associated Targets with Machine Learning. Minji Jeon, Kathleen M. Jagodnik, Eryk Kropiwnicki, Daniel J. Stein, Avi Ma’ayan. Biochemistry. 2021 May 11; 60(18):1430-1446.