Summary: Researchers are turning to artificial intelligence to find new drugs that can block kappa opioid receptors in hopes of reducing opioid addiction.
Source: Biophysical Society
About three million Americans suffer from opioid use disorder, and each year more than 80,000 Americans die from overdoses.
Opioid drugs, such as heroin, fentanyl, oxycodone, and morphine, activate opioid receptors. Activation of mu-opioid receptors results in pain relief and euphoria, but also physical dependence and decreased breathing, the latter leading to death in overdose.
Preclinical studies have shown that blocking kappa-opioid receptors may offer a promising pharmacological approach to treating opioid addiction.
By discovering drugs that inhibit the kappa-opioid receptor, Leslie Salas Estrada in the lab of Marta Filizola at the Icahn School of Medicine at Mount Sinai hopes to alleviate opioid addiction. Salas Estrada, postdoctoral researcher, will present his work on Monday, February 20 at the 67th annual meeting of the Biophysical Society in San Diego, California.
Kappa-opioid receptors are known to mediate brain rewards.
“If you are addicted and you try to quit, at some point you will have withdrawal symptoms, and these can be very difficult to overcome,” explained Salas Estrada, “after long exposure to opioids, your brain is rewired to need more medication Blocking kappa opioid receptor activity has been shown in animal models to reduce this need to use medication during the withdrawal period.
However, finding drugs that can block the activity of a protein, like the kappa-opioid receptor, can be a long and expensive process. Using computer tools can make it more efficient, but it can take months to screen for billions of chemical compounds. Instead, Salas Estrada uses artificial intelligence (AI) to optimize the process.
“Artificial intelligence has the advantage of being able to take huge amounts of information and learn to recognize patterns in it. So we believe that machine learning can help us take advantage of information that can be derived from large chemical databases to design new drugs from scratch. And in this way we can potentially reduce the time and cost associated with drug discovery,” she said.
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Using information about the kappa-opioid receptor and known drugs, they trained a computer model to generate compounds that might block the receptor with a reinforcement learning algorithm that rewards favorable properties for drug treatments.
So far, the team has identified several compounds with promising properties and is working with collaborators to synthesize them and possibly test their ability to block the kappa-opioid receptor in cells, before testing them in animal models for safety. and their effectiveness. Ultimately, said Salas Estrada, “we hope we can help people struggling with addiction.”
About this news on AI research and opioid addiction
Author: Leann Fox
Source: Biophysical Society
Contact: Leann Fox – Biophysical Society
Picture: Image is in public domain
Original research: The results will be presented at the 67th Annual Meeting of the Biophysical Society in San Diego, California.