AI in opioid addiction: Artificial intelligence could play a big role in treatment, preventing deaths

Leslie Salas Estrada of the Filizola Laboratory believes that opioid addiction can be reduced through the development of drugs that block the kappa-opioid receptor. But, the point is, finding drugs that inhibit the action of proteins like the kappa-opioid receptor can be a time- and money-consuming process because screening billions of chemical molecules can take months.

Opioids are a group of drugs used to treat moderate to severe pain. It can be produced in a laboratory or from the opium poppy plant. By binding to opioid receptors on nerve cells in the brain, spinal cord, gastrointestinal tract, and other organs in the body, they inhibit the transmission of pain signals.

To streamline the process, Salas Estrada is implementing artificial intelligence (AI). She is employing computational techniques that can enhance its effectiveness.

Canadian researchers are conducting a similar experiment in Alberta. In light of the growing national drug overdose problem, researchers are testing artificial intelligence to quantify the dangers associated with prescription opioids.

According to Dr. Dean Eurich of the University of Alberta, machine learning may be more effective at identifying those who are most vulnerable. Physicians can have greater peace of mind thanks to AI-assisted systems, knowing that there are other resources they can use to ensure that the patient is receiving the proper medication at the right time.

Nearly 3 million Americans suffer from opioid use disorder. Every year 80,000 Americans die of overdose. The opioid drugs oxycodone, morphine, fentanyl, and heroin all bind to opioid receptors.

The euphoria and pain relief brought on by mu-opioid receptor activation as well as physical dependence and decreased respiration can lead to mortality from overdose of both drugs. According to preclinical investigations, blocking kappa-opioid receptors may be a useful drug method for treating opioid dependence.

According to Salas Estrada, if people keep trying to stop, they will eventually experience withdrawal symptoms, and recovery from this can be quite challenging. If people are repeatedly exposed to opioids, the brain needs more of the drug. In animal models, it has been demonstrated that blocking the activation of the kappa opioid receptor reduces the need for drug use during the withdrawal process, she says.


According to Salas Estrada, the advantage of artificial intelligence is that it can learn to identify patterns from large amounts of data. Machine learning can help use the knowledge that can be gained from extensive chemical databases, to create new drugs from scratch. By doing so, it may be possible to reduce the time and expense required for drug discovery, he said.

According to Dr. Fiza Gliani of the College of Physicians and Surgeons of Alberta, once machine learning is integrated into a healthcare system, it can be a useful tool to reduce hospital stays and patient morbidity. Sometimes, current techniques can't identify causes of risk, leading to more involved medical treatments than simply reducing a patient's opioid dosage, according to Gianni.