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Separate brain circuits drive fentanyl addiction

New therapies might be able to treat addiction by selectively targeting withdrawal

by Alla Katsnelson, special to C&EN
May 23, 2024 | A version of this story appeared in Volume 102, Issue 16


A syringe sits on top of a table. It has a blue label marked with black ink spelling out "fentanyl." Several additional syringes are in the background.
Credit: Yang H. Ku/C&EN/Shutterstock
New insights into the way fentanyl binds to opioid receptors may help researchers develop ways to reduce addiction and ease withdrawal.

Distinct brain circuits drive two key components of addiction to the synthetic opioid drug fentanyl, according to a new finding published in Nature (2024, DOI: 10.1038/s41586-024-07440-x). The research significantly revises researchers’ understanding of opioid addiction, and it points to approaches for more targeted treatment.

Opioids are extremely addictive, and fentanyl—which is 50–100 times as potent as morphine or heroin and causes addiction in about a quarter of people who use it—is often deadly. In 2022, almost 82,000 people in the US died of opioid overdoses, the vast majority because of fentanyl. Researchers know that opioid addiction is driven both by positive reinforcement (a pull to the substance’s euphoric effects) and by negative reinforcement (an effort to avoid deeply unpleasant withdrawal symptoms). Yet how opioids hook the brain isn’t fully understood.

Fentanyl and other opioid compounds bind to mu opioid receptors in neurons in a part of the midbrain known as the ventral tegmental area (VTA). According to the leading hypothesis, activating these receptors drives the release of dopamine, a neurotransmitter thought to be involved in the reward pathway. This causes positive reinforcement, and the dopamine depression that results when access to the drug ends causes the withdrawal and negative reinforcement.

But when Christian Lüscher at Geneva University and his colleagues hampered the mu opioid receptor’s activity in the VTA of mice and then gave them fentanyl, the animals displayed fewer signs of positive reinforcement but still showed behaviors reflecting negative reinforcement. That finding suggested the two processes might be separate, so the researchers quantified neuronal activity in different parts of the brain during withdrawal by tracking the expression of a quick-acting gene. They found increased activity only in a brain area called the central amygdala. Selectively knocking out mu opioid receptors there eliminated withdrawal behaviors.

“The role of the central amygdala opioid mu receptors is novel and really interesting,” says David H. Root, a neuroscientist at the University of Colorado Boulder who was not involved in the work. “They show pretty good evidence that these neurons are selectively activated under this very specific condition of withdrawal from opioids.”

The researchers took a close look at the circuitry in this brain area. Giving the mice fentanyl followed by naloxone, an opioid receptor blocker that induces withdrawal, resulted in activity in opioid-receptor-expressing neurons in the central amygdala. And when they stimulated those neurons directly using a technique called optogenetics, the mice quickly learned to press a lever to turn the stimulation off.

“Modern circuit neuroscience now allows us to disentangle” the many different effects that a drug like fentanyl has when it enters the brain of a living animal, Lüscher says.

Lüscher and his colleagues are now investigating how the positive and negative reinforcements interact “and whether they both contribute to the transition from a controlled use to a compulsive use.”

But knowing the two systems are separate means addiction therapies can target each independently, Lüscher says. Currently, opioid addiction treatment often involves prescribing methadone to avoid withdrawal—the negative reinforcement—even though it still activates opioid receptors, which can help maintain dependence on the medication. The new findings might lead to more efficient therapies that target negative reinforcement alone, Lüscher says.


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