This trial and error approach can exhaust and discourage patients, and too many failed trials can cause some to stop seeking treatment altogether.
“Every failure they have is a huge setback,” says Diego Pizzagalli, director of the Center for Depression, Anxiety and Stress Research at McLean Hospital in Belmont, Massachusetts.
In a new study now recruiting patients, Pizzagalli and his team are trying to use MRI scans and other technology to identify biomarkers in the brain’s reward system that could help predict which of the two antidepressants will work best for patients with depression. marked by anhedonia, or an inability to experience pleasure, which Pizzagalli calls a “major symptom” of depression.
While medical fields such as oncology have long used predictive biomarkers to develop therapies, it is a clinical approach that has been elusive in psychiatry.
“We’ve always been very jealous of that approach in a way,” said Pizzagalli. “I’ve been doing this for 20 years and I don’t think we’ve ever been closer.”
Pizzagalli and his team at McLean have been international leaders in identifying biomarkers for anhedonia, with few colleagues focusing on other aspects of depression.
“They’re doing a great job developing an approach where you can treat one of these types of depression that doesn’t respond to today’s standard antidepressants,” said Leanne Williams, who is leading similar prospective research using MRI to identify predictive biomarkers of cognitive subtypes of depression. at the Stanford Center for Precision Mental Health and Wellness.
Pizzagalli’s team uses MRI scans to assess the reward system in someone’s brain at rest; the researchers also assign volunteers certain computer tasks. Participants are shown certain “stimulus” on the computer, asked to make certain decisions, given rewards, and then tested again. In the same way that an oncologist can take an image of a tumor and then run additional tests to confirm the results, the behavioral task acts as a backup to the prediction made from the MRI.
A previous study by Pizzagalli’s group analyzed patients taking sertraline (an SSRI) and bupropion (an atypical antidepressant that stimulates dopamine and norepinephrine). The researchers found that stronger connections between two specific nodes in the brain’s reward system indicated a response to the atypical antidepressant, as opposed to the SSRI. This was reinforced by the behavioral task, which showed that a higher sensitivity to reward also indicated a better response to the atypical depressant.
In the new trial, the participants will perform the same scan and the same tasks, after which they will be treated for eight weeks. Some people will get their “intended” antidepressant — the one that matches the prediction based on their biomarkers — and others won’t. Pizzagalli’s team will assess whether participants who received the intended treatment show more improvement than those who did not.
If the researchers can successfully predict which of the two antidepressants will work for humans, it could finally be a big step toward much-needed clinical action for patients.
Marin Moore is a 22-year-old public school teacher in Virginia who attended college in Boston and was part of a separate trial from Pizzagalli. Moore was diagnosed with depression at age 16 and has been on a variety of medications throughout her life. She was off the meds in her senior year of college when she began experiencing a major depressive episode. She knew antidepressants might help, but she didn’t want to bother finding one.
“The process of finding that right dosage takes months, and that time when you’re not having fun, when you can’t focus, you can’t really be a person — it disrupts your life,” Moore said.
Pizzagalli’s long-term vision is to develop actionable steps to predict which antidepressant will be most effective for a patient. Williams is optimistic that the research could lead to clinical action. “I don’t think we’re that far from it being possible,” she said.
But despite the abundance of biomarker research, implementation is difficult without any prior framework for using predictive technology in inpatient psychiatric settings. Prospective studies such as McLean’s are resource-intensive and costly. Not every doctor has access to an MRI machine, and it’s unclear whether every insurance company would cover the high cost of the scans.
“A lot of treatment decision making is driven by economics,” said Andrew Leuchter, a physician and mood disorder researcher at the University of California, Los Angeles, who focuses on treatment-resistant depression.
And of course biomarkers can’t predict everything.
When she was first diagnosed, Moore was able to find an antidepressant that worked for her fairly quickly, as her mother had successfully taken the medication before. But what neither she nor her psychiatrist could predict were the side effects — intense nausea on one drug, then incidents of complete vision loss on another.
“It was such an overwhelming, time consuming and sometimes physically painful process to get through that I would rather find ways to deal with my depression — like a really bad depression — than try to go back on medication,” said Moore.
In research to date, it seems that when a treatment is a “match,” the side effects are also reduced, Williams said. But they can still occur, and experts hope that future technology can also accurately predict physical side effects. For now, there may be other treatments that would work better for Moore, but there are no shortcuts to finding them.
In light of the new study at McLean, experts focused on precision psychiatry are hopeful. They envision a future where simple scans could save patients months or years of trial-and-error experimentation, and instead lead them to the one most likely to help them on the first try.
Get your daily dose of health and medicine every weekday with STAT’s free Morning Rounds newsletter. Register here.