Psychiatry may be moving closer to precision medicine

For decades, treating depression has largely involved a difficult form of medical guesswork: prescribing one antidepressant after another in hopes that one will eventually help.

A new study led by researchers at the University of California, Irvine and Mass General Brigham-affiliated McLean Hospital suggests that psychiatry may be moving toward something far more precise.

Published today in Nature Mental Health, the study found that using biological and behavioral markers to guide antidepressant treatment selection boosted response rates by nearly 67 percent compared with patients who lacked favorable biomarker profiles.

Researchers said it was one of the first studies to test biomarker-guided antidepressant treatment selection in patients with major depressive disorder using two widely prescribed medications.

Diego A. Pizzagalli, founding director of UC Irvine’s Noel Drury, M.D. Institute for Translational Depression Discoveries and Distinguished Professor of psychiatry and human behavior, neurobiology and behavior, and biomedical engineering, led the effort.

Depression treatment still relies far too heavily on trial and error. Patients often spend months cycling through medications before finding one that works, while symptoms worsen and suicide risk can increase. Our findings suggest we may be able to move psychiatry closer to precision medicine, where objective biological and behavioral data help guide treatment decisions from the outset.”

Diego A. Pizzagalli, founding director of UC Irvine’s Noel Drury, M.D. Institute for Translational Depression Discoveries

The challenge is enormous. Major depressive disorder affects hundreds of millions of people worldwide, yet only 30 to 50 percent of patients respond to the first antidepressant treatment. Even when medications eventually work, individuals may endure weeks or months of debilitating symptoms, side effects and uncertainty before improvement begins.

Unlike many other fields of medicine, psychiatry still lacks objective laboratory tests or biomarkers that can reliably guide treatment decisions, Pizzagalli said.

To address that gap, the researchers turned to two of the most commonly prescribed antidepressants: sertraline, sold under the brand name Zoloft, and bupropion, sold as Wellbutrin.

Using data from a large national depression study known as EMBARC, in a first step, investigators developed predictive algorithms based on a combination of brain imaging, cognitive testing and clinical characteristics. The models incorporated factors including functional MRI measurements of brain connectivity, reward sensitivity, cognitive control, depression severity, personality traits and employment status.

In a separate study, the researchers used these predictive algorithms to select which antidepressant should be prescribed for each participant with major depressive disorder. Specifically, subjects underwent brain imaging, cognitive testing and psychiatric assessments before being assigned to receive either sertraline or bupropion based on the algorithms developed in the prior study.

One of the study’s most striking findings emerged when researchers examined overall biomarker patterns. Patients with favorable biomarkers for one or both medications responded substantially better than patients with no positive biomarkers.

Response rates reached 71.4 percent among patients with positive biomarkers for both medications, compared with 42.8 percent among patients with no positive biomarkers – a nearly 67 percent improvement.

The study did not find statistically significant differences between patients who received the medication specifically matched to their biomarker profile and those intentionally assigned a nonmatching medication, likely because a larger sample size will be necessary to test this hypothesis. But researchers said the broader pattern still offers notable evidence that measurable biological signatures may help identify patients more apt to benefit from standard antidepressants.

“This is important because it reinforces the idea that depression is not a single uniform illness,” Pizzagalli said. “Different biological pathways likely contribute to symptoms in different people. Understanding those differences could eventually allow us to tailor treatments much more effectively.”

The implications could extend well beyond medication selection. In the future, biomarker-guided approaches might help clinicians identify patients unlikely to respond to conventional antidepressants, allowing them to move more quickly toward alternatives such as psychotherapy, brain stimulation therapies or ketamine-based treatments.

Researchers cautioned that the technology is not yet ready for routine clinical use. The study involved fewer than 50 patients in the final analyses, and some predictive measures relied on expensive functional MRI scans that are not, to date, practical for most clinical settings.

Still, scientists said the work represents a milestone in the emerging field of precision psychiatry – an effort to bring the kind of personalized treatment strategies now common in cancer care and cardiology into mental health treatment.

“This study is an early but important proof of concept,” Pizzagalli said. “It lays the groundwork for larger studies that could ultimately transform how we treat depression. These are the types of studies that we will prioritize within the recently launched Noel Drury, M.D. Institute for Translational Depression Discoveries at UC Irvine.”

The research was conducted at McLean Hospital in collaboration with investigators from UC Irvine.

The National Institute of Mental Health funded the EMBARC study. The UC Irvine-led clinical trial received support from Wellcome Leap’s Multi-Channel Psych program. Pizzagalli also received partial backing from the National Institute of Mental Health.