A new era of precision depression treatment

For millions of people living with depression, finding an effective treatment can feel like a long and uncertain journey. Patients often spend months trying different medications, enduring side effects and persistent symptoms while waiting to discover whether a prescription will help.

A new study led by researchers at the University of California, Irvine and Mass General Brigham-affiliated McLean Hospital suggests there may be a better way.

Published in Nature Mental Health, the research found that using biological and behavioral markers to help guide antidepressant treatment selection increased response rates by nearly 67 percent compared with patients who lacked favorable biomarker profiles. The findings represent one of the first efforts to use biomarkers to inform antidepressant prescribing decisions for people with major depressive disorder.

The study was led by 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.

"Depression treatment still relies far too heavily on trial and error," Pizzagalli says. "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."

Why depression treatment remains difficult

Major depressive disorder affects hundreds of millions of people worldwide and remains one of the leading causes of disability. Yet despite the widespread use of antidepressants, only about 30 to 50 percent of patients respond to the first medication they receive.

Unlike many other areas of medicine, psychiatry has few objective tools to help physicians determine which treatment is most likely to work for an individual patient. While cancer specialists can use genetic information to guide therapies and cardiologists can rely on laboratory tests and imaging, mental health professionals often must depend on symptom reports and clinical judgment.

The result is a treatment process that can be frustrating for both patients and clinicians.

Building a personalized treatment model

To address that challenge, researchers focused on two commonly prescribed antidepressants: sertraline, sold under the brand name Zoloft, and bupropion, sold as Wellbutrin.

The team first analyzed data from EMBARC, a large national depression study, to develop predictive algorithms designed to identify which patients were more likely to respond to each medication.

Those algorithms drew on a range of information, including brain imaging data, cognitive testing results and clinical characteristics. Researchers evaluated factors such as functional MRI measurements of brain connectivity, reward sensitivity, cognitive control, depression severity, personality traits and employment status.

In a separate clinical trial, participants underwent brain imaging, cognitive testing and psychiatric evaluations before researchers used the algorithms to determine which antidepressant should be prescribed.

Biomarkers reveal stronger treatment responses

One of the study's most significant findings emerged when researchers examined participants' overall biomarker profiles.

Patients who showed favorable biomarkers for one or both medications responded at much higher rates than those without positive biomarkers. Response rates reached 71.4 percent among patients with favorable biomarkers for both antidepressants, compared with 42.8 percent among patients with no positive biomarkers.

Although the study did not find statistically significant differences between patients who received a biomarker-matched medication and those intentionally assigned a nonmatching treatment, researchers believe the small sample size likely limited their ability to detect those effects.

Even so, the findings provide encouraging evidence that measurable biological signatures may help identify individuals who are more likely to benefit from standard antidepressant treatments.

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

Looking beyond medication

The implications of the research extend beyond choosing between antidepressants.

In the future, biomarker-guided approaches could help clinicians identify patients who are unlikely to respond to conventional medications. Those individuals might then be directed more quickly toward alternatives such as psychotherapy, brain stimulation therapies or ketamine-based treatments.

Researchers caution that the technology is not yet ready for routine clinical use. The study included fewer than 50 patients in its final analyses, and some of the predictive measures relied on costly functional MRI scans that are not widely available in most clinical settings.

Still, scientists view the work as an important milestone in the emerging field of precision psychiatry, which seeks to bring personalized treatment strategies to mental health care.

"This study is an early but important proof of concept," Pizzagalli says. "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. Funding for the EMBARC study came from the National Institute of Mental Health, while the UC Irvine-led clinical trial received support from Wellcome Leap's Multi-Channel Psych program. Pizzagalli also received partial support from the National Institute of Mental Health.