Summary: A new clinical trial used objective biological and behavioral markers to predict antidepressant effectiveness. By screening patients with a combined profile of functional MRI (fMRI) brain connectivity, cognitive reward sensitivity, and clinical variables, researchers were able to anticipate treatment outcomes. Patients with favorable biomarker signatures achieved a 71.4% response rate—about a 67% improvement compared with those without these markers.
The study represents a step toward precision psychiatry by demonstrating that measurable biobehavioral signatures can meaningfully stratify who is likely to respond to commonly prescribed antidepressants.
Key Facts
- The clinical trial-and-error burden: Only 30% to 50% of people with major depressive disorder respond to their first antidepressant. Relying on a sequence of medication trials can mean weeks or months of persistent symptoms and increased risk for adverse outcomes.
- Two medication targets tested: The trial evaluated predictive markers for two widely used antidepressants—sertraline (selective serotonin reuptake inhibitor) and bupropion (norepinephrine-dopamine reuptake inhibitor)—demonstrating biomarker-guided selection across distinct pharmacological mechanisms.
- Algorithmic predictor from multimodal data: Investigators built predictive models from the EMBARC multisite dataset that combined functional MRI measures of brain connectivity with cognitive control and reward-sensitivity tests, clinical severity, personality features, and baseline life variables such as employment status.
- Substantial response improvement: Prospectively, patients who showed favorable biomarkers for both medications had a 71.4% response rate. By contrast, participants with no positive biomarkers had a 42.8% response rate—a near 67% relative increase for biomarker-positive patients.
- Depression is heterogeneous: The findings reinforce that depression encompasses diverse biological pathways. This heterogeneity helps explain why one medication may help one patient but not another.
- Clinical implications and limits: Although promising, the tool is not yet ready for routine clinical use—final analyses included fewer than 50 patients and some predictive measures rely on costly fMRI scans. Still, the framework could eventually help clinicians fast-track patients without favorable biomarker profiles to alternative therapies such as ketamine, transcranial magnetic stimulation (TMS), or intensive psychotherapy.
Source: University of California, Irvine
For decades, antidepressant prescribing has largely been a process of educated guesswork: clinicians often try one medication after another until one proves effective. This new multisite effort led by researchers at the University of California, Irvine and McLean Hospital (Mass General Brigham) advances an alternative: selecting medications using objective biological and behavioral markers.
Published in Nature Mental Health, the trial tested whether an algorithmic, biomarker-guided approach could improve response rates to sertraline and bupropion in patients with major depressive disorder. The models were trained on data from the EMBARC study and incorporated fMRI connectivity, cognitive measures of reward sensitivity and control, symptom severity, personality, and sociodemographic variables.
In a prospective clinical trial, participants underwent brain imaging, cognitive testing, and psychiatric assessments before assignment to either sertraline or bupropion based on the predeveloped predictive models. Across analyses, patients with positive biological signatures for one or both drugs responded substantially better than those with no positive markers.
Although the trial did not find statistically significant differences between patients who received medication that exactly matched their biomarker profile versus those assigned a nonmatching drug—likely due to limited sample size—the overall pattern highlights that measurable markers identify groups more likely to benefit from standard antidepressants.
Lead investigator Diego A. Pizzagalli emphasized the clinical significance: depression is not a single uniform illness, and treatments guided by objective data may allow clinicians to tailor interventions more effectively from the outset, reducing time spent on ineffective trials and the associated morbidity.
Researchers caution that broader implementation will require larger trials and more accessible, cost-effective biomarkers. Current reliance on fMRI limits immediate clinical scalability. Nonetheless, this work represents an important proof of concept for precision psychiatry, analogous to personalized approaches used in oncology and cardiology.
The study was conducted at McLean Hospital in collaboration with UC Irvine investigators. Funding included the National Institute of Mental Health for the EMBARC dataset and support for the clinical trial from Wellcome Leap’s Multi-Channel Psych program, with additional backing for investigators from the National Institute of Mental Health.
Key Questions Answered
A: Unlike many medical specialties that use laboratory tests or imaging to choose treatments, psychiatry currently lacks widely validated objective tests to predict individual medication response. Major depression can present with similar outward symptoms while arising from different internal biological mechanisms, so medication response varies considerably between individuals.
A: Researchers trained predictive algorithms on the EMBARC dataset that combined multimodal features: fMRI measures of brain connectivity, cognitive tasks measuring reward sensitivity and cognitive control, clinical severity, personality traits, and baseline sociodemographic factors. Integrating these markers allowed models to identify patterns associated with better response to sertraline or bupropion.
A: The initial clinical study analyzed a relatively small sample (fewer than 50 patients in final analyses), and some predictive inputs rely on expensive fMRI scans that are not yet practical for typical clinical settings. Larger trials and more scalable biomarker options will be needed before routine implementation.
Editorial Notes
- This article was edited by a Neuroscience News editor.
- The journal paper was reviewed in full by the editorial team.
- Additional context was added by staff to clarify clinical implications and limitations.
About this psychopharmacology and mental health research news
Author: Carly Murphy
Source: University of California – Irvine
Contact: Carly Murphy, University of California – Irvine
Image: The image is credited to Neuroscience News
Original Research: Open access. “A precision medicine trial of bupropion and sertraline for major depressive disorder using a biomarker-guided sequential multiple-assignment design” by Peter Zhukovsky et al., Nature Mental Health. DOI: 10.1038/s44220-026-00671-z
Abstract
A precision medicine trial of bupropion and sertraline for major depressive disorder using a biomarker-guided sequential multiple-assignment design
Treatment for major depressive disorder remains challenging: only 30–50% of patients respond to first-line antidepressants. Investigators developed algorithms predicting response to sertraline and bupropion from a multisite dataset and tested those markers in an independent prospective clinical trial of unmedicated patients with MDD (NCT05537584). Cross-validated models achieved good performance in training (area under the curve 0.66–0.86). In the preregistered clinical trial, while assignment to matched versus mismatched drugs did not produce statistically significant differences—likely due to limited sample size—patients with positive markers for both drugs showed a response rate of 71.4%, and those with either drug marker present showed 65.4% response, versus 42.9% with two negative markers. These findings provide foundational evidence for larger personalized treatment studies in MDD.