Recent research published in natural mental health The study suggests that using biological and behavioral markers may help predict how well people with depression will respond to common medications. By assessing specific brain patterns and cognitive characteristics before treatment, scientists found evidence that certain patients were much more likely to experience symptom relief from antidepressants. These findings provide the basis for creating personalized treatment plans that can reduce the time and guesswork typically required to find the right treatment for depression.
Major depressive disorder affects millions of people, but finding effective treatments often requires a process of trial and error. Patients may take the drug for more than a month before knowing whether it is working, and many do not experience sufficient symptom relief with the first drug they try.
“Treatment for depression still requires a lot of trial and error. Only 30 to 50 percent of patients respond to the first antidepressant, and it can take several weeks to determine whether the drug is working,” said Diego A., founding director of the Noel Drury, M.D., Translational Depression Discovery Institute and distinguished professor of psychiatry and human behavior, neurobiology and behavior, and biomedical engineering at the University of California, Irvine. Pizzagalli said.
To reduce this wait time and improve outcomes, scientists are looking for objective signs that can predict a person’s response to a particular drug before a prescription is written. These objective signs are known as biomarkers and are measurable indicators of biological conditions or conditions. In the case of depression, biomarkers may be specific patterns of brain activity, cognitive characteristics, or even demographic factors such as employment status. By identifying these markers, clinicians may ultimately be able to match patients with the precise drugs that are most likely to help them.
In this context, this study aimed to predict response to two widely prescribed antidepressants, sertraline and bupropion. Pizzagalli explained the reasoning behind considering these particular options. “We wanted to test whether information collected before treatment, such as brain connectivity, reward learning, cognitive performance, and clinical characteristics, could help identify people who are most likely to benefit from two commonly prescribed antidepressants, sertraline and bupropion,” he said.
Sertraline belongs to a class of drugs that increase levels of the chemical serotonin in the brain to help regulate mood. Bupropion works differently, targeting other brain chemicals called dopamine and norepinephrine, which are associated with motivation and reward processing. Because these drugs act through different chemical pathways, the researchers reasoned that their distinct biological profiles may indicate which drugs tend to be most effective for individual patients.
The authors first developed a predictive tool using data from a previous large clinical trial known as the EMBARC study. “An important feature of this study is that it was not simply an after-the-fact look at existing treatment data,” Pizzagalli said. “We first developed a predictive model in an early independent study and then tested it prospectively in a double-blind clinical trial. This resulted in a very rigorous initial test of a biomarker-based antidepressant treatment.”
To build this predictive algorithm, the researchers looked at a variety of potential biomarkers, including demographic details such as employment status and clinical factors such as personality traits such as depression severity and neuroticism. It also includes specific cognitive tests and brain imaging data to complete the predictive tool. By analyzing these historical records, they built a mathematical model to identify the main characteristics of people who responded well to each drug.
One specific measurement in this model included functional magnetic resonance imaging (fMRI). The technology measures brain activity by detecting changes in blood flow over time, allowing scientists to see how different areas communicate. The researchers examined resting-state connections between two brain regions involved in reward processing and emotional regulation. These two regions are the nucleus accumbens, located deep in the brain, and the rostral anterior cingulate cortex, located near the front of the brain.
In addition to brain scans, this predictive tool incorporates specific behavioral tests to measure cognitive function. Participants completed a computer-based assessment called a probabilistic reward task to measure reward sensitivity, which assesses how strongly a person responds to positive feedback. They also completed a cognitive control assessment known as the flanker task. It measures a person’s ability to focus on a specific target while actively ignoring distracting information.
By combining these diverse data points, the algorithm assigned a positive or negative marker to both sertraline and bupropion. To test the algorithm in real time, the researchers enrolled a new group of 48 drug-naive adults with major depression. Each participant underwent a baseline MRI scan, cognitive assessment, and clinical interview. The researchers used standardized clinical questionnaires, such as the Montgomery-Osberg Depression Rating Scale, to quantify the exact severity of each participant’s depressive symptoms before starting treatment.
Within days of these initial tests, the algorithm analyzed the new participants’ data to determine their biological markers. Of the original group, 47 participants completed at least 1 week of treatment and were included in the main analysis. After biomarker assessment, participants were randomly assigned to receive either sertraline or bupropion.
Half of the group received a drug that matched their biological marker. This means that the algorithm predicted that they would respond well to the drug based on their unique biology. The other half received drugs that did not match their marker profile. To prevent bias, this study was conducted in a double-blind manner. This meant that neither the patient nor the physician evaluating the patient knew what specific drugs had been prescribed or what the biomarker results were.
Researchers tracked the participants’ symptoms of depression over eight weeks to see how they progressed. They expected that those who took the appropriate medication would show stronger improvement. “We were hoping that if we gave people the specific drug that their biomarker profile indicated would improve their symptoms, that wasn’t the case,” Pizzagalli told PsyPost.
“Instead, this marker seemed to be better at distinguishing between people who were generally more or less likely to respond to one drug or the other. The response rate was about 71% for participants with positive markers for both drugs, compared to about 43% for participants without either drug,” Pizzagalli said. The data showed that assigning drugs strictly based on aligned markers did not result in a statistically significant difference in symptom relief compared to nonaligned assignment. The specific medication participants received appeared to be more important than the overall presence of positive biomarkers.
Regardless of drug assignment, participants who had positive markers for both drugs or at least one drug had greater reductions in depressive symptoms than participants who had two negative markers. The response rate for those with markers for just one of the drugs was 65.4 percent. In this study, response was defined as a decrease in clinical depression scores of 50 percent or more from the start of the study to the end.
“Our results provide an encouraging but preliminary step towards making depression treatment more individualized,” Pizzagalli said. “Participants whose profiles included at least one positive treatment response marker showed more improvement than participants whose profiles suggested they would not respond to either drug. However, markers do not reliably tell us whether sertraline or bupropion is a better choice for a particular individual.”
There are several possible misconceptions that readers should avoid. “These findings do not mean that brain scans or behavioral tests can now determine which antidepressants a patient should take,” Pizzagalli said. “This was a relatively small study, and our primary comparison of whether patients had better outcomes when matched to the marker-indicated drug was not statistically significant. Therefore, this result needs to be replicated in a substantially larger and more diverse sample.”
Reliance on functional magnetic resonance imaging represents another practical barrier to widespread clinical application. Brain scans are expensive, time-consuming, and require specialized equipment that is not easily accessible in everyday primary care settings. Additionally, the way different MRI machines process data differs, which can make it difficult to generalize algorithms across different hospitals and research centers.
To address these logistical hurdles, standardized procedures must be established across the medical field. “The next step is to test and refine these predictive models in large, multicenter clinical trials,” Pizzagalli said. “We also hope to develop markers that are easier and cheaper to use in routine care, and to determine whether different profiles predict response not only to traditional antidepressants, but also to treatments such as brain stimulants, ketamine, and other fast-acting therapies.”
The study, “Precision Medicine Trial of Bupropion and Sertraline for Major Depressive Disorder Using a Biomarker-Based Sequential Multiple Assignment Design,” was conducted by Peter Zhukovsky, Manuel Kuhn, Lauren R. Borchers, Boyu Ren, Sarah E. Woronko, Mohan Li, Choi Sze Tracy Lam, Ethan M. Zhang, Kerry J. Ressler, Brian P. Brennan, Gordana Vitaliano and Diego A. Pizzagalli.
