Antidepressant response: what does DNA have to do with it?

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More than 13 percent of adults in the United States take antidepressant medications, according to 2018 statistics from the Centers for Disease Control and Prevention.¹ These numbers have risen steadily over the past decade, but the effectiveness of antidepressants in treating the growing number of people with mental health problems still raises questions. Finding the right drug and the right dose is often still a guessing game, leaving patients and healthcare professionals eager to find answers on how to best treat challenging symptoms.

Julia Sealock, PhD, a postdoctoral researcher at the Broad Institute, is looking for these answers. Using the data from All of us Research Program, a historic effort by the National Institutes of Health (NIH) to enlist 1 million or more individuals from diverse backgrounds in building the largest health care database of its kind, Dr. Sealock embarked on research to predict antidepressant response.² With electronic health record (EHR) data from hundreds of thousands of enrolled participants All of usDr. Sealock developed an algorithm that aims to determine antidepressant response based on whether an individual continued, added to, or switched their antidepressant after a 6-week period.

Here, Dr. Sealock talks about her work, her inspiration, and what she hopes comes next.

Orlovsky: What was the motivation for your mental health research?

Closed: I received my PhD from Vanderbilt University. At the time, I knew I was interested in genetic research and wanted to focus on brain-related conditions. I thought I wanted to work on Alzheimer’s, but once I started working in the lab, I saw the need for more psychiatric genetic research. I realized that if we can focus more on psychiatric disorders in relation to genetics, maybe we can develop better treatments in the future.

Orlovsky: What inspired you to focus on the antidepressant response?

Closed: We know a lot about brain function. But we don’t know the biology of depression or anxiety or the mechanism of action of antidepressants. There is a disconnect between brain biology and how antidepressants work. For other ailments, we know the biology that is causing them. However, this is not true for psychiatric disorders, which is why an individual response to medication is of great interest to me. My initial research got me thinking about how genetics can influence how we respond to medications. This work really brings together the genetics and mechanism of action of pharmaceuticals, and that’s really interesting to me.

Orlovsky: What gaps have you seen in the current pharmaceutical treatment for depression?

Closed: With the antidepressant response, there’s not much we know. When you go to a doctor to get an antidepressant, they usually start with a general SSRI and then you have to wait to see the effects. That’s why I started thinking long-term wondering if we can find ways to determine who will respond to which treatment. Rather than having them wait 6 weeks to see if the drug works, can we have some tools to help select an effective drug from the start?

Orlovsky: How is your research working towards this goal?

Closed: The project I’m working on now focuses on creating an algorithm to determine antidepressant response from the very beginning. We’re using EHR data to see if people stop one antidepressant and switch to another, or if they’re using an antipsychotic drug or other medications. We’re also looking at their genetic history, as well as looking longitudinally at the EHR data and some of the survey data All of us. By comparing all of this data, we can see if our response results match up with other genetic data.

Orlovsky: What is your ultimate goal with this project?

Closed: The best outcome would be to find genetic variants that actually affect how a person metabolizes a drug. That would be a best-case scenario and it will be a very long-term goal.

Orlovsky: How does the diversity of the All of us database helps you work towards this goal?

Closed: All of us it’s a very large dataset with a lot of information to work with, especially when dealing with different demographic communities. Thinking about genetic studies, the vast majority of them have been done with individuals of European ancestral descent. This really leaves us at a disadvantage. Diversity in research is really important to make sure that all of us are represented.

Mrs. Orlovsky is a medical copywriter at Scripps Research. He has been writing about health and healthcare for over 20 years. Doctor Sealock is a postdoctoral researcher at the Broad Institute.

References

1. Brody DJ, Gu Q. Antidepressant use among adults: United States, 2015-2018. National Center for Health Statistics. 2020. Accessed June 29, 2023. https://www.cdc.gov/nchs/products/databriefs/db377.htm

2. Validation of an algorithm to predict antidepressant response. All of us Research pole. October 7, 2022. Accessed June 29, 2023. https://www.researchallofus.org/spotlight/validating-an-algorithm-to-predict-antidepressant-response/