An increasingly common question I get asked is, Will gene testing help my doctor know which antidepressant to prescribe? Popular tests such as GeneSight suggests that they can shorten your road to recovery and how you, as an individual, will respond to specific antidepressant medications.

Does drug-gene testing, also referred to as pharmacogenomics or pharmacogenetics, work? And if so, does it only work for certain types of medications? Lets find out.

The idea of gene-drug testing is pretty simple. By testing your DNA, companies hope to be able to predict your response (or likely non-response) to specific types of antidepressants. Its also being marketed for a number of other diseases and medications.

Just a year ago, GeneSight had some pretty strong marketing language on its site. The company was strongly suggesting its test could help your doctor choose the best antidepressant for you:

Fortunately, the GeneSight genetic test can provide doctors answers that quickly lead to relief. Pharmacogenomic testing helps empower your doctor with the exact information needed to prescribe you the best medication for you. By examining how your DNA responds to specific medications such as antidepressants, this simple, painless test lets doctors know which medications may not work for you, so you can get back to feeling like yourself again. [] Through pharmacogenomic testing, your doctor can identify the correct medication and create a personalized treatment for you.

In the 2018 announcement of its own antidepressant test, another gene-drug testing company called Color says it now analyzes a number of these genes, starting with two that can impact your response to certain mental health medications like Zoloft, Paxil, and Lexapro. The blog entry cites seven research studies, but none of them have anything to do with antidepressants.

Few genetic researchers feel as positive about the current usefulness of gene-drug testing than companies marketing these tests. The American Psychiatric Associations research council reviewed the evidence last year and found that such genetic testing is not really ready for mass consumption.

Greden et al. (2019) looked at using pharmacogenomics directly to help in depression treatment. Because the researchers didnt find a significant difference (either statistically or clinically) in their primary outcome measure, they instead emphasized the statistical significance they found in two of the 25 secondary outcome measures they examined.

In treatment research, scientists increasingly use a statistic called Number Needed to Treat (NNT) that allows for cross-comparisons of the real-world efficacy of different kinds of treatment. The National Institute for Clinical Excellence (NICE) in the UK recommends that for a treatment to be clinically significant, the NNT should be in the single digits.

According to a critique of the researchers (Goldberg et al., 2019), the Greden study had an NNT of 17 for a response to an antidepressant and an NNT of 19 for remission of a depressive episode. Not exactly powerful numbers. In fact, combined with the non-significance of the primary outcome studied, Greden ironically demonstrated that pharmacogenomics doesnt appear to very good at its primary goal of helping to guide antidepressant treatment.

In short, the science today doesnt support the mainstream use of these tests for antidepressants.

Personalized medicine is the new New Thing marketed by anyone who has access to a DNA lab. The problem is that the marketing of gene-drug testing far overshadows the science. In early 2019, the U.S. Food and Drug Administration updated its guidance on gene-drug testing:

[The] FDA is aware of genetic tests that claim results can be used to help physicians identify which antidepressant medication would have increased effectiveness or side effects compared to other antidepressant medications. However, the relationship between DNA variations and the effectiveness of antidepressant medication has never been established. []

Do not change or stop taking any medicine based on a report from a genetic test you took on your own. []

[And to doctors:] If you are using, or considering using, a genetic test to predict a patients response to specific medications, be aware that for most medications, the relationship between DNA variations and the medications effects has not been established.

Goldberg et al. (2019) said it best:

[Researchers] have noted that commercial [] test manufacturers promote their products with a zeal that is disproportionate to the existing evidence base particularly when marketing to the lay public and clinicians who are likely unfamiliar with the limited statistical power of candidate gene association studies.

Youd be wasting your money by purchasing one of these tests in hopes of getting better results from your antidepressant treatment. The science simply doesnt support use of these tests at this time.

Online health information isnt always accurate on this issue even from trusted sources. For instance, the Mayo Clinic suggest these tests can help, but its unclear whether the anonymous, unlisted author of that article has examined the primary research (as there are no research references listed in the article). Harvard Health Publishing, on the other hand, got it right by noting that the research of gene-drug testing showed no evidence of effectiveness.

Someday, the hope is that pharmacogenetics may meaningfully inform treatment decisions, as it does in oncology. But were not yet there.

References

Goldberg, J.F., Rosenblat, J.D., McIntyre, R.S., Preskorn, S.H., de Leon, J. (2019). Letter to the Editor: Clinical versus statistical significance of pharmacogenomic-guided antidepressant therapy: Whats really being measured and marketed? Journal of Psychiatric Research, 114, 208-209.

Greden, J.F., Parikh, S.V., Rothschild, A.J., et al. (2019). Impact of pharmacogenomics on clinical outcomes in major depressive disorder in the GUIDED trial: a large, patient-and rater-blinded, randomized, controlled study. J. Psychiatr. Res. 111, 5967. https://doi.org/10.1016/j.jpsychires.2019.01.003

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