Pharmacogenomic Clinical Support Tools for the Treatment of Depression
Abstract
Objective:
In this review, the authors update the 2018 position statement of the American Psychiatric Association Council of Research Workgroup on Biomarkers and Novel Treatments on pharmacogenomic (PGx) tools for treatment selection in depression.
Methods:
The literature was reviewed for new clinical trials and meta-analyses, published from 2017 to 2022, of studies using PGx tools for treatment selection in depression. The blinding and control conditions, as well as primary and secondary outcomes and post hoc analyses, were summarized.
Results:
Eleven new clinical trials and five meta-analyses were identified; all studies had primary outcome measures related to speed or efficacy of treatment response. Three trials (27%) demonstrated efficacy on the primary outcome measure with statistical significance; the three studies used different PGx tools; one study was open-label and the other two were small single-blind trials. Five trials (45%) did not detect efficacy with statistical significance on either primary or secondary outcome measures. Only one trial (9%) used adverse events as a primary outcome measure. All studies had significant limitations; for example, none adopted a fully blinded study design, only two studies attempted to blind the treating clinician, and none incorporated measures to estimate the effectiveness of the blinds or the influence of lack of blinding on the study results.
Conclusions:
The addition of these new data do not alter the recommendations of the 2018 report, or the advice of the U.S. Food and Drug Administration, that the evidence does not support the use of currently available combinatorial PGx tools for treatment selection in major depressive disorder. Priority efforts for future studies and the development and testing of effective tools include fully blinded study designs, inclusion of promising genetic variants not currently included in any commercially available tests, and investigation of other uses of pharmacogenomics, such as estimating the likelihood of rare adverse drug effects, rather than increasing the speed or magnitude of drug response.
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