Estimating the Potential Impact of CYP2C19 and CYP2D6 Genetic Testing on Protocol-Based Care for Depression in Canada and the United States

Abstract

The Sequenced Treatment Alternatives to Relieve Depression (STAR*D) algorithm is the most recognized protocol-based care approach for moderate to severe depression. However, its implementation results in one-third of individuals receiving modest to no symptom remission. One possible explanation is the inter-individual differences in antidepressant metabolism due to<i> CYP2C19</i> and <i>CYP2D6</i>genetic variation. Here, we aimed to determine the potential benefit of pairing <i>CYP2C19</i> and <i>CYP2D6</i>testing with the five-step STAR*D algorithm. To estimate the proportion of individuals that could benefit from <i>CYP2C19</i> and <i>CYP2D6</i> testing, we simulated the STAR*D algorithm using ethnicity-specific phenotype (e.g., metabolizer status) frequencies published by the Clinical Pharmacogenetics Implementation Consortium and census data from the Canada and the US. We found that up to one-third of the US and Canadian populations being treated for depression could benefit from the addition of <i>CYP2C19</i>and <i>CYP2D6</i> genetic testing. The potential benefit varied for each step of the algorithm and for each province, territory, and state. <i>CYP2C19</i> genotyping had the greatest potential impact within the first two steps of the algorithm, while <i>CYP2D6</i> genotyping had the most notable impact in Steps 3, 4, and 5. Our findings suggest the implementation of <i>CYP2C19</i>and <i>CYP2D6</i> genetic testing alongside the STAR*D treatment algorithm may improve depression treatment outcomes in Canada and the US.

The Utility of CYP2D6 and CYP2C19 Variants to Guide Pharmacological Treatment in Complex Unipolar Major Depression: A Pilot Longitudinal Study

“…It is noteworthy that the studies that tested the impact of pharmacogenomics in major depressive disorders and included CYP2D6 / CYP2C19 genetic variants largely investigated participants at the beginning of their treatment histories where the impact of pharmacogenomics could be more significant due to the prevailing absence of treatment-resistant depression [ 7 ]. This is in line with a recent study by Fan and Bousman which estimated that up to one-third of US and Canadian patients treated for major depressive disorders carry actionable CYP2C19 and CYP2D6 genetic variants and could benefit clinically from pairing CYP2C19 and CYP2D6 testing with the STAR*D treatment algorithm conferring greater effect of CYP2C19 genotyping for the first two steps and CYP2D6 genotyping for the remaining 3 steps [ 10 ]. Although Fan and Bousman’s results are consistent with a cumulative probability estimate for the frequency of non-normal metaboliser phenotypes of CYP2D6 and CYP2C19 across different populations (36.4% for CYP2D6 and 61.9% for CYP2C19 ) [ 11 ], the impact of this approach in the Middle East is largely unknown due to the absence of systematic research conducted in this part of the world [ 11 ].…”

“…SSRIs are commonly used as first line pharmacological treatment in major depressive disorders [1]. This might contribute to explain the larger impact of pharmacogenomic guided treatment in major depression for depressed patients who are relatively new to pharmacologi- cal treatment [10] and the large effect size supporting pharmacogenomic guided treatment shown in meta-analyses which largely excluded treatment resistance [7]. In addition, with an increasing number of treatment trials, improvement, response and remission rates significantly diminish as shown in the STAR*D study [3], contributing to explain the limited level of improvement in this work.…”

Estimating the potential impact of implementing pre‐emptive pharmacogenetic testing in primary care across the UK

“…For the gene CYP2C19, haplotype was translated to phenotype (intermediate [activity score 1], intermediate [activity score 1.5], poor metaboliser), using CPIC guidelines to support application of therapeutic recommendation for non‐steroidal anti‐inflammatories 23 (see Supplementary File 1). Phenotype frequencies for HLA‐A*31:01, HLA‐B*15:02 and HLA‐B*58:01 were calculated using ethnicity incidence frequency tables 24 matched to UK census data 2011 similar to the methodology described by Fan and Bousman 25 . (Supplementary File 2 contains estimates for UK phenotype incidence used in this study.…”