The coming‐of‐age of pharmacogenetic testing in clinical psychiatry
Bibliographic record
Abstract
Pharmacogenetic (PGx) testing is key to addressing the issue of the prevailing ‘trial-and-error’ prescribing of medications in psychiatry. Expert groups have reported high evidence levels for several gene–drug pairs related to psychotropic medications with most of the focus related to antidepressants.1 For instance, several commercially available PGx tests have been launched in psychiatric care that typically examine genetic variations of at least the CYP2C19 and CYP2D6 genes. Some companies have conducted prospective evaluation trials examining the benefits of genetic testing versus treatment as usual (TAU). The largest trial to date was recently completed and consisted of an 8-week double-blind, multicenter, randomized controlled trial (RCT) of the Genesight commercial test versus TAU in depression (N = 1167).2 While using symptom improvement as the outcome variable showed no significance (P = 0.107), patients guided by the test achieved a significant improvement in response (≥50% decrease in 17-item Hamilton Rating Scale for Depression [HAMD-17]; P = 0.013) and remission (HAMD-17 ≤ 7; P = 0.007), compared with those who received TAU. Notably, a recent meta-analysis of multigene PGx tests (incorporating the study by Greden et al.2 and four other RCTs) reported that the PGx test-guided groups (n = 887) showed significantly better remission rates (odds ratio of 1.7) compared to the non-guided (TAU) group (n = 850).3 Given that a high proportion of patients with depression is treated by primary care providers, the utility of PGx testing was evaluated in primary (N = 810) and psychiatric (N = 1061) care settings in a subset of the IMPACT project (a 7-year naturalistic study examining the effects of PGx testing).4 The response rate at follow up tended to be better in patients taking medications congruent with their combinatorial PGx test report (28.0%), in comparison with the patients taking incongruent medications (21.4%, P = 0.057). This study suggested that PGx testing appears to be at least as effective in primary care as in psychiatric care settings, though the response rate was higher in patients treated by primary care providers than in those treated by psychiatrists in this sample (response rate: 30.1% vs 22.3%, respectively, P < 0.01). In summary, PGx testing is promising for psychiatric treatment and can provide substantial benefit to patients; however, there are still several issues to be addressed. First, most studies have evaluated patients who have not responded to previous treatment, and it remains unclear whether these tests are beneficial and cost-effective enough to be applied pre-emptively for the entire patient population at the beginning of their treatment. More prospective RCTs are needed that will evaluate factors such as level of treatment resistance and cost–benefit analyses. Second, it is important to keep in mind that test panels across academic centers and industry differ substantially. For instance, the level of agreement in psychotropic medication recommendations across four PGx tests was reported to be modest as follows: antidepressants (56%), anxiolytics/hypnotics (56%), and antipsychotics (55%), with the exception of mood stabilizers (84%).5 This finding indicates that PGx tests cannot be considered as interchangeable, which highlights the need for test standardization and further prospective RCTs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".