An Overview of Pharmacogenomic Testing for Psychiatric Disorders
Bibliographic record
Abstract
Pharmacogenomic testing is a precision medicine technology that examines genetic variation in medication metabolism. Selected for inclusion in CADTH’s 2023 Watch List, pharmacogenomic testing has the potential to significantly influence the landscape of health care in Canada over the next 5 years. By analyzing an individual’s unique genetic profile, this testing aims to guide personalized treatment strategies that improve therapeutic outcomes, optimize the medication selection process, and enhance patient experiences. The integration of pharmacogenomic testing into clinical practice may pave the way for a more efficient and effective delivery of mental health care. Pharmacogenomic tests for psychiatric disorders that are available in Canada include direct-to-consumer tests — which are paid out of pocket — and tests that are offered as a laboratory service (which may or may not be paid out of pocket). These tests are quite different from each other, including the types and number of genes examined, cost of testing, and methods used for sample collection and analysis. Despite being available for approximately 20 years and that numerous guideline-developing groups and regulators have issued recommendations about the types of pharmacogenomic information that should be used to guide prescribing decisions, pharmacogenomic testing has yet to be integrated into most psychiatric care practices in Canada and worldwide. Although some studies suggest pharmacogenomic testing provides benefits over treatment as usual, the evidence is often conflicting and of limited quality. Concerns related to risk of bias, inconsistency across studies, reproducibility, and generalizability do not allow a clear and consistent interpretation of the results. This Horizon Scan provides an overview of information related to pharmacogenomic testing for psychiatric disorders, a description of some of the published studies, and a summary of some important considerations related to clinician education and training, privacy and confidentiality of health data, health equity, and laboratory capacity should testing become more widely used in Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".