Pharmacogenetic Testing of Children and Adolescents with Mental Health Conditions: Real-World Experiences
Bibliographic record
Abstract
Background/Objectives: Medication discontinuation attributable to adverse drug reactions (ADRs) and/or inefficacy remains a concern of psychotropic medications among children and adolescents. Pharmacogenetic (PGx) testing has been proposed to individualize treatment, although its utility remains uncertain. We retrospectively evaluated whether PGx testing of two key metabolism genes (i.e., CYP2C19 and CYP2D6) explains reported episodes of ADRs and treatment inefficacy experienced by children and adolescents with diverse mental health conditions. Methods: PGx testing of CYP2C19 and CYP2D6 was conducted for 100 participants before, during, or after the use of psychotropic medication(s) that have clinical practice guidelines supporting PGx-guided dosing. The theoretical impact on medication dosing was reviewed in the context of clinical guidelines. We then evaluated whether the PGx-inferred metabolizer phenotype was consistent with reported ADR and/or treatment inefficacy. Results: If PGx testing had been performed before the start of treatment, 43% (35/82) of participants would have been recommended dose adjustments or alternative therapy of at least one medication. PGx test results corroborated 8% (6/76) of ADR events and 3% (2/61) of inefficacies. However, no single participant had all prior reported ADRs or inefficacies explained by the results of CYP2C19 nor CYP2D6 testing. Conclusions: Reactive testing of CYP2C19 and CYP2D6 provided limited insight into isolated incidents of psychotropic medication intolerance in this population. No individual’s PGx test results explained all episodes of ADR or suboptimal response. Variation in drug metabolism genes alone does not provide an explanation for multiple episodes of inefficacy or adverse reaction. In the setting of child and adolescent psychiatry, PGx testing is best suited for preemptive use to complement clinical decision making.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".