Prevalence Estimates of Cytochrome <scp>P450</scp> Phenoconversion in Youth Receiving Pharmacotherapy for Mental Health Conditions
Bibliographic record
Abstract
Pharmacogenetics-predicted drug metabolism may not match clinically observed metabolism due to a phenomenon known as phenoconversion. Phenoconversion can occur when an inhibitor or inducer of a drug-metabolizing enzyme is present. Although estimates of phenoconversion in adult populations are available, prevalence estimates in youth populations are limited. To address this gap, we estimated the prevalence of phenoconversion in 1281 youth (6-24 years) receiving pharmacotherapy for mental health conditions and who had pharmacogenetics testing completed for four genes (CYP2B6, CYP2C19, CYP2D6, CYP3A4). Self-reported medication and cannabidiol/cannabis use were collected at the time of pharmacogenetics testing. Nearly, half (46%) of the cohort was estimated to be phenoconverted for one of the four genes examined. Comparison of metabolizer phenotype frequencies before and after adjustment for phenoconversion showed significantly more youth had actionable phenotypes for CYP2C19 (60.3% vs. 69.1%; P =< 0.001), CYP2D6 (49.3% vs. 63.0%; P =< 0.001), and CYP3A4 (8.5% vs.12.2%; P = 0.003) after phenoconversion adjustment. Of youth who were phenoconverted, 24% had a change in their metabolizer phenotype that would lead to current pharmacogenetics-based prescribing guidelines recommending a change to standard prescribing (dose adjustment, alternative medication). Our findings indicate a high prevalence of cytochrome P450 phenoconversion among youth receiving pharmacotherapy for mental health conditions. Adjustment for phenoconversion should be considered when implementing pharmacogenetics testing in youth populations to improve the clinical utility of this testing in practice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".