448 A systematic review of real-world evidence on the clinical relevance, characterization, and utility of CYP2D6 biomarker testing
Bibliographic record
Abstract
Objectives/Goals: Limited systematic analysis of real-world evidence (RWE) on pharmacogenomic (PGx) testing utility, which can help provider decision-making and avoid serious adverse effects, exists. This study explores clinical-behavioral outcomes and implementation considerations, for PGx testing in real-world clinical settings for PGx biomarker CYP2D6. Methods/Study Population: Our systematic review investigated drug–gene pairs with strong evidence (Level A, Final classification by the Clinical Pharmacogenomics Implementation Consortium [CPIC]) for CYP2D6. Search strings were deployed across Google Scholar, PubMed, Scopus, and Semantic Scholar. Papers were included only if they presented, measured, and/or described real-world clinical or patient and/or provider behavioral outcomes based on EHR or claims data assessment following PGx testing for the CYP2D6 biomarker, included PGx biomarker(s)–drug pairs with CPIC Level A, Final Evidence designations, and published in English. Study quality and bias risks were assessed using the Newcastle–Ottawa scale or A comprehenSive tool to Support rEporting and critical appraiSal…of reSearch outcomes (ASSESS) tools, where applicable. Results/Anticipated Results: Of the 218 articles identified, 25 met our inclusion criteria and explored 9 CPIC Level A, Final CYP2D6–drug pairs. Overarching qualitative themes were 1) variation in CYP2D6 biomarker testing and interpretation and 2) PGx test implementation and data considerations. CYP2D6–drug pairs were reported across four therapeutic areas (analgesia [n = 21], psychiatry [n = 17], oncology [n = 7], and gastroenterology [n = 6]) with the two most researched drugs being codeine (n = 21) and tramadol (n = 18). Six (6) articles reported PGx clinical outcomes, considered to be a “measurable change in symptoms, overall health, ability to function, quality of life, or survival outcomes” in relation to PGx testing. Discussion/Significance of Impact: Our findings indicate a need to address both inconsistent phenotype categorization and racial, ethnic, and genetic ancestry classifications and to improve EHR interoperability with PGx test results. Future RWE-PGx studies should associate dosage and regimen changes to both discrete provider choices and patient clinical-behavioral outcomes.
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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 | 0.027 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".