A systematic review of real-world evidence on the clinical relevance, characterization, and utility of CYP2D6 biomarker testing.

Pharmacogenomic (PGx) research investigates how an individual's genetic make-up impacts their drug metabolism. PGx testing can therefore inform therapeutic decision-making, especially as compelling evidence develops over time to substantiate its clinical and personal utility across a range of therapeutic areas. PGx biomarker CYP2D6, in particular, is widely implicated in drug metabolism and across several therapeutic areas. Real-world evidence (RWE) derived intentionally using electronic health record (EHR) and insurance claims data presents an opportunity to explore clinical-behavioral outcomes and implementation barriers and facilitators for PGx testing in real-world clinical settings. In this systematic review, we explored these areas with a focus on PGx biomarker CYP2D6, investigating drug-gene pairs with strong evidence (Level A, Final classification by the Clinical Pharmacogenetics Implementation Consortium [CPIC]). Across 25 studies that met our study inclusion criteria, nine (9) drug-gene pairs that met the CPIC Level A, Final, strong evidence category for CYP2D6 were described. Overarching qualitative themes across studies 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], gastroenterology [n = 6]) with the two most researched drugs being codeine (n = 21) and tramadol (n = 18). Six (6) of 25 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. Special EHR and claims data considerations for future work include but are not limited to addressing inconsistent phenotype categorizations (i.e., natural genotype versus phenoconversion); lack of reliable racial, ethnic, and genetic ancestry data within EHR and claims data sources; and data inoperability issues between PGx test results and EHRs.