Comparison of Multidrug Use in the General Population and among Persons with Diabetes in Denmark for Drugs Having Pharmacogenomics (PGx) Based Dosing Guidelines

Use of drugs with pharmacogenomics (PGx)-based dosing guidelines in a Danish cohort of persons with chronic kidney disease, both on dialysis and not on dialysis: Perspectives for prescribing optimization

AIM

The objective of this registry study is to assess the utilization of pharmacogenomic (PGx) drugs among patients with chronic kidney disease (CKD).

METHODS

This study was a retrospective study of patients affiliated with the Department of Nephrology, Aalborg University Hospital, Denmark in 2021. Patients diagnosed with CKD were divided into CKD without dialysis and CKD with dialysis. PGx prescription drugs were retrieved from the Patient Administration System. Actionable dosing guidelines (AG) for specific drug-gene pairs for CYP2D6, CYP2C9, CYP2C19 and SLCO1B1 were retrieved from the PharmGKB homepage.

RESULTS

Out of 1241 individuals, 25.5% were on dialysis. The median number of medications for each patient was 9 within the non-dialysis group and 16 within the dialysis group. Thirty-one distinct PGx drugs were prescribed. Altogether, 76.0% (943 individuals) were prescribed at least one PGx drug and the prevalence of prescriptions of PGx drugs was higher in the dialysis group compared to the non-dialysis group. The most frequently prescribed drugs with AG were metoprolol, pantoprazole, atorvastatin, simvastatin and warfarin.

CONCLUSION

This study demonstrated that a substantial proportion of patients with CKD are exposed to drugs or drug combinations for which there exists AG related to PGx of CYP2D6, CYP2C19, CYP2C9 and SLCO1B1.

Pharmacogenes that demonstrate high association evidence according to CPIC, DPWG, and PharmGKB

BACKGROUND

Different levels of evidence related to the variable responses of individuals to drug treatment have been reported in various pharmacogenomic (PGx) databases. Identification of gene-drug pairs with strong association evidence can be helpful in prioritizing the implementation of PGx guidelines and focusing on a gene panel. This study aimed to determine the pharmacogenes with the highest evidence-based association and to indicate their involvement in drug-gene interactions.

METHODOLOGY

The publicly available datasets CPIC, DPWG, and PharmGKB were selected to determine the pharmacogenes with the highest drug outcome associations. The upper two levels of evidence rated by the three scoring methods were specified (levels A-B in CPIC, 3-4 in DPWG, or 1-2 levels in PharmGKB). The identified pharmacogenes were further ranked in this study based on the number of medications they interacted with.

RESULTS

Fifty pharmacogenes, with high to moderately high evidence of associations with drug response alterations, with potential influence on the therapeutic and/or toxicity outcomes of 152 drugs were identified. CYP2D6, CYP2C9, CYP2C19, G6PD, HLA-B, SLCO1B1, CACNA1S, RYR1, MT-RNR1, and IFNL4 are the top 10 pharmacogenes, where each is predicted to impact patients' responses to ≥5 drugs.

CONCLUSION

This study identified the most important pharmacogenes based on the highest-ranked association evidence and their frequency of involvement in affecting multiple drugs. The obtained data is useful for customizing a gene panel for PGx testing. Identifying the strength of scientific evidence supporting drug-gene interactions aids drug prescribers in making the best clinical decision.