Alshabeeb MA, Alyabsi M, Aziz MA, Abohelaika S. Pharmacogenes that demonstrate high association evidence according to CPIC, DPWG, and PharmGKB.
Front Med (Lausanne) 2022;
9:1001876. [PMID:
36388934 PMCID:
PMC9640910 DOI:
10.3389/fmed.2022.1001876]
[Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 09/22/2022] [Indexed: 12/20/2023] Open
Abstract
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.
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