Estimation of the benefit from pre-emptive genotyping based on the nationwide cohort data in South Korea.
Ki Young HuhSejung HwangJoo Young NaKyung-Sang YuIn-Jin JangJae-Young ChungSeonghae YoonPublished in: Clinical and translational science (2024)
Genetic variants affect drug responses, making pre-emptive genotyping crucial for averting serious adverse events (SAEs) and treatment failure. However, assessing the benefits of pre-emptive genotyping based on genetic distribution, drug exposure, and demographics is challenging. This study aimed to estimate the population-level benefits of pre-emptive genotyping in the Korean population using nationwide cohort data. We reviewed actionable gene-drug combinations recommended by both the Clinical Pharmacogenomics Implementation Consortium (CPIC) and the Dutch Pharmacogenetics Working Group (DPWG) as of February 2022, identifying high-risk phenotypes. We collected reported risk reduction from genotyping and standardized it into population attributable risks. Healthcare reimbursement costs for SAEs and treatment failures were obtained from the Health Insurance Review and Assessment Service Statistics in 2021. The benefits of pre-emptive genotyping for a specific group were determined by multiplying drug exposure from nationwide cohort data by individual genotyping benefits. We identified 31 gene-drug-event pairs, with CYP2D6 and CYP2C19 demonstrating the greatest benefits for both male and female patients. Individuals aged 65-70 years had the highest individual benefit from pre-emptive genotyping, with $84.40 for men and $100.90 for women. Pre-emptive genotyping, particularly for CYP2D6 and CYP2C19, can provide substantial benefits.
Keyphrases
- genome wide
- high throughput
- healthcare
- genetic diversity
- health insurance
- dna methylation
- copy number
- end stage renal disease
- ejection fraction
- type diabetes
- cross sectional
- chronic kidney disease
- drug induced
- primary care
- newly diagnosed
- gene expression
- emergency department
- climate change
- single cell
- machine learning
- patient reported outcomes
- adipose tissue
- affordable care act
- polycystic ovary syndrome
- deep learning
- clinical decision support
- skeletal muscle
- genome wide identification