Economic Evaluation of Pre-emptive Pharmacogenetic Panel Testing versus No Genetic Testing in a Multi-ethnic Asian...

The cost-utility of a panel-based pre-emptive pharmacogenomic (PPGx) test has not been evaluated in a multi-ethnic Asian population. Prior studies have largely focused on reactive, single drug-gene tests. This study assessed the cost-utility of a PPGx panel test and identified key drivers influencing its economic value.

We developed a prioritization framework integrating clinical and economic criteria to select drug-gene pairs for economic analysis. Cost-utility analysis was conducted using Discretely Integrated Condition Event (DICE) simulation, which allowed simultaneous analysis of multiple diseases and treatments of varying duration. The analysis focused on a hypothetical cohort of healthy 40-year-old Singaporeans and assessed the lifetime impact of a one-time panel test on outcomes such as disease occurrence and serious adverse drug events (ADE). Costs were evaluated from a healthcare payer's perspective and reported in 2024 Singapore dollars (S$). Both costs and health outcomes were discounted at 3% annually. Deterministic, probabilistic, and scenario analyses were performed to address uncertainty.

Four drug-gene pairs were selected: clopidogrel-CYP2C19, capecitabine-DPYD, allopurinol-HLA-B*58:01, and simvastatin-SLCO1B1. In the base case, panel testing was dominant, resulting in savings of S$37,600 and gain of 9.32 quality-adjusted life years (QALYs) per 1000 individuals compared with no PGx testing. Results were sensitive to drug costs, ADE-related costs, and the age for panel administration. Ideal drug-gene pairs for panel inclusion involve commonly prescribed drugs with variants associated with severe ADEs, where genotype-guided alternatives (e.g., dose adjustment or switching therapy) have costs comparable to standard care.

Pre-emptive PGx panel testing is economically viable when panel design, variant prevalence, drug costs, and local prescribing patterns are carefully considered. As more data become available, the model can be tailored to evaluate additional drug-gene pairs and their downstream consequences.