Authors
Chris Eisenhart, Rachel Brickey, Joel Mewton
Published in
Clinical and translational science. Volume 19. Issue 10. Pages e70745.
Abstract
The Clinical Pharmacogenetics Implementation Consortium (CPIC) bases its drug-gene recommendations on the assignment of star alleles, which map known genotypes to defined functional categories and corresponding drug dosage guidelines. The star allele framework, first proposed in 1996 for the CYP gene family and later formalized with CPIC's establishment in 2010, remains foundational to pharmacogenomics. However, this system has notable limitations. Its dependence on a restricted set of benchmark single nucleotide polymorphisms (SNPs) excludes rare or novel pathogenic variants that can invalidate a star allele call and lead to incorrect dosing recommendations. Furthermore, nearby non-pathogenic variants can interfere with haplotype interpretation, introducing additional risk of misclassification. To explore how these gaps might be addressed at the analytical layer, we describe PHARMWATCH, a proposed multilayer pharmacogenomics framework that couples standard star-allele assignment with two additional analyses: (1) germline variant screening using the ACMG-based BIAS-2015 classifier and (2) variant interpretation in context (VIIC), which reconstructs the observed amino acid sequence at each star-allele-defining locus for comparison against the sequence predicted by the assigned star allele (3). We describe the framework's architecture, specify the flag thresholds and translation rules used by each layer, and illustrate its behavior on a documented DPYD case in which a rare pathogenic variant fell outside the tag-SNP panel used for pre-treatment genotyping. We present PHARMWATCH as an exploratory framework; cohort-scale performance characterization and extension to additional SNP-driven pharmacogenes are identified as priority future work.
PMID:
42876374
Bibliographic data and abstract were imported from PubMed on 15 Oct 2026.
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