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Published on: June 6, 2025
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[Predictive genomic markers for severe adverse drug reactions]
Yoshiro Saito1, Susumu Kodama, Emiko Sugiyama
1Division of Medicinal Safety Science, National Institute of Health Sciences.
Summary
Genomic biomarkers, like HLA types, are linked to severe adverse drug reactions such as Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN). Identifying these genetic markers can help predict and prevent dangerous drug reactions, especially in Asian populations.
Area of Science:
- Pharmacogenomics
- Immunogenetics
- Drug Metabolism
Context:
- Severe adverse drug reactions (SADRs), including Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN), pose significant risks during postmarketing drug surveillance.
- Idiosyncratic SADRs are often unpredictable and unrelated to a drug's primary pharmacological targets.
- Genomic advancements have identified specific genetic markers associated with certain SADRs, highlighting ethnic variations.
Purpose:
- To review and demonstrate genomic biomarkers associated with SJS/TEN and drug-induced liver injury (DILI).
- To highlight ethnic differences in biomarker frequencies, particularly in Japanese and Caucasian populations.
- To discuss the current application and future directions for utilizing genomic biomarkers in preventing SADRs.
Summary:
- Specific human leukocyte antigen (HLA) alleles and cytochrome P450 (CYP) variants are strongly associated with SJS/TEN and DILI.
- Notable associations include HLA-B*58:01 for allopurinol and CYP2C9*3 for phenytoin, with varying allele frequencies across Asian populations.
- Direct binding of drugs or metabolites to HLA proteins is suggested as a mechanism for some reactions.
Impact:
- Genomic biomarkers offer potential for predicting and preventing severe adverse drug reactions.
- Current utilization of these biomarkers varies globally, with limited implementation in Japan's drug labeling.
- Further research is needed to identify additional risk factors and develop cost-effective genotyping strategies for pharmacoeconomic viability.
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