Pharmacogenomics in epilepsy care across Asia

Area of Science:

• Pharmacogenomics
• Epilepsy Treatment
• Clinical Decision-Making

Background:

• Current anti-seizure medication (ASM) selection for epilepsy is empirical, resulting in variable efficacy and significant adverse drug reactions (ADRs).
• Pharmacogenomic strategies offer personalized ASM selection by integrating genetic data, yet few gene-drug associations have strong clinical applicability.
• Key Asian pharmacogenomic markers like HLA-B*15:02 and HLA-A31:01 are linked to severe cutaneous adverse reactions (SCARs), influencing regional policies.

Purpose of the Study:

• To propose evidence-based criteria for implementing pharmacogenomic markers in clinical practice for epilepsy.
• To evaluate the current evidence supporting gene-drug associations for ASM selection.
• To identify real-world challenges and future research directions for pharmacogenomic implementation in epilepsy.

Main Methods:

• Review of existing gene-drug associations in epilepsy, focusing on replicated evidence and clinical significance.
• Analysis of regional regulatory policies and their impact on pharmacogenomic marker implementation.
• Identification of practical challenges, including allele frequencies, healthcare infrastructure, and urgent care settings.

Main Results:

• HLA-B*15:02 screening prior to carbamazepine use has the strongest evidence for mandatory implementation due to its association with SCARs.
• Other pharmacogenomic markers remain advisory due to limited outcome data and variable clinical utility.
• Real-world implementation faces challenges such as incomplete risk prediction, overlapping hypersensitivity profiles, and workflow limitations.

Conclusions:

• Evidence-based criteria are crucial for the effective clinical implementation of pharmacogenomic markers in epilepsy.
• HLA-B*15:02 screening is currently the most clinically actionable pharmacogenomic test for ASM selection.
• Future research should focus on multiethnic data, health-economic evaluations, and advanced multi-omics approaches to improve personalized epilepsy treatment.