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Evaluating genotype-treatment interactions for high-risk medications in British general practice: a retrospective
Kinan Mokbel1, Michael Weedon2, Rob Daniels3
1Department of Health and Care Professions, Faculty of Health and Life Sciences, University of Exeter Medical School, Exeter, UK K.A.Mokbel@exeter.ac.uk.
Background:
Pharmacogenetics has the potential to optimise drug therapy and reduce adverse drug effects (ADEs) by tailoring treatment to a patient's genotype, particularly for chronic disorders managed in general practice. However, the adoption of pharmacogenetics in general practice remains slow.
Aim:
To evaluate the reproducibility of previously reported associations between genomic variants and medically important adverse drug effects (MIADEs) associated with high-risk medications in general practice.
Design And Setting:
A retrospective study using data from the UK Biobank (UKBB), a population-based cohort of over 500 000 community-based participants.
Method:
High-risk medications prescribed in general practice were identified by linking serious ADEs from the Yellow Card database with English general practice prescription data. These high-risk medications were then cross-examined with genomic variants associated with MIADEs from the Pharmacogenomics Knowledgebase (PharmGKB) to select variant-drug pairs for investigation within the UKBB.
Results:
From 78 high-risk medications prescribed in general practice and 56 PharmGKB annotations linked to MIADE risk, SLCO1B1 rs4149056 was the only variant with guideline-based prescribing recommendations. This variant, along with others of lower evidence levels, was analysed in the UKBB. No genotype-treatment interaction was observed for SLCO1B1 rs4149056 and statin-related muscle toxicity. Similarly, no interactions were detected for the remaining variants in either secondary or exploratory analyses.
Conclusion:
No statistically significant genotype-treatment interactions were observed for MIADE risk associated with high-risk medications in general practice. However, the limited predictive value of the assessed variants may reflect underlying phenotypic imprecision and methodological limitations. Hence, further research is needed to validate these results.
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