Abstract
Pharmacogenetics is a branch of genomic medicine aiming to personalize drug prescription guidelines based on individual genetic information. This concept might lead to a reduction in adverse drug reactions, which place a heavy burden on individual patients’ health and the economy of the healthcare system. The aim of this study was to present insights gained from the pharmacogenetics-based clustering of over 500 patients from the Croatian population. The data used in this article were obtained by the pharmacogenetic testing of 522 patients from the Croatian population. The patients were clustered based on the genotypes of 28 pharmacologically relevant genes. Dimensionality reduction was employed using the UMAP algorithm, after which clusters were defined using HDBSCAN. Validation of clustering was performed by decision tree analysis and predictive modeling using the RandomForest, XGBoost, and ExtraTrees classification algorithms. The clustering algorithm defined six clusters of patients based on two UMAP components (silhouette score = 0.782). Decision tree analysis demonstrated CYP2D6 and SLCO1B1 genotypes as the main points of cluster determination. Predictive modeling demonstrated an excellent ability to discern the cluster of each patient based on all genes (avg. ROC-AUC = 0.998), CYP2D6 and SLCO1B1 (avg. ROC-AUC = 1.000), and CYP2D6 alone (avg. ROC-AUC = 0.910). Membership in each cluster provided clinically relevant information, in the context of ruling out certain favorable or unfavorable phenotypes. However, this study’s main limitation is its cohort size. Through further research and investigation of a larger number of patients, more accurate and clinically applicable associations between pharmacogenetic genotypes and phenotypes might be discovered.
Published Version
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