Exploring the Pharmacogenomic Map of Croatia: PGx Clustering of 522-Patient Cohort Based on UMAP + HDBSCAN Algorithm

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 s...

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Bibliographic Details
Published in:International journal of molecular sciences Vol. 26; no. 2; p. 589
Main Authors: Brlek, Petar, Bulić, Luka, Mršić, Leo, Sokač, Mateo, Brenner, Eva, Matišić, Vid, Skelin, Andrea, Bach-Rojecky, Lidija, Primorac, Dragan
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 12.01.2025
MDPI
Subjects:
Adult
Aged
Algorithms
Cluster Analysis
Clustering
Cohort Studies
Croatia
Cytochrome P-450 CYP2D6 - genetics
Data analysis
Decision Trees
Drug therapy
Enzymes
Female
Genes
Genotype
Genotype & phenotype
Humans
Liver-Specific Organic Anion Transporter 1 - genetics
Machine learning
Male
Medical research
Medicine, Experimental
Metabolism
Middle Aged
Patients
Pharmacogenetics
Pharmacogenetics - methods
Pharmacogenomic Testing
Precision Medicine
Side effects
Technology application
Toxicity
Croatia
adverse drug reactions
dimensionality reduction
unsupervised machine learning
hierarchical density-based clustering
pharmacogenetics
ISSN:1422-0067, 1661-6596, 1422-0067
Online Access:Get full text
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Summary: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.
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These authors contributed equally to this work.
ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms26020589