Prediction and visualization of CYP2D6 genotype-based phenotype using clustering algorithms

Abstract

This study focused on the role of cytochrome P450 2D6 (CYP2D6) genotypes to predict phenotypes in the metabolism of dextromethorphan. CYP2D6 genotypes and metabolic ratios (MRs) of dextromethorphan were determined in 201 Koreans. Unsupervised clustering algorithms, hierarchical and k-means clustering analysis, and color visualizations of CYP2D6 activity were performed on a subset of 130 subjects. A total of 23 different genotypes were identified, five of which were observed in one subject. Phenotype classifications were based on the means, medians, and standard deviations of the log MR values for each genotype. Color visualization was used to display the mean and median of each genotype as different color intensities. Cutoff values were determined using receiver operating characteristic curves from the k-means analysis, and the data were validated in the remaining subset of 71 subjects. Using the two highest silhouette values, the selected numbers of clusters were three (the best) and four. The findings from the two clustering algorithms were similar to those of other studies, classifying *5/*5 as a lowest activity group and genotypes containing duplicated alleles (i.e., CYP2D6*1/*2N) as a highest activity group. The validation of the k-means clustering results with data from the 71 subjects revealed relatively high concordance rates: 92.8% and 73.9% in three and four clusters, respectively. Additionally, color visualization allowed for rapid interpretation of results. Although the clustering approach to predict CYP2D6 phenotype from CYP2D6 genotype is not fully complete, it provides general information about the genotype to phenotype relationship, including rare genotypes with only one subject.

Physiologically based pharmacokinetic (PBPK) modeling of the role of CYP2D6 polymorphism for metabolic phenotyping with dextromethorphan

“…During the last 20 years various modeling approaches and software solutions were utilized to investigate various aspects of DXM pharmacokinetics, e.g., using GastroPlus ( Bolger et al, 2019 ), P-Pharm ( Moghadamnia et al, 2003 ), SAS ( Ito et al, 2010 ; Chiba et al, 2012 ), SimCYP ( Dickinson et al, 2007 ; Ke et al, 2013 ; Sager et al, 2014 ; Chen et al, 2016 ; Rougée et al, 2016 ; Adiwidjaja et al, 2018 ; Storelli et al, 2019b ; Machavaram et al, 2019 ), MATLAB ( Kim et al, 2017 ), or PK-Sim ( Rüdesheim et al, 2022 ). However, most of the work is difficult/impossible to validate or to build up on due to a lack of accessibility of models and software, and platform-dependency of the models.…”

Physiologically based pharmacokinetic (PBPK) modeling of the role of CYP2D6 polymorphism for metabolic phenotyping with dextromethorphan

“…During the last 20 years various modeling approaches and software solutions were utilized to investigate various aspects of DXM pharmacokinetics, e.g. using GastroPlus (Bolger et al, 2019), P-Pharm (Moghadamnia et al, 2003), SAS (Ito et al, 2010; Chiba et al, 2012), SimCYP (Dickinson et al, 2007; Ke et al, 2013; Sager et al, 2014; Chen et al, 2016; Rougée et al, 2016; Adiwidjaja et al, 2018; Machavaram et al, 2019; Storelli et al, 2019b), MATLAB (Kim et al, 2017), or PK-Sim (Rüdesheim et al, 2022). However, most of the work is difficult/impossible to validate or to build up on due to a lack of accessibility of models and software, and platform-independence of the models.…”

Review on Databases and Bioinformatic Approaches on Pharmacogenomics of Adverse Drug Reactions

“…127 Some others used hierarchical or k-means clustering to detect the correlation between genotype and phenotype. 128 Some recent tools such as Hubble have applied deep learning techniques to predict the functions of PGx alleles. 129 Nevertheless, classifying genomic variants to explore the correlation of genes related to ADRs is still challenging and needs significant improvement.…”