Supervised Classification of CYP2D6 Genotype and Metabolizer Phenotype With Postmortem Tramadol-Exposed Finns

Effect of CYP2D6, 2C19, and 3A4 Phenoconversion in Drug-Related Deaths

Molecular autopsy is a very important tool in forensic toxicology. However, many determinants, such as co-medication and physiological parameters, should be considered for optimal results. These determinants could cause phenoconversion (PC), a discrepancy between the real metabolic profile after phenoconversion and the phenotype determined by the genotype. This study's objective was to assess the PC of drug-metabolizing enzymes, namely CYP2D6, 2C19, and 3A4, in 45 post-mortem cases where medications that are substrates, inducers, or inhibitors of these enzymes were detected. It also intended to evaluate how PC affected the drug's metabolic ratio (MR) in four cases. Blood samples from 45 cases of drug-related deaths were analyzed to detect and determine drug and metabolite concentrations. Moreover, all the samples underwent genotyping utilizing the HaloPlex Target Enrichment System for CYP2D6, 2C19, and 3A4. The results of the present study revealed a statistically significant rate of PC for the three investigated enzymes, with a higher frequency of poor metabolizers after PC. A compatibility was seen between the results of the genomic evaluation after PC and the observed MRs of venlafaxine, citalopram, and fentanyl. This leads us to focus on the determinants causing PC that may be mainly induced by drug interactions. This complex phenomenon can have a significant impact on the analysis, interpretation of genotypes, and accurate conclusions in forensic toxicology. Nevertheless, more research with more cases in the future is needed to confirm these results.

Metabolic ratios and SNPs implicated in tramadol-related deaths

Tramadol (TR) metabolism is performed by polymorphic enzymes that are influenced by genetic polymorphisms. Within this scope, the study presented here aimed to describe 41 genetic variants within CYP2D6, CYP2B6, and CYP3A4 genes in 48 cases of TR-related death that may be involved in the response to TR and to assess whether there is a correlation between these genetic variants and metabolic ratios (MRs). Blood samples from 48 victims of a TR-related death were analyzed to determine the concentrations of TR and its metabolites [O-desmethyltramadol (M1) & N-desmethyltramadol (M2)] using a LC-MS/MS method. All the samples were also genotyped for 41 common CYP2D6, CYP2B6, and CYP3A4 single nucleotide polymorphisms (SNPs) using the HaloPlex Target Enrichment system. Cases with the T/- genotype (rs35742686 in CYP2D6) had significantly higher M2/M1 ratio than cases with T/T genotype and cases with the G/A genotype (rs35599367 in CYP3A4) had significantly higher MR2 (TR/M2) ratio than cases with G/G genotype. The frequency of tested SNPs which belong to CYP2D6, CYP2B6, and CYP3A4 revealed the over-presentation of 2 SNPs (rs1058172 in CYP2D6 and rs4803419 in CYP2B6) in TR overdose group, which could have toxicological implications. These results indicate these polymorphisms in CYP2D6, CYP2B6, and CYP3A4 might influence the function and could increase the risk of toxicity. However, these findings should be supported in future studies with larger groups of cases.

Pharmacogenetics of Lethal Opioid Overdose: Review of Current Evidence and Preliminary Results from a Pilot Study

There has been a worldwide substantial increase in accidental opioid-overdose deaths. The aim of this review, along with preliminary results from our pilot study, is to highlight the use of pharmacogenetics as a tool to predict causes of accidental opioid-overdose death. For this review, a systematic literature search of PubMed^® between the time period of January 2000 to March 2023 was carried out. We included study cohorts, case-controls, or case reports that investigated the frequency of genetic variants in opioid-related post-mortem samples and the association between these variants and opioid plasma concentrations. A total of 18 studies were included in our systematic review. The systematic review provides evidence of the use of CYP2D6, and to a lower extent, CYP2B6 and CYP3A4/5 genotyping in identifying unexpectedly high or low opioid and metabolite blood concentrations from post-mortem samples. Our own pilot study provides support for an enrichment of the CYP2B6*4-allele in our methadone-overdose sample (n = 41) compared to the anticipated frequency in the general population. The results from our systematic review and the pilot study highlight the potential of pharmacogenetics in determining vulnerability to overdose of opioids.

The Evaluation of CYP2D6, CYP2C9, CYP2C19, and CYP2B6 Phenoconversion in Post-Mortem Casework: The Challenge of Forensic Toxicogenetics

In toxicogenetics, an integrative approach including the prediction of phenotype based on post-mortem genotyping of drug-metabolising enzymes might help explain the cause of death (CoD) and manner of death (MoD). The use of concomitant drugs, however, might lead to phenoconversion, a mismatch between the phenotype based on the genotype and the metabolic profile actually observed after phenoconversion. The aim of our study was to evaluate the phenoconversion of CYP2D6, CYP2C9, CYP2C19, and CYP2B6 drug-metabolising enzymes in a series of autopsy cases tested positive for drugs that are substrates, inducers, or inhibitors of these enzymes. Our results showed a high rate of phenoconversion for all enzymes and a statistically significant higher frequency of poor and intermediate metabolisers for CYP2D6, CYP2C9, and CYP2C19 after phenoconversion. No association was found between phenotypes and CoD or MoD, suggesting that, although phenoconversion might be useful for a forensic toxicogenetics approach, more research is needed to overcome the challenges arising from the post-mortem setting.

Correlation of CYP2D6 allelic polymorphism to outcome of acute coronary syndrome in mid-Euphrates Iraqi patients on metoprolol therapy

This study aims to investigate the different clinically relevant allele variants (allele frequencies) of CYP2D6 gene and to determine whether a specific genotype of CYP2D6 gene (based on genetic polymorphism "allelic types" and combination) have impact on metoprolol effectiveness (clinical outcome) in patients who have acute coronary syndrome (ACS). The study included 250 patients with ACS who were classified into 2 study groups, 125 patients received metoprolol and served as a study group (Group1) and 125 who received no metoprolol therapy (due to contraindication to the medication) and served as a control group (Group 2). Venous blood samples were taken from all participants for DNA extraction. Urine samples were also collected to assess the metabolic ratio using High-performance liquid chromatography (HPLC) technique. There was significant variation in the distribution of Iraqi patients with respect to CYP2D6 allelic polymorphism as compared to similar patients in other countries. Besides, this significant difference existed in patients' outcome in terms of morbidity and mortality in respect to variable genotypes and phenotypes. We recommend a dose individualization of metoprolol in patients with ACS is essential to improve patients' outcome.

A pathway-driven predictive model of tramadol pharmacogenetics

Predicting metabolizer phenotype (MP) is typically performed using data from a single gene. Cytochrome p450 family 2 subfamily D polypeptide 6 (CYP2D6) is considered the primary gene for predicting MP in reference to approximately 30% of marketed drugs and endogenous toxins. CYP2D6 predictions have proven clinically effective but also have well-documented inaccuracies due to relatively high genotype-phenotype discordance in certain populations. Herein, a pathway-driven predictive model employs genetic data from uridine diphosphate glucuronosyltransferase, family 1, polypeptide B7 (UGT2B7), adenosine triphosphate (ATP)-binding cassette, subfamily B, number 1 (ABCB1), opioid receptor mu 1 (OPRM1), and catechol-O-methyltransferase (COMT) to predict the tramadol to primary metabolite ratio (T:M1) and the resulting toxicologically inferred MP (t-MP). These data were then combined with CYP2D6 data to evaluate performance of a fully combinatorial model relative to CYP2D6 alone. These data identify UGT2B7 as a potentially significant explanatory marker for T:M1 variability in a population of tramadol-exposed individuals of Finnish ancestry. Supervised machine learning and feature selection were used to demonstrate that a set of 16 loci from 5 genes can predict t-MP with over 90% accuracy, depending on t-MP category and algorithm, which was significantly greater than predictions made by CYP2D6 alone.