Identification of Escitalopram Metabolic Ratios as Potential Biomarkers for Predicting CYP2C19 Poor Metabolizers

Combined effect of CYP2C19 and CYP2D6 genotypes on escitalopram serum concentration and its metabolic ratio in a European patient population

AIMS

The aim of the present study was to investigate the impact of CYP2D6 genotype on exposure and metabolism of escitalopram in patients stratified by CYP2C19 genotype in a large real-world population.

METHODS

Patients were included from a therapeutic drug monitoring service if they had measured serum concentration of escitalopram and the metabolite, N-desmethyl escitalopram, and performed CYP2C19 and CYP2D6 genotyping. Patients were divided into 16 combined genotype-predicted phenotype subgroups (poor [PM], intermediate [IM], normal [NM] and ultrarapid metabolizers [UM]) of CYP2C19/CYP2D6. The concentration-to-dose (CD) ratio and metabolite-to-parent ratio (metabolic ratio) of escitalopram were compared across subgroups using the Kruskal-Wallis test followed by Dunn's test with CYP2D6 NMs as the reference group.

RESULTS

A total of 5067 patients were included in the study. A stepwise increase in escitalopram CD ratio by decreasing CYP2D6 activity was observed in all CYP2C19 subgroups, except for in CYP2C19 UMs. The percentage differences in escitalopram CD ratio between CYP2D6 PMs and NMs were 24% in CYP2C19 NMs (P < .001), 28% in CYP2C19 IMs (P < .001) and 31% in CYP2C19 PMs (P = .04). As for the CD ratio, CYP2D6 genotype effect on metabolic ratio increased stepwise by decreasing CYP2C19 metabolism.

CONCLUSIONS

CYP2D6 genotype is of significant importance for the individual variation in escitalopram pharmacokinetics. The most relevant increase in escitalopram concentration is seen in individuals with decreased and/or absent CYP2C19 activity. By combining CYP2C19 and CYP2D6 genotypes, the optimal dose for patients may be predicted with greater precision than for CYP2C19 genotype alone.

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.

Pro-arrhythmic effect of escitalopram and citalopram at serum concentrations commonly observed in older patients - a study based on a cohort of 19,742 patients

BACKGROUND

For a decade, patients have been advised against using high citalopram- and escitalopram-doses due to risk for ventricular arrhythmia and cardiac arrest. Still, these drugs are widely used to treat depression and anxiety especially in older patients. It is unclear why they are cardiotoxic and at what serum concentrations patients are at risk for arrhythmias. Thus, how many patients that are at risk for iatrogenic cardiac arrest is unknown.

METHODS

We studied the arrhythmogenic effects of citalopram, escitalopram and their metabolites on human cardiomyocytes. Concentrations showing pro-arrhythmic activity were compared with observed drug and metabolite serum concentrations in a cohort of 19,742 patients (age 12-105 years) using escitalopram or citalopram in Norway (2010-2019). As arrhythmia-risk is related to maximum serum concentration, this was simulated for different age-groups from the escitalopram patient material.

FINDINGS

Therapeutic concentrations of both citalopram and escitalopram but not their metabolites showed pro-arrhythmic changes in the human cardiac action potential. Due to age-dependent reduction of drug clearance, the proportion of patients above threshold for arrhythmia-risk increased with age. 20% of patients >65 years were predicted to reach potentially pro-arrhythmic concentrations, following intake of 10 mg escitalopram.

INTERPRETATION

All patients that are using escitalopram or citalopram and have genetic disposition for acquired long-QT syndrome, are >65 years, are using additional pro-arrhythmic drugs or have predisposition for arrhythmias, should be monitored with therapeutic drug monitoring (TDM) to avoid exposure to potentially cardiotoxic concentrations. Serum concentrations should be kept below 100 nM, to reduce arrhythmia-risk.

FUNDING

This study was funded by The Research Council of Norway (project number: 324062).

Pharmacogenetic Dose Modeling Based on CYP2C19 Allelic Phenotypes

Pharmacogenetic variability in drug metabolism leads to patient vulnerability to side effects and to therapeutic failure. Our purpose was to introduce a systematic statistical methodology to estimate quantitative dose adjustments based on pharmacokinetic differences in pharmacogenetic subgroups, addressing the concerns of sparse data, incomplete information on phenotypic groups, and heterogeneity of study design. Data on psychotropic drugs metabolized by the cytochrome P450 enzyme CYP2C19 were used as a case study. CYP2C19 activity scores were estimated, while statistically assessing the influence of methodological differences between studies, and used to estimate dose adjustments in genotypic groups. Modeling effects of activity scores in each substance as a population led to prudential predictions of adjustments when few data were available ('shrinkage'). The best results were obtained with the regularized horseshoe, an innovative Bayesian approach to estimate coefficients viewed as a sample from two populations. This approach was compared to modeling the population of substance as normally distributed, to a more traditional "fixed effects" approach, and to dose adjustments based on weighted means, as in current practice. Modeling strategies were able to assess the influence of study parameters and deliver adjustment levels when necessary, extrapolated to all phenotype groups, as well as their level of uncertainty. In addition, the horseshoe reacted sensitively to small study sizes, and provided conservative estimates of required adjustments.