Relationship between CYP2D6 genotype and haloperidol pharmacokinetics and extrapyramidal symptoms in healthy volunteers

CYP2D6 pharmacogenetics and phenoconversion in personalized medicine

INTRODUCTION

CYP2D6 contributes to the metabolism of approximately 20-25% of drugs. However, CYP2D6 is highly polymorphic and different alleles can lead to impacts ranging from null to increase in activity. Moreover, there are commonly used drugs that potently inhibit the CYP2D6, thus causing 'phenoconversion' which can convert the genotypic normal metabolizer into phenotypic poor metabolizer. Despite growing literature on the clinical implications of non-normal CYP2D6 genotype and phenoconversion on patient-related outcomes, implementation of CYP2D6 pharmacogenetics and phenoconversion to guide prescribing is rare. This review focuses on providing the clinical importance of CYP2D6 pharmacogenetics and phenoconversion in precision medicine and summarizes the challenges and approaches to implement these into clinical practice.

AREAS COVERED

A literature search was performed using PubMed and clinical studies documenting the effects of CYP2D6 genotypes and/or CYP2D6 inhibitors on pharmacokinetics, pharmacodynamics or treatment outcomes of CYP2D6-metabolized drugs, and studies on implementation challenges and approaches.

EXPERT OPINION

Considering the extent and impact of genetic polymorphisms of CYP2D6, phenoconversion by the comedications, and contribution of CYP2D6 in drug metabolism, CYP2D6 pharmacogenetics is essential to ensure drug safety and efficacy. Utilization of proper guidelines incorporating both CYP2D6 pharmacogenetics and phenoconversion in clinical care assists in optimizing drug therapy.

Human variability in polymorphic CYP2D6 metabolism: Implications for the risk assessment of chemicals in food and emerging designer drugs

The major human cytochrome P450 CYP2D6 isoform enzyme plays important roles in the liver and in the brain with regards to xenobiotic metabolism. Xenobiotics as CYP2D6 substrates include a whole range of pharmaceuticals, pesticides and plant alkaloids to cite but a few. In addition, a number of endogenous compounds have been shown to be substrates of CYP2D6 including trace amines in the brain such as tyramine and 5-methoxytryptamine as well as anandamide and progesterone. Because of the polymorphic nature of CYP2D6, considerable inter-phenotypic and inter-ethnic differences in the pharmaco/toxicokinetics (PK/TK) and metabolism of CYP2D6 substrates exist with potential consequences on the pharmacology and toxicity of chemicals. Here, large extensive literature searches have been performed to collect PK data from published human studies for a wide range of pharmaceutical probe substrates and investigate human variability in CYP2D6 metabolism. The computed kinetic parameters resulted in the largest open source database, quantifying inter-phenotypic differences for the kinetics of CYP2D6 probe substrates in Caucasian and Asian populations, to date. The database is available in supplementary material (CYPD6 DB) and EFSA knowledge junction (DOI to added). Subsequently, meta-analyses using a hierarchical Bayesian model for markers of chronic oral exposure (oral clearance, area under the plasma concentration time curve) and acute oral exposure (maximum plasma concentration (Cmax) provided estimates of inter-phenotypic differences and CYP2D6-related uncertainty factors (UFs) for chemical risk assessment in Caucasian and Asian populations classified as ultra-rapid (UM), extensive (EMs), intermediate (IMs) and poor metabolisers (PMs). The model allowed the integration of inter-individual (i.e. inter-phenotypic and inter-ethnic), inter-compound and inter-study variability together with uncertainty in each PK parameter. Key findings include 1. Higher frequencies of PMs in Caucasian populations compared to Asian populations (>8% vs 1-2%) for which EM and IM were the most frequent phenotype. 2. Large inter-phenotypic differences in PK parameters for Caucasian EMs (coefficients of variation (CV) > 50%) compared with Caucasian PMs and Asian EMs and IMs (i.e CV < 40%). 3. Inter-phenotypic PK differences between EMs and PMs in Caucasian populations increase with the quantitative contribution of CYP2D6 for the metabolism (fm) for a range of substrates (fm_CYP2D6 range: 20-95% of dose) (range: 1-54) to a much larger extent than those for Asian populations (range: 1-4). 4. Exponential meta-regressions between Fm_CYP2D6 in EMs and inter-phenotypic differences were also shown to differ between Caucasian and Asian populations as well as CYP2D6-related UFs. Finally, implications of these results for the risk assessment of food chemicals and emerging designer drugs of public health concern, as CYP2D6 substrates, are highlighted and include the integration of in vitro metabolism data and CYP2D6-variability distributions for the development of quantitative in vitro in vivo extrapolation models.

Association of CYP2C19 and CYP2D6 Poor and Intermediate Metabolizer Status With Antidepressant and Antipsychotic Exposure: A Systematic Review and Meta-analysis

IMPORTANCE

Precise estimation of the drug metabolism capacity for individual patients is crucial for adequate dose personalization.

OBJECTIVE

To quantify the difference in the antipsychotic and antidepressant exposure among patients with genetically associated CYP2C19 and CYP2D6 poor (PM), intermediate (IM), and normal (NM) metabolizers.

DATA SOURCES

PubMed, Clinicaltrialsregister.eu, ClinicalTrials.gov, International Clinical Trials Registry Platform, and CENTRAL databases were screened for studies from January 1, 1990, to June 30, 2020, with no language restrictions.

STUDY SELECTION

Two independent reviewers performed study screening and assessed the following inclusion criteria: (1) appropriate CYP2C19 or CYP2D6 genotyping was performed, (2) genotype-based classification into CYP2C19 or CYP2D6 NM, IM, and PM categories was possible, and (3) 3 patients per metabolizer category were available.

DATA EXTRACTION AND SYNTHESIS

The Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines were followed for extracting data and quality, validity, and risk of bias assessments. A fixed-effects model was used for pooling the effect sizes of the included studies.

MAIN OUTCOMES AND MEASURES

Drug exposure was measured as (1) dose-normalized area under the plasma level (time) curve, (2) dose-normalized steady-state plasma level, or (3) reciprocal apparent total drug clearance. The ratio of means (RoM) was calculated by dividing the mean drug exposure for PM, IM, or pooled PM plus IM categories by the mean drug exposure for the NM category.

RESULTS

Based on the data derived from 94 unique studies and 8379 unique individuals, the most profound differences were observed in the patients treated with aripiprazole (CYP2D6 PM plus IM vs NM RoM, 1.48; 95% CI, 1.41-1.57; 12 studies; 1038 patients), haloperidol lactate (CYP2D6 PM vs NM RoM, 1.68; 95% CI, 1.40-2.02; 9 studies; 423 patients), risperidone (CYP2D6 PM plus IM vs NM RoM, 1.36; 95% CI, 1.28-1.44; 23 studies; 1492 patients), escitalopram oxalate (CYP2C19 PM vs NM, RoM, 2.63; 95% CI, 2.40-2.89; 4 studies; 1262 patients), and sertraline hydrochloride (CYP2C19 IM vs NM RoM, 1.38; 95% CI, 1.27-1.51; 3 studies; 917 patients). Exposure differences were also observed for clozapine, quetiapine fumarate, amitriptyline hydrochloride, mirtazapine, nortriptyline hydrochloride, fluoxetine hydrochloride, fluvoxamine maleate, paroxetine hydrochloride, and venlafaxine hydrochloride; however, these differences were marginal, ambiguous, or based on less than 3 independent studies.

CONCLUSIONS AND RELEVANCE

In this systematic review and meta-analysis, the association between CYP2C19/CYP2D6 genotype and drug levels of several psychiatric drugs was quantified with sufficient precision as to be useful as a scientific foundation for CYP2D6/CYP2C19 genotype-based dosing recommendations.

Genotyping and phenotyping of CYP2D6 and CYP3A isoenzymes in patients with alcohol use disorder: correlation with haloperidol plasma concentration

BACKGROUND

Haloperidol is used for the treatment of alcohol use disorders in patients with signs of alcohol-related psychosis. Haloperidol therapy poses a high risk of adverse drug reactions (ADR). Contradictory data, which include the effects of genetic polymorphisms in genes encoding the elements of haloperidol biotransformation system on haloperidol metabolism rate and plasma drug concentration ratio, are described in patients with different genotypes. The primary objective of this study was to investigate the effects of CYP2D6 and CYP3A5 genetic polymorphisms on haloperidol equilibrium concentration in patients with alcohol use disorder.

METHODS

The study included 69 male patients with alcohol use disorder. Genotyping was performed using the allele-specific real-time PCR. CYP2D6 and CYP3A were phenotyped with HPLC-MS using the concentration of endogenous substrate of the enzyme and its urinary metabolites [6-hydroxy-1,2,3,4-tetrahydro-β-carboline(6-HO-THBC) to pinoline ratio for CYP2D6 and 6-β-hydroxycortisol to cortisol ratio for CYP3A]. The equilibrium plasma concentration was determined using LC-MS-MS.

RESULTS

Results indicated that both C/D indexes and equilibrium concentration levels depend on CYP2D6 genetic polymorphism, but only in patients receiving haloperidol intramuscular injections [0.26 (0.09; 0.48) vs. 0.54 (0.44; 0.74), p=0.037].

CONCLUSIONS

The study demonstrates that CYP2D6 genetic polymorphism (1846G>A) can affect haloperidol concentration levels in patients with alcohol use disorder.

The correlation between CYP2D6 isoenzyme activity and haloperidol efficacy and safety profile in patients with alcohol addiction during the exacerbation of the addiction

Background

Today, it is proved that isoenzymes CYP2D6 and CYP3A4 are involved in metabolism of haloperidol. In our previous investigation, we found a medium correlation between the efficacy and safety of haloperidol and the activity of CYP3A4 in patients with alcohol abuse.

Objective

The aim of this study was to evaluate the correlation between the activity of CYP2D6 and the efficacy and safety of haloperidol in patients with diagnosed alcohol abuse.

Methods

The study involved 70 men (average age: 40.83±9.92 years) with alcohol addiction. A series of psychometric scales were used in the research. The activity of CYP2D6 was evaluated by high-performance liquid chromatography with mass spectrometry using the ratio of 6-hydroxy-1,2,3,4-tetrahydro-beta-carboline to pinoline. Genotyping of CYP2D6 (1846G>A) was performed using real-time polymerase chain reaction.

Results

According to results of correlation analysis, statistically significant values of Spearman correlation coefficient (r_s) between the activity of CYP2D6 and the difference of points in psychometric scale were obtained in patients receiving haloperidol in injection form (Sheehan Clinical Anxiety Rating Scale =−0.721 [P<0.001] and Udvald for Kliniske Undersogelser Side Effect Rating Scale =0.692 [P<0.001]) and in those receiving haloperidol in tablet form (Covi Anxiety Scale =−0.851 [P<0.001] and Udvald for Kliniske Undersogelser Side Effect Rating Scale =0.797 [P<0.001]).

Conclusion

This study demonstrated the correlations between the activity of CYP2D6 isozyme and the efficacy and safety of haloperidol in patients with alcohol addiction.

Genetics of Common Antipsychotic-Induced Adverse Effects

The effectiveness of antipsychotic drugs is limited due to accompanying adverse effects which can pose considerable health risks and lead to patient noncompliance. Pharmacogenetics (PGx) offers a means to identify genetic biomarkers that can predict individual susceptibility to antipsychotic-induced adverse effects (AAEs), thereby improving clinical outcomes. We reviewed the literature on the PGx of common AAEs from 2010 to 2015, placing emphasis on findings that have been independently replicated and which have additionally been listed to be of interest by PGx expert panels. Gene-drug associations meeting these criteria primarily pertain to metabolic dysregulation, extrapyramidal symptoms (EPS), and tardive dyskinesia (TD). Regarding metabolic dysregulation, results have reaffirmed HTR2C as a strong candidate with potential clinical utility, while MC4R and OGFR1 gene loci have emerged as new and promising biomarkers for the prediction of weight gain. As for EPS and TD, additional evidence has accumulated in support of an association with CYP2D6 metabolizer status. Furthermore, HSPG2 and DPP6 have been identified as candidate genes with the potential to predict differential susceptibility to TD. Overall, considerable progress has been made within the field of psychiatric PGx, with inroads toward the development of clinical tools that can mitigate AAEs. Going forward, studies placing a greater emphasis on multilocus effects will need to be conducted.

Possible biomarkers modulating haloperidol efficacy and/or tolerability

Haloperidol (HP) is widely used in the treatment of several forms of psychosis. Despite of its efficacy, HP use is a cause of concern for the elevated risk of adverse drug reactions. adverse drug reactions risk and HP efficacy greatly vary across subjects, indicating the involvement of several factors in HP mechanism of action. The use of biomarkers that could monitor or even predict HP treatment impact would be of extreme importance. We reviewed the elements that could potentially be used as peripheral biomarkers of HP effectiveness. Although a validated biomarker still does not exist, we underlined the several potential findings (e.g., about cytokines, HP metabolites and genotypic biomarkers) which could pave the way for future research on HP biomarkers.

Applicability of gene expression and systems biology to develop pharmacogenetic predictors; antipsychotic-induced extrapyramidal symptoms as an example

Pharmacogenetics has been driven by a candidate gene approach. The disadvantage of this approach is that is limited by our current understanding of the mechanisms by which drugs act. Gene expression could help to elucidate the molecular signatures of antipsychotic treatments searching for dysregulated molecular pathways and the relationships between gene products, especially protein-protein interactions. To embrace the complexity of drug response, machine learning methods could help to identify gene-gene interactions and develop pharmacogenetic predictors of drug response. The present review summarizes the applicability of the topics presented here (gene expression, network analysis and gene-gene interactions) in pharmacogenetics. In order to achieve this, we present an example of identifying genetic predictors of extrapyramidal symptoms induced by antipsychotic.