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The cytochrome P450 2D6 (CYP2D6) genotype is the single most important determinant of CYP2D6 activity as well as interindividual and interpopulation variability in CYP2D6 activity. Here, the CYP2D6 activity score provides an established tool to categorize the large number of CYP2D6 alleles by activity and facilitates the process of genotype-to-phenotype translation. Compared to the broad traditional phenotype categories, the CYP2D6 activity score additionally serves as a superior scale of CYP2D6 activity due to its finer graduation. Physiologically based pharmacokinetic (PBPK) models have been successfully used to describe and predict the activity score-dependent metabolism of CYP2D6 substrates. This study aimed to describe CYP2D6 drug–gene interactions (DGIs) of important CYP2D6 substrates paroxetine, atomoxetine and risperidone by developing a substrate-independent approach to model their activity score-dependent metabolism. The models were developed in PK-Sim®, using a total of 57 plasma concentration–time profiles, and showed good performance, especially in DGI scenarios where 10/12, 5/5 and 7/7 of DGI AUClast ratios and 9/12, 5/5 and 7/7 of DGI Cmax ratios were within the prediction success limits. Finally, the models were used to predict their compound's exposure for different CYP2D6 activity scores during steady state. Here, predicted DGI AUCss ratios were 3.4, 13.6 and 2.0 (poor metabolizers; activity score = 0) and 0.2, 0.5 and 0.95 (ultrarapid metabolizers; activity score = 3) for paroxetine, atomoxetine and risperidone active moiety (risperidone + 9-hydroxyrisperidone), respectively. [ABSTRACT FROM AUTHOR]

The cytochrome P450 2D6 (CYP2D6) genotype is the single most important determinant of CYP2D6 activity as well as interindividual and interpopulation variability in CYP2D6 activity. Here, the CYP2D6 activity score provides an established tool to categorize the large number of CYP2D6 alleles by activity and facilitates the process of genotype-to-phenotype translation. Compared to the broad traditional phenotype categories, the CYP2D6 activity score additionally serves as a superior scale of CYP2D6 activity due to its finer graduation. Physiologically based pharmacokinetic (PBPK) models have been successfully used to describe and predict the activity score-dependent metabolism of CYP2D6 substrates. This study aimed to describe CYP2D6 drug–gene interactions (DGIs) of important CYP2D6 substrates paroxetine, atomoxetine and risperidone by developing a substrate-independent approach to model their activity score-dependent metabolism. The models were developed in PK-Sim®, using a total of 57 plasma concentration–time profiles, and showed good performance, especially in DGI scenarios where 10/12, 5/5 and 7/7 of DGI AUClast ratios and 9/12, 5/5 and 7/7 of DGI Cmax ratios were within the prediction success limits. Finally, the models were used to predict their compound’s exposure for different CYP2D6 activity scores during steady state. Here, predicted DGI AUCss ratios were 3.4, 13.6 and 2.0 (poor metabolizers; activity score = 0) and 0.2, 0.5 and 0.95 (ultrarapid metabolizers; activity score = 3) for paroxetine, atomoxetine and risperidone active moiety (risperidone + 9-hydroxyrisperidone), respectively.

The beta-blocker metoprolol (the sixth most commonly prescribed drug in the USA in 2017) is subject to considerable drug–gene interaction (DGI) effects caused by genetic variations of the CYP2D6 gene. CYP2D6 poor metabolizers (5.7% of US population) show approximately five-fold higher metoprolol exposure compared to CYP2D6 normal metabolizers. This study aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model to predict CYP2D6 DGIs with metoprolol. The metoprolol (R)- and (S)-enantiomers as well as the active metabolite α-hydroxymetoprolol were implemented as model compounds, employing data of 48 different clinical studies (dosing range 5–200 mg). To mechanistically describe the effect of CYP2D6 polymorphisms, two separate metabolic CYP2D6 pathways (α-hydroxylation and O-demethylation) were incorporated for both metoprolol enantiomers. The good model performance is demonstrated in predicted plasma concentration–time profiles compared to observed data, goodness-of-fit plots, and low geometric mean fold errors of the predicted AUClast (1.27) and Cmax values (1.23) over all studies. For DGI predictions, 18 out of 18 DGI AUClast ratios and 18 out of 18 DGI Cmax ratios were within two-fold of the observed ratios. The newly developed and carefully validated model was applied to calculate dose recommendations for CYP2D6 polymorphic patients and will be freely available in the Open Systems Pharmacology repository.

CYP2D6, a cytochrome P450 isoform, significantly contributes to the metabolism of many clinically important drugs. Thioridazine (THD) is one of the phenothiazine-type antipsychotics, which exhibit dopamine D 2 antagonistic activity. THD shows characteristic metabolic profiles compared to other phenothiazine-type antipsychotics such as chlorpromazine. The sulfur atom attached to the phenothiazine ring is preferentially oxidized mainly by CYP2D6, that is, the 2-sulfoxide is a major metabolite, and interestingly this metabolite shows more potent activity against dopamine D 2 receptors than THD. On the other hand, the formation of this metabolite causes many serious problems for its clinical use. Wójcikowski et al. ( Drug Metab . Dispos . 2006 , 34 , 471) reported a kinetic study of THD formed by CYP2D6. Recently, Wang et al. ( J . Biol . Chem . 2012 , 287 , 10834 and J . Biol . Chem . 2015 , 290 , 5092) revealed the crystallographic structure of THD with CYP2D6. In the current study, the binding and reaction mechanisms at the atomic and electronic levels were computationally examined based on the assumption as to whether or not the different crystallographic binding poses correspond to the different metabolites. The binding and oxidative reaction steps in the whole metabolic process were investigated using molecular dynamics and density functional theory calculations, respectively. The current study demonstrated the essential importance of the orientation of the substrate in the reaction center of CYP2D6 for the metabolic reaction. [ABSTRACT FROM AUTHOR]

Endocrine disrupting chemicals influence growth and development through interactions with the hormone system, often through binding to hormone receptors such as the estrogen receptor. Computational methods can predict endocrine disrupting chemical activity of unmodified compounds, but approaches predicting activity following metabolism are lacking. The present study uses a well-known environmental contaminant, PCB-30 (2,4,6-trichlorobiphenyl), as a prototype endocrine disrupting chemical and integrates predictive (computational) and experimental methods to determine its metabolic transformation by cytochrome P450 3A4 (CYP3A4) and cytochrome P450 2D6 (CYP2D6) into estrogenic byproducts. Computational predictions suggest that hydroxylation of PCB-30 occurs at the 3- or 4-phenol positions and leads to metabolites that bind more strongly than the parent molecule to the human estrogen receptor alpha (hER-α). Gas chromatography-mass spectrometry experiments confirmed that the primary metabolite for CYP3A4 and CYP2D6 is 4-hydroxy-PCB-30, and the secondary metabolite is 3-hydroxy-PCB-30. Cell-based bioassays (bioluminescent yeast expressing hER-α) confirmed that hydroxylated metabolites are more estrogenic than PCB-30. These experimental results support the applied model's ability to predict the metabolic and estrogenic fate of PCB-30, which could be used to identify other endocrine disrupting chemicals involved in similar pathways. Environ Toxicol Chem 2014;33:1615-1623. © 2014 SETAC [ABSTRACT FROM AUTHOR]

Abstract: Latest research in the mental health field brings new hope to patients and promises to revolutionize the field of psychiatry. Personalized pharmacogenetic tests that aid in diagnosis and treatment choice are now becoming available for clinical practice. Amyloid beta peptide biomarkers in the cerebrospinal fluid of patients with Alzheimer's disease are now available. For the first time, radiologists are able to visualize amyloid plaques specific to Alzheimer's disease in live patients using Positron Emission Tomography-based tests approved by the FDA. A novel blood-based assay has been developed to aid in the diagnosis of depression based on activation of the HPA axis, metabolic, inflammatory and neurochemical pathways. Serotonin reuptake inhibitors have shown increased remission rates in specific ethnic subgroups and Cytochrome P450 gene polymorphisms can predict antidepressant tolerability. The latest research will help to eradicate “trial and error” prescription, ushering in the most personalized medicine to date. Like all major medical breakthroughs, integration of new algorithms and technologies requires sound science and time. But for many mentally ill patients, diagnosis and effective therapy cannot happen fast enough. This review will describe the newest diagnostic tests, treatments and clinical studies for the diagnosis and treatment of Alzheimer's disease and unipolar, major depressive disorder. [Copyright &y& Elsevier]

Abstract: An approach for evaluating and integrating genomic data in chemical risk assessment was developed based on the lessons learned from performing a case study for the chemical dibutyl phthalate. A case study prototype approach was first developed in accordance with EPA guidance and recommendations of the scientific community. Dibutyl phthalate (DBP) was selected for the case study exercise. The scoping phase of the dibutyl phthalate case study was conducted by considering the available DBP genomic data, taken together with the entire data set, for whether they could inform various risk assessment aspects, such as toxicodynamics, toxicokinetics, and dose–response. A description of weighing the available dibutyl phthalate data set for utility in risk assessment provides an example for considering genomic data for future chemical assessments. As a result of conducting the scoping process, two questions—Do the DBP toxicogenomic data inform 1) the mechanisms or modes of action?, and 2) the interspecies differences in toxicodynamics?—were selected to focus the case study exercise. Principles of the general approach include considering the genomics data in conjunction with all other data to determine their ability to inform the various qualitative and/or quantitative aspects of risk assessment, and evaluating the relationship between the available genomic and toxicity outcome data with respect to study comparability and phenotypic anchoring. Based on experience from the DBP case study, recommendations and a general approach for integrating genomic data in chemical assessment were developed to advance the broader effort to utilize 21st century data in risk assessment. [Copyright &y& Elsevier]

Abstract: From a MeOH extract of Lunasia amara, which showed the cytochrome P450 2D6 (CYP2D6) inhibition, 14 acridone alkaloids including two new alkaloids [5-hydoroxygraveoline (1) and 8-methoxyifflaiamine (2)] were identified. Among the 14 acridone alkaloids, 5-hydoroxygraveoline (1) and lunamarine (3) showed moderate inhibition selective for CYP2D6, with IC50 values of 4.7μM and 1.8μM, respectively. [Copyright &y& Elsevier]

Even though 85% of adults drink caffeinated beverages daily, very limited studies on plasma caffeine concentration in the US population have been published. Smoking induces cytochrome P450 1A2 (CYP1A2), which is the main enzyme involved in caffeine metabolism. The current naturalistic pilot study explores plasma caffeine concentrations in a US sample, and presents a mathematical model of the relationship between caffeine intake and plasma concentrations for smokers and nonsmokers. Caffeine intake and average plasma caffeine concentrations from morning (7:30–9:30 a.m.) and afternoon (2:00–4:00 p.m.) samples were studied in 69 volunteers (21 smokers and 48 nonsmokers). The mean caffeine intake obtained from caffeinated beverages was 3.02 mg/kg/day, which is similar to the intake in the US population. Almost all subjects in the present sample (99%; 95% confidence interval [CI]: 96–100) had detectable plasma caffeine concentrations. Smokers had significantly higher caffeine intake than nonsmokers. The ratio of concentration/dose of caffeine from caffeinated beverages was approximately four-fold higher in nonsmokers (1.33 kg×day/l) than in smokers (0.29 kg×day/l). According to the model, the median plasma caffeine concentration was two- to three-fold higher in nonsmokers for each level of caffeine intake. Our model improves our understanding of the interactions between caffeine and smoking. Additional studies are needed to replicate the model. This model may help epidemiologists to correct for the effects of smoking on caffeine intake and pharmacologists to screen for the activity of CYP1A2. [Copyright &y& Elsevier]

Steady-state plasma concentrations of clozapine and norclozapine, its major metabolite, as well as their sum and ratio (norclozapine/clozapine), were evaluated in 50 in- and outpatients taking clozapine and naturalistically recruited. Drug plasma concentrations were measured by means of a reversed-phase high-performance liquid chromatography (RPLC) method with an ultraviolet detection. Daily doses (milligrams per kilogram of body weight) of clozapine correlated positively with clozapine plasma parameters, except with the norclozapine/clozapine ratio, in all patients. When the patients were divided in subgroups with respect to gender, the corresponding plasma concentrations were no longer dose-related in men. A lack of significant correlation was observed also in patients (n=23) co-treated with typical neuroleptics. Conversely, dose–concentration correlations were significant in either smoker or nonsmoker patients. No significant relationship between body weight and clozapine plasma parameters was reported. Further, we observed (1) a trend towards higher medians of clozapine or total analytes in women than those reported in men (P=.09 and .07); (2) no significant difference in plasma levels obtained in subjects younger than 34 years and subjects 34 years old or older; (3) a trend towards higher norclozapine and clozapine plus norclozapine levels (P=.05 and .08) in nonsmoker than smoker patients; (4) no significant difference between clozapine plasma parameters measured in patients co-medicated with typical neuroleptics and in patients receiving clozapine alone. [Copyright &y& Elsevier]

The beta-blocker metoprolol (the sixth most commonly prescribed drug in the USA in 2017) is subject to considerable drug–gene interaction (DGI) effects caused by genetic variations of the CYP2D6 gene. CYP2D6 poor metabolizers (5.7% of US population) show approximately five-fold higher metoprolol exposure compared to CYP2D6 normal metabolizers. This study aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model to predict CYP2D6 DGIs with metoprolol. The metoprolol (R)- and (S)-enantiomers as well as the active metabolite α-hydroxymetoprolol were implemented as model compounds, employing data of 48 different clinical studies (dosing range 5–200 mg). To mechanistically describe the effect of CYP2D6 polymorphisms, two separate metabolic CYP2D6 pathways (α-hydroxylation and O-demethylation) were incorporated for both metoprolol enantiomers. The good model performance is demonstrated in predicted plasma concentration–time profiles compared to observed data, goodness-of-fit plots, and low geometric mean fold errors of the predicted AUClast (1.27) and Cmax values (1.23) over all studies. For DGI predictions, 18 out of 18 DGI AUClast ratios and 18 out of 18 DGI Cmax ratios were within two-fold of the observed ratios. The newly developed and carefully validated model was applied to calculate dose recommendations for CYP2D6 polymorphic patients and will be freely available in the Open Systems Pharmacology repository. [ABSTRACT FROM AUTHOR]

Perhexiline is a unique anti-anginal agent that is frequently used in the treatment of chronic refractory angina. Its utility has been limited because of its complex pharmacokinetics that were only appreciated following the development of a therapeutic perhexiline assay. Perhexiline is cleared primarily via formation of mono-hydroxy metabolites (OH-perhexiline) by cytochrome P450 2D6 (CYP2D6). Drugs that are inhibitors of CYP2D6 may therefore inhibit perhexiline metabolism, increase plasma perhexiline concentration and may consequently increase the risk of toxicity. We report a case of a rise in perhexiline plasma concentration to a toxic level following the introduction of terbinafine hydrochloride; a moderate CYP2D6 inhibiting drug. [Copyright &y& Elsevier]