Cytochrome P450 3A4 (CYP3A4) metabolizes ~50% of all clinically used drugs. Although CYP3A4 expression varies widely between individuals, the contribution of genetic factors remains uncertain. In this study, we measured allelic CYP3A4 heteronuclear RNA (hnRNA) and mRNA expression in 76 human liver samples heterozygous for at least one of eight marker SNPs and found marked allelic expression imbalance (1.6–6.3-fold) in 10/76 liver samples (13%). This was fully accounted for by an intron 6 SNP (rs35599367, C>T), which also affected mRNA expression in cell culture on minigene transfections. CYP3A4 mRNA level and enzyme activity in livers with CC genotype were 1.7- and 2.5-fold, respectively, greater than in CT and TT carriers. In 235 patients taking stable doses of atorvastatin, simvastatin, or lovastatin for lipid control, carriers of the T allele required significantly lower statin doses (0.2–0.6-fold, P=0.019) than non-T carriers for optimal lipid control. These results indicate that intron 6 SNP rs35599367 markedly affects expression of CYP3A4 and could serve as a biomarker for predicting response to CYP3A4-metabolized drugs.

To understand Wuhan residents’ psychological reactions to the COVID-19 epidemic and offer a reference point for interventions, an online questionnaire survey was conducted. It included the Disorder 7-Item Scale (GAD-7), the Patient Health Questionnaire 9-Item Scale (PHQ-9), Athens Insomnia Scale, and Simplified Coping Style Questionnaire. Categorical data were reported as numbers and percentages. Multivariate logistic regression models were used to evaluate the association between demographic factors and anxiety, depression, sleep disorder, and passive coping style. A total of 1242 Wuhan residents investigated, 27.5% had anxiety, 29.3% had depression, 30.0% had a sleep disorder, and 29.8% had a passive response to COVID-19. Being female was the risk factor for anxiety (OR = 1.62) and sleep disorder (OR = 1.36); being married was associated with anxiety (OR = 1.75); having a monthly income between 1000 and 5000 CNY (OR = 1.44, OR = 1.83, OR = 2.61) or >5000 CNY (OR = 1.47, OR = 1.45, OR = 2.14) was a risk factor for anxiety, depression, and sleep disorder; not exercising (OR = 1.45, OR = 1.71, OR = 1. 85, OR = 1.71) was a common risk factor for anxiety, depression, sleep disorder, and passive coping style; and having a higher education level (bachelor’s degree and above) (OR = 1.40) was associated with having a sleep disorder. Wuhan residents’ psychological status and sleep quality were relatively poorer than they were before the COVID-19 epidemic; however, the rate of passive coping to stress was relatively higher.

ABSTRACT:Efavirenz primary and secondary metabolism was investigated in vitro and in vivo. In human liver microsome (HLM) samples, 7-and 8-hydroxyefavirenz accounted for 22.5 and 77.5% of the overall efavirenz metabolism, respectively. Kinetic, inhibition, and correlation analyses in HLM samples and experiments in expressed cytochrome P450 show that CYP2A6 is the principal catalyst of efavirenz 7-hydroxylation. Although CYP2B6 was the main enzyme catalyzing efavirenz 8-hydroxylation, CYP2A6 also seems to contribute. Both 7-and 8-hydroxyefavirenz were further oxidized to novel dihydroxylated metabolite(s) primarily by CYP2B6. These dihydroxylated metabolite(s) were not the same as 8,14-dihydroxyefavirenz, a metabolite that has been suggested to be directly formed via 14-hydroxylation of 8-hydroxyefavirenz, because 8,14-dihydroxyefavirenz was not detected in vitro when efavirenz, 7-, or 8-hydroxyefavirenz were used as substrates. Efavirenz and its primary and secondary metabolites that were identified in vitro were quantified in plasma samples obtained from subjects taking a single 600-mg oral dose of efavirenz. 8,14-Dihydroxyefavirenz was detected and quantified in these plasma samples, suggesting that the glucuronide or the sulfate of 8-hydroxyefavirenz might undergo 14-hydroxylation in vivo. In conclusion, efavirenz metabolism is complex, involving unique and novel secondary metabolism. Although efavirenz 8-hydroxylation by CYP2B6 remains the major clearance mechanism of efavirenz, CYP2A6-mediated 7-hydroxylation (and to some extent 8-hydroxylation) may also contribute. Efavirenz may be a valuable dual phenotyping tool to study CYP2B6 and CYP2A6, and this should be further tested in vivo.

This white paper examines recent progress, applications, and challenges in predicting unbound and total tissue and intra/subcellular drug concentrations using in vitro and preclinical models, imaging techniques, and physiologically based pharmacokinetic (PBPK) modeling. Published examples, regulatory submissions, and case studies illustrate the application of different types of data in drug development to support modeling and decision making for compounds with transporter-mediated disposition, and likely disconnects between tissue and systemic drug exposure. The goals of this article are to illustrate current best practices and outline practical strategies for selecting appropriate in vitro and in vivo experimental methods to estimate or predict tissue and plasma concentrations, and to use these data in the application of PBPK modeling for human pharmacokinetic (PK), efficacy, and safety assessment in drug development.

Genetic variants of the beta2-AR gene seem to explain some part of the differences between various strains of mice to develop OIH. The association of this gene with OIH suggests specific pharmacologic strategies for reducing the impact of OIH on patients consuming opioids.

Hepatic OATPs 1B1, 1B3 and 2B1, as well as P-gp, play important roles in regulating liver uptake and biliary excretion of drugs. The intrinsic ethnic variability in OATP1B1-mediated hepatic uptake of statins has been proposed to underlie the ethnic variability in plasma exposures of statins between Caucasians and Asians. Using a targeted quantitative proteomic approach, we determined hepatic protein concentrations of OATP1B1, OATP1B3, OATP2B1, P-gp, and PMCA4 (a housekeeping protein) in a panel of human livers (n = 141) and compared protein expression across Caucasian, Asian, African-American, and unidentified donors. Using an optimized protocol that included sodium deoxycholate as a membrane protein solubilizer, the hepatic protein expression levels (mean ± S.D.) of these transporters across all livers were determined to be 15.0 ± 6.0, 16.1 ± 8.1, 4.1 ± 1.3, 0.6 ± 0.2, and 2.4 ± 1.0 fmol/μg of total membrane protein, respectively. The scaling factor was 3.5 mg of total membrane protein in 100 mg of wet liver tissue. OATP1B1 protein expression was significantly associated with the c.388A>G (rs2306283, N130D) single nucleotide polymorphism. When compared across ethnicity, the hepatic expression levels of OATP1B1 and OATP1B3 were unexpectedly higher in Asians relative to Caucasians, suggesting that hepatic OATP expression alone does not explain the increased systemic statin levels in Asians compared with Caucasians. These findings may help improve physiologically based pharmacokinetic modeling to predict statin pharmacokinetic profiles and enable extrapolation of pharmacokinetic data of OATP substrates across ethnic groups.

A mechanistically sound SV/SVA population model with clinical applications (e.g., assessment of drug-drug interaction and myopathy risk) was developed, illustrating the advantages of an integrated population PBPK approach.

The aim of this work was to develop a joint population pharmacokinetic model for simvastatin (SV) and its active metabolite, simvastatin acid (SVA), that incorporates the effects of multiple genetic polymorphisms and clinical/demographic characteristics. SV/SVA plasma concentrations, demographic/clinical data, and genotypes for 18 genetic variants were collected from 74 individuals (three clinical trials) and analyzed using a nonlinear mixed-effects modeling approach. The structural model that best described the data included a two- and a one-compartment disposition model for SV and SVA, respectively. Age, weight, Japanese ethnicity, and seven genetic polymorphisms-rs4149056 (SLCO1B1), rs776746 (CYP3A5), rs12422149 (SLCO2B1), rs2231142 (ABCG2), rs4148162 (ABCG2), rs4253728 (PPARA), and rs35599367 (CYP3A4)-were identified as significantly affecting model parameters. The developed model was used to assess combinations of these covariates, highlighting specific risk factors associated with altered SV/SVA pharmacokinetics, and consequently myopathy cases that cannot be solely attributed to the rs4149056 CC genotype.