Yingying Guo

Efavirenz primary and secondary metabolism in vitro and in vivo: identification of novel metabolic pathways and cytochrome P450 2A6 as the principal catalyst of efavirenz 7-hydroxylation.

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.

Effects of the CYP2B6*6 Allele on Catalytic Properties and Inhibition of CYP2B6 In Vitro: Implication for the Mechanism of Reduced Efavirenz Metabolism and Other CYP2B6 Substrates In Vivo

The mechanism by which CYP2B6*6 allele alters drug metabolism in vitro and in vivo is not fully understood. To test the hypothesis that altered substrate binding and/or catalytic properties contribute to its functional consequences, efavirenz 8-hydroxylation and bupropion 4-hydroxylation were determined in CYP2B6.1 and CYP2B6.6 proteins expressed without and with cytochrome b5 (Cyt b5) and in human liver microsomes (HLMs) obtained from liver tissues genotyped for the CYP2B6*6 allele. The susceptibility of the variant protein to inhibition was also tested in HLMs. Significantly higher Vmax and Km values for 8-hydroxyefavirenz formation and ∼2-fold lower intrinsic clearance (Clint) were noted in expressed CYP2B6.6 protein (−b5) compared with that of CYP2B6.1 protein (−b5); this effect was abolished by Cyt b5. The Vmax and Clint values for 4-hydroxybupropion formation were significantly higher in CYP2B6.6 than in CYP2B6.1 protein, with no difference in Km, whereas coexpression with Cyt b5 reversed the genetic effect on these kinetic parameters. In HLMs, CYP2B6*6/*6 genotype was associated with markedly lower Vmax (and moderate increase in Km) and thus lower Clint values for efavirenz and bupropion metabolism, but no difference in catalytic properties was noted between CYP2B6*1/*1 and CYP2B6*1/*6 genotypes. Inhibition of efavirenz 8-hydroxylation by voriconazole was significantly greater in HLMs with the CYP2B6*6 allele (Ki = 1.6 ± 0.8 μM) than HLMs with CYP2B6*1/*1 genotype (Ki = 3.0 ± 1.1 μM). In conclusion, our data suggest the CYP2B6*6 allele influences metabolic activity by altering substrate binding and catalytic activity in a substrate- and Cyt b5-dependent manner. It may also confer susceptibility to inhibition.

Identification of the Effect of Multiple Polymorphisms on the Pharmacokinetics of Simvastatin and Simvastatin Acid Using a Population‐Modeling 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.

Ethnic Variability in the Expression of Hepatic Drug Transporters: Absolute Quantification by an Optimized Targeted Quantitative Proteomic Approach

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.

Pharmacogenomics of Gemcitabine Metabolism: Functional Analysis of Genetic Variants in Cytidine Deaminase and Deoxycytidine Kinase

Gemcitabine (dFdC, 2′,2′-difluorodeoxycytidine) is metabolized by cytidine deaminase (CDA) and deoxycytidine kinase (DCK), but the contribution of genetic variation in these enzymes to the variability in systemic exposure and response observed in cancer patients is unclear. Wild-type enzymes and variants of CDA (Lys27Gln and Ala70Thr) and DCK (Ile24Val, Ala119Gly, and Pro122Ser) were expressed in and purified from Escherichia coli, and enzyme kinetic parameters were estimated for cytarabine (Ara-C), dFdC, and its metabolite 2′,2′-difluorodeoxyuridine (dFdU) as substrates. All three CDA proteins showed similar Km and Vmax for Ara-C and dFdC deamination, except for CDA70Thr, which had a 2.5-fold lower Km and 6-fold lower Vmax for Ara-C deamination. All four DCK proteins yielded comparable metabolic activity for Ara-C and dFdC monophosphorylation, except for DCK24Val, which demonstrated an approximately 2-fold increase (P < 0.05) in the intrinsic clearance of dFdC monophosphorylation due to a 40% decrease in Km (P < 0.05). DCK did not significantly contribute to dFdU monophosphorylation. In conclusion, the Lys27Gln substitution does not significantly modulate CDA activity toward dFdC, and therefore would not contribute to interindividual variability in response to gemcitabine. The higher in vitro catalytic efficiency of DCK24Val toward dFdC monophosphorylation may be relevant to dFdC clinical response. The substrate-dependent alterations in activities of CDA70Thr and DCK24Val in vitro were observed for the first time, and demonstrate that the in vivo consequences of these genetic variations should not be extrapolated from one substrate of these enzymes to another.

CYP2B6 Pharmacogenetics–Based In Vitro–In Vivo Extrapolation of Efavirenz Clearance by Physiologically Based Pharmacokinetic Modeling

Efavirenz is mainly cleared by CYP2B6. The CYP2B6*6 allele is associated with lower efavirenz clearance. Efavirenz clearance was predictable using in vitro data for carriers of the CYP2B6*1/*1 genotype, but the prediction in carriers of the CYP2B6*6 allele was poor. To test the hypothesis that incorporation of mechanism of reduced efavirenz metabolism by the CYP2B6*6 allele can predict the genetic effect on efavirenz pharmacokinetics, in vitro–in vivo extrapolation of efavirenz clearance was performed by physiologically based pharmacokinetic modeling (Simcyp Simulator; Simcyp Ltd., Sheffield, UK) using data obtained from expressed CYP2B6.1 and CYP2B6.6 as well as human liver microsomes (HLMs) with CYP2B6*1/*1, *1/*6, and *6/*6 genotypes. Simulated pharmacokinetics of a single 600-mg oral dose of efavirenz for individuals with each genotype was compared with data observed in healthy subjects genotyped for the CYP2B6*6 allele (n = 20). Efavirenz clearance for carriers of the CYP2B6*1/*1 genotype was predicted reasonably well using HLM data, but the clearance in carriers of the CYP2B6*6 allele was underpredicted using both expressed and HLM systems. Improved prediction of efavirenz clearance was obtained from expressed CYP2B6 after recalculating intersystem extrapolation factors for CYP2B6.1 and CYP2B6.6 based on in vitro intrinsic clearance of bupropion 4-hydroxylation. These findings suggest that genetic effect on both CYP2B6 protein expression and catalytic efficiency needs to be taken into account for the prediction of pharmacokinetics in individuals carrying the CYP2B6*6/*6 genotype. Expressed CYP2B6 proteins may be a reliable in vitro system to predict effect of the CYP2B6*6 allele on the metabolism of CYP2B6 substrates.

Literature mining on pharmacokinetics numerical data: A feasibility study

A feasibility study of literature mining is conducted on drug PK parameter numerical data with a sequential mining strategy. Firstly, an entity template library is built to retrieve pharmacokinetics relevant articles. Then a set of tagging and extraction rules are applied to retrieve PK data from the article abstracts. To estimate the PK parameter population-average mean and between-study variance, a linear mixed meta-analysis model and an E-M algorithm are developed to describe the probability distributions of PK parameters. Finally, a cross-validation procedure is developed to ascertain false-positive mining results. Using this approach to mine midazolam (MDZ) PK data, an 88% precision rate and 92% recall rate are achieved, with an F-score=90%. It greatly out-performs a conventional data mining approach (support vector machine), which has an F-score of 68.1%. Further investigate on 7 more drugs reveals comparable performances of our sequential mining approach.

Prediction of In Vivo Clearance and Associated Variability of CYP2C19 Substrates by Genotypes in Populations Utilizing a Pharmacogenetics-Based Mechanistic Model

It is important to examine the cytochrome P450 2C19 (CYP2C19) genetic contribution to drug disposition and responses of CYP2C19 substrates during drug development. Design of such clinical trials requires projection of genotype-dependent in vivo clearance and associated variabilities of the investigational drug, which is not generally available during early stages of drug development, but is essential for CYP2C19 substrates with multiple clearance pathways. This study evaluated the utility of pharmacogenetics-based mechanistic modeling in predicting such parameters. Hepatic CYP2C19 activity and variability within genotypes were derived from in vitro S-mephenytoin metabolic activity in genotyped human liver microsomes (N = 128). These data were then used in mechanistic models to predict genotype-dependent disposition of CYP2C19 substrates (i.e., S-mephenytoin, citalopram, pantoprazole, and voriconazole) by incorporating in vivo clearance or pharmacokinetics of wild-type subjects and parameters of other clearance pathways. Relative to the wild-type, the CYP2C19 abundance (coefficient of variation percentage) in CYP2C19*17/*17, *1/*17, *1/*1, *17/null, *1/null, and null/null microsomes was estimated as 1.85 (117%), 1.79 (155%), 1.00 (138%), 0.83 (80%), 0.38 (130%), and 0 (0%), respectively. The subsequent modeling and simulations predicted, within 2-fold of the observed, the means and variabilities of urinary S/R-mephenytoin ratio (36 of 37 genetic groups), the oral clearance of citalopram (9 of 9 genetic groups) and pantoprazole (6 of 6 genetic groups), and voriconazole oral clearance (4 of 4 genetic groups). Thus, relative CYP2C19 genotype-dependent hepatic activity and variability were quantified in vitro and used in a mechanistic model to predict pharmacokinetic variability, thus allowing the design of pharmacogenetics and drug-drug interaction trials for CYP2C19 substrates.

Modelling of atorvastatin pharmacokinetics and the identification of the effect of a BCRP polymorphism in the Japanese population

Aim Ethnicity plays a modulating role in atorvastatin pharmacokinetics (PK), with Asian patients reported to have higher exposure compared with Caucasians. Therefore, it is difficult to safely extrapolate atorvastatin PK data and models across ethnic groups. This work aims to develop a population PK model for atorvastatin and its pharmacologically active metabolites specifically for the Japanese population. Subsequently, it aimed to identify genetic polymorphisms affecting atorvastatin PK in this population. Methods Atorvastatin acid (ATA) and ortho-hydroxy-atorvastatin acid (o-OH-ATA) plasma concentrations, clinical/demographic characteristics and genotypes for 18 (3, 3, 1, 1, 7, 2 and 1 in the ABCB1, ABCG2, CYP3A4, CYP3A5, SLCO1B1, SLCO2B1 and PPARA genes, respectively) genetic polymorphisms were collected from 27 Japanese individuals (taking 10 mg atorvastatin once daily) and analysed using a population PK modelling approach. Results The population PK model developed (one-compartment for ATA linked through metabolite formation to an additional compartment describing the disposition of o-OH-ATA) accurately described the observed data and the associated population variability. Our analysis suggested that patients carrying one variant allele for the rs2622604 polymorphism (ABCG2) show a 55% (95% confidence interval: 16–131%) increase in atorvastatin oral bioavailability relative to the value in individuals without the variant allele. Conclusion The current work reports the identification in the Japanese population of a BCRP polymorphism, not previously associated with the PK of any statin, that markedly increases ATA and o-OH-ATA exposure. The model developed may be of clinical importance to guide dosing recommendations tailored specifically for the Japanese.

Advancing Predictions of Tissue and Intracellular Drug Concentrations Using In Vitro, Imaging and Physiologically Based Pharmacokinetic Modeling Approaches

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.