Direct and metabolism‐dependent cytochrome P450 inhibition assays for evaluating drug–drug interactions

We developed methods for evaluating the ntial inhibition of human cytochrome P450 (CYP) enzymes, including CYP1A2, CYP2A6, CYP2B6, CYP2 C9, CYP2 C19, CYP2D6, CYP2E1 and CYP3A4, using pooled human liver microsomes (HLMs). The CYP inhibition assay used substrate cocktail sets [set A: phenacetin for CYP1A2, coumarin for CYP2A6, (S)‐(+)‐mephenytoin for CYP2C19, dextromethorphan for CYP2D6 and midazolam for CYP3A4; set B: bupropion for CYP2B6, tolbutamide for CYP2C9, chlorzoxazone for CYP2E1, and testosterone for CYP3A4] with quantitation by liquid chromatography–tandem mass spectrometry. A direct inhibition assay was performed with the substrate cocktails without β‐nicotinamide adenine dinucleotide phosphate (NADPH) pre‐incubation, and a metabolism‐dependent inhibition (MDI) assay was performed after 30 min of pre‐incubation with NADPH in HLMs. MDI was identified based on the half‐maximal inhibitory concentration (IC₅₀) shifts. The IC₅₀ values of the direct inhibitors determined using the probe substrate cocktails were in good agreement with previously reported values. Eight metabolism‐dependent inhibitors including furafylline, 8‐methoxypsoralen, tienilic acid, ticlopidine, fluoxetine, paroxetine, disulfiram and verapamil against CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP2E1 and CYP3A4, respectively, resulted in significant IC₅₀ shifts (≥2.5‐fold) after pre‐incubation. Thus, these CYP inhibition assays are considered to be useful tools for evaluating both direct inhibition and MDI at an early stage of the drug discovery and development process. Copyright © 2011 John Wiley & Sons, Ltd.     

Prediction of drug‐drug interactions using physiologically‐based pharmacokinetic models of CYP450 modulators included in Simcyp software

In recent years, physiologically based PharmacoKinetic (PBPK) modeling has received growing interest as a useful tool for the assessment of drug pharmacokinetics. It has been demonstrated to be informative and helpful to quantify the modification in drug exposure due to specific physio‐pathological conditions, age, genetic polymorphisms, ethnicity and particularly drug–drug interactions (DDIs). In this paper, the prediction success of DDIs involving various cytochrome P450 isoenzyme (CYP) modulators namely ketoconazole (a competitive inhibitor of CYP3A), itraconazole (a competitive inhibitor of CYP3A), clarithromycin (a mechanism‐based inhibitor of CYP3A), quinidine (a competitive inhibitor of CYP2D6), paroxetine (a mechanism‐based inhibitor of CYP2D6), ciprofloxacin (a competitive inhibitor of CYP1A2), fluconazole (a competitive inhibitor of CYP2C9/2C19) and rifampicin (an inducer of CYP3A) were assessed using Simcyp^® software. The aim of this report was to establish confidence in each CYP‐specific modulator file so they can be used in the future for the prediction of DDIs involving new victim compounds. Our evaluation of these PBPK models suggested that they can be successfully used to evaluate DDIs in untested scenarios. The only noticeable exception concerned a quinidine inhibitor model that requires further improvement. Additionally, other important aspects such as model validation criteria were discussed.     

Laparoscopic Roux‐en‐Y gastric bypass surgery influenced pharmacokinetics of several drugs given as a cocktail with the highest impact observed for CYP1A2, CYP2C8 and CYP2E1 substrates

There is a lack of information about the changes in drug pharmacokinetics and cytochrome P450 (CYP) metabolism after bariatric surgery. Here, we investigated the effects of laparoscopic Roux‐en‐Y gastric bypass (LRYGB) surgery on pharmacokinetics of nine drugs given simultaneously which may reveal changes in the activities of the main CYPs. Eight obese subjects undergoing LRYGB received an oral cocktail containing nine drugs, substrates of various CYPs: melatonin (CYP1A2), nicotine (CYP2A6), bupropion (CYP2B6), repaglinide (CYP2C8), losartan (CYP2C9), omeprazole (CYP2C19/CYP3A4), dextromethorphan (CYP2D6), chlorzoxazone (CYP2E1) and midazolam (CYP3A). The 6‐hours pharmacokinetic profiles in serum and urine of each drug or corresponding metabolite as well as their metabolic ratios were compared before surgery with those at a median 1 year later. LRYGB exerted variable effects on the pharmacokinetics of these drugs. The geometric mean AUC_0‐6 (90% confidence interval) of melatonin, bupropion, repaglinide, chlorzoxazone and midazolam after LRYGB was 27 (19%‐41%), 54 (43%‐67%), 44 (29%‐66%), 160 (129%‐197%) and 74 (62%‐90%) of the pre‐surgery values, respectively. The pharmacokinetics of losartan, omeprazole and dextromethorphan did not change in response to surgery. Nicotine was not detected in serum, while geometric mean of AUC_0‐6 of its metabolite, cotinine, increased by 1.7 times after surgery. There were 3.6‐ and 1.3‐fold increases in the AUC ratios of 6‐hydroxymelatonin/melatonin and hydroxybupropion/bupropion, respectively. The cocktail revealed multiple pharmacokinetic changes occurring after LRYGB with the greatest effects observed for CYP1A2, CYP2C8 and CYP2E1 substrates. Future studies should be focused on CYP1A2, CYP2A6, CYP2C8 and CYP2B6 to clarify the changes in activities of these enzymes after LRYGB.     

A physiologically based pharmacokinetic modeling approach to predict drug–drug interactions between domperidone and inhibitors of CYP3A4

Domperidone is a dopamine receptor antagonist and a substrate of CYP3A4, hence there is a potential for CYP3A inhibition‐based drug–drug interactions (DDI). A physiologically based pharmacokinetic model was developed to describe DDIs between domperidone and three different inhibitors of CYP3A4. Simcyp V13.1 was used to simulate human domperidone pharmacokinetics and DDIs. Inputs included domperidone chemical and physical properties (LogP, pKa, etc.), in vitro human liver microsomal data and pharmacokinetic parameters from single‐dose intravenous clinical studies in healthy participants. The simulated mean maximum domperidone plasma concentration and AUC after single‐ and multiple‐oral doses under diverse conditions were within 1.1–1.4 fold of the observed values. The simulated intestinal availability, hepatic availability and the fraction absorbed were 0.45 ± 0.14, 0.31 ± 0.10 and 0.89 ± 0.11, respectively, and comparable to observed in vivo values. The simulated ratios of AUC and C_max in the presence of ketoconazole, erythromycin or itraconazole to baseline were consistent with the observed ratios. Simulated ketoconazole, erythromycin, itraconazole and C_max,ss and AUC_ss were within 1.5‐fold of the observed values. Copyright © 2016 John Wiley & Sons, Ltd.     

Inhibitory Effects of Silibinin on Cytochrome P‐450 Enzymes in Human Liver Microsomes

Silibinin, the main constituent of silymarin, a flavonoid drug from silybum marianum used in liver disease, was tested for inhibition of human cytochrome P‐450 enzymes. Metabolic activities were determined in liver microsomes from two donors using selective substrates. With each substrate, incubations were carried out with and without silibinin (concentrations 3.7–300 μM) at 37° in 0.1 M KH₂PO₄ buffer containing up to 3% DMSO. Metabolite concentrations were determined by HPLC or direct spectroscopy. First, silibinin IC₅₀ values were determined for each substrate at respective K_M concentrations. Silibinin had little effect (IC₅₀>200 μM) on the metabolism of erythromycin (CYP3A4), chlorzoxazone (CYP2E1), S(+)‐mephenytoin (CYP2C19), caffeine (CYP1A2) or coumarin (CYP2A6). A moderate effect was observed for high affinity dextromethorphan metabolism (CYP2D6) in one of the microsomes samples tested only (IC₅₀=173 μM). Clear inhibition was found for denitronifedipine oxidation (CYP3A4; IC₅₀=29 μM and 46 μM) and S(?)‐warfarin 7‐hydroxylation (CYP2C9; IC₅₀=43 μM and 45 μM). When additional substrate concentrations were tested to assess enzyme kinetics, silibinin was a potent competitive inhibitor of dextromethorphan metabolism at the low affinity site, which is not CYP2D6 (K_i,c=2.3 μM and 2.4 μM). Inhibition was competitive for S(?)‐warfarin 7‐hydroxylation (K_i,c=18 μM and 19 μM) and mainly non‐competitive for denitronifedipine oxidation (K_i,n=9 μM and 12 μM). With therapeutic silibinin peak plasma concentrations of 0.6 μM and biliary concentrations up to 200 μM, metabolic interactions with xenobiotics metabolised by CYP3A4 or CYP2C9 cannot be excluded.     

Pharmacokinetics of tolbutamide and its metabolite 4‐hydroxy tolbutamide in poloxamer 407‐induced hyperlipidemic rats

Under hyperlipidemic conditions, there are likely to be alterations in the pharmacokinetics of CYP2C11 substrates following decreased expression of CYP2C11, which is homologous to human CYP2C9. The pharmacokinetics of tolbutamide (TB) and its metabolite 4‐hydroxy tolbutamide (4‐OHTB) were evaluated as a CYP2C11 probe after intravenous and oral administration of 10 mg/kg tolbutamide to poloxamer 407‐induced hyperlipidemic rats (HL rats). Changes in the expression and metabolic activity of hepatic CYP2C11 and the plasma protein binding of tolbutamide in HL rats were also evaluated. The total area under the plasma concentration–time curve (AUC) of tolbutamide in HL rats after intravenous administration was comparable to that in controls due to their comparable non‐renal clearance (CL_NR). The free fractions of tolbutamide in plasma were comparable between the control and HL rats. The 4‐hydroxylated metabolite formation ratio (AUC_4‐OHTB/AUC_TB) in HL rats was significantly smaller than that in the control rats as a result of the reduced expression of hepatic CYP2C11 (by 15.0%) and decreased hepatic CL_int (by 28.8%) for metabolism of tolbutamide to 4‐OHTB via CYP2C11. Similar pharmacokinetic changes were observed in HL rats after oral administration of tolbutamide. These findings have potential therapeutic implications, assuming that the HL rat model qualitatively reflects similar changes in patients with hyperlipidemia. Since other sulfonylureas in clinical use are substrates of CYP2C9, their hepatic CL_int changes have the potential to cause clinically relevant pharmacokinetic changes in a hyperlipidemic state. Copyright © 2014 John Wiley & Sons, Ltd.     

Physiologically based pharmacokinetic modeling to predict complex drug–drug interactions: a case study of AZD2327 and its metabolite,competitive and time‐dependent CYP3A inhibitors

4‐{(R)‐(3‐Aminophenyl)[4‐(4‐fluorobenzyl)‐piperazin‐1‐yl]methyl}‐N,N‐diethylbenzamide (AZD2327) is a highly potent and selective agonist of the δ ‐opioid receptor. AZD2327 and N‐deethylated AZD2327 (M1) are substrates of cytochrome P450 3A (CYP3A4) and comprise a complex multiple inhibitory system that causes competitive and time‐dependent inhibition of CYP3A4. The aim of the current work was to develop a physiologically based pharmacokinetic (PBPK) model to predict quantitatively the magnitude of CYP3A4 mediated drug–drug interaction with midazolam as the substrate. Integrating in silico, in vitro and in vivo PK data, a PBPK model was successfully developed to simulate the clinical accumulation of AZD2327 and its primary metabolite. The inhibition of CYP3A4 by AZD2327, using midazolam as a probe drug, was reasonably predicted. The predicted maximum concentration (C_max) and area under the concentration–time curve (AUC) for midazolam were increased by 1.75 and 2.45‐fold, respectively, after multiple dosing of AZD2327, indicating no or low risk for clinically relevant drug–drug interactions (DDI). These results are in agreement with those obtained in a clinical trial with a 1.4 and 1.5‐fold increase in C_max and AUC of midazolam, respectively. In conclusion, this model simulated DDI with less than a two‐fold error, indicating that complex clinical DDI associated with multiple mechanisms, pathways and inhibitors (parent and metabolite) can be predicted using a well‐developed PBPK model. Copyright © 2015 John Wiley & Sons, Ltd.     

Inhibition of cytochrome P450 by ethambutol in human liver microsomes

Although cytochrome P450 inhibition is the major drug–drug interaction (DDI) mechanism in clinical pharmacotherapy, DDI of a number of well-established drugs have not been investigated. Rifampicin, isoniazid, pyrazinamide and ethambutol combination therapy inhibits clearance of theophylline in patients with tuberculosis. We determined the inhibitory effects of ethambutol on the activities of nine CYP isoforms including CYP1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1 and 3A4 in pooled human liver microsomes (HLM). As measured by liquid chromatography–electrospray ionization tandem mass spectrometry, ethambutol exhibited strong inhibitory potential against CYP1A2 and CYP2E1, moderate against CYP2C19 and CYP2D6 and weak against CYP2A6, CYP2C9 and CYP3A4, based on the IC₅₀ values. The K_i value of ethambutol for CYP1A2 was 1.4 μM and for CYP2E1 was 2.9 μM. Inhibition of CYP1A2 and CYP2E1 was not increased by preincubation with ethambutol and β-nicotinamideadenine dinucleotide phosphate (NADPH), suggesting that the ethambutol-induced CYP inhibition may not be metabolism-dependent. Kinetic analysis showed that the inhibition of CYP1A2 and CYP2E1 by ethambutol was best fit to a competitive inhibition model. Formation of 1-methylxanthene and 1,3-dimethyluric acid from theophylline in HLM was decreased to 47% and 36%, respectively, by 3.0 μM ethambutol, which is comparable to its IC₅₀ value against CYP1A2. Considering its maximal plasma concentrations of ∼10 μM and long half-life of ∼22 h, our findings raise the possibility that ethambutol causes significant DDIs in clinical situations with drugs with narrow therapeutic index, such as theophylline, in clinical situations.     

Evaluation of the effects of 18 non-synonymous single-nucleotide polymorphisms of CYP450 2C19 on in vitro drug inhibition potential by a fluorescence-based high-throughput assay

1. To comprehensively understand the effects of CYP2C19 genetic polymorphisms on inhibition-based drug–drug interactions (DDIs), 18 human CYP2C19 non-synonymous single-nucleotide polymorphic variants and the wild-type isoform (CYP2C19.1A) were expressed in yeast cells. Using a fluorescence-based high-throughput method, the kinetic constants of these variants, as well as the inhibition constants for 10 drugs, were determined.

2. CYP2C19.5B and CYP2C19.6 showed no activity towards CEC (3-cyano-7-ethoxycoumarin) O-deethylation. CYP2C19.8, CYP2C19.9, CYP2C19.10, CYP2C19.16, CYP2C19.19, E122A and A161P* (an allele containing both A161P and I331V) exhibited significantly reduced catalytic activities compared with CYP2C19.1A. The inhibition assay showed that the CYP2C19 genotype significantly affected the in vitro drug inhibition potential. Although the effect on drug inhibition potential is genotype- and inhibitor-dependent, there was an obvious trend: drugs tended to exhibit higher IC₅₀ values (i.e. decreased inhibition potential) towards variants with reduced activity compared with variants with normal activity. This indicated that patients with reduced-function alleles may be less susceptible to CYP2C19-related DDIs.

3. In this study, we provided the first in vitro evidence of CYP2C19 genotype-dependent effects on drug inhibition potential. This work greatly extends our understanding of the functional consequences of CYP2C19 genetic polymorphisms, and thus should prove valuable for CYP2C19 genotype-based therapy.

     

In vitro–in vivo extrapolations to evaluate the effect of concomitant drugs on tacrolimus (FK506) exposure

The effect of concomitant drugs having a cytochrome P450 (CYP) 3A inhibitory potency on tacrolimus exposure was predicted from in vitro metabolism results. In this study, the IC₅₀ values of concomitant drugs on the formation of M‐I, the major metabolite of tacrolimus, were determined, and the effect on oral exposure (AUC_p.o.) of tacrolimus was assessed from static models. When an absorbed fraction (F_a) of 0.97, intestinal wall availability (F_g) of 0.27 and fraction metabolized by CYP3A (f_m(CYP3A)) of 0.8 were used, the least bias was observed for the prediction of the AUC_p.o. of tacrolimus. The relationship of the IC₅₀ values of 11 inhibitors between tacrolimus and typical CYP3A substrates (midazolam and testosterone) was also analysed. A strong correlation was found between the IC₅₀ values of tacrolimus and typical CYP3A substrates (r² ≥ 0.85). The predictability of the effect of inhibitors on tacrolimus AUC_p.o. was investigated based on the same static models with the use of published IC₅₀ values for midazolam and testosterone. The bias for the prediction of tacrolimus AUC_p.o. was minimal with the use of IC₅₀ values determined using tacrolimus itself as a substrate. These results suggest that tacrolimus itself is still the best choice for predicting the AUC_p.o. of tacrolimus, although our findings suggest that midazolam or testosterone may be used instead of tacrolimus to estimate roughly (predicted AUC_p.o. within an approximately 2‐fold range of observed values) the effect of CYP3A inhibitors on the tacrolimus AUC_p.o.. Copyright © 2015 John Wiley & Sons, Ltd.     

Metabolism and related human risk factors for hepatic damage by usnic acid containing nutritional supplements

Usnic acid is a component of nutritional supplements promoted for weight loss that have been associated with liver-related adverse events including mild hepatic toxicity, chemical hepatitis, and liver failure requiring transplant. To determine if metabolism factors might have had a role in defining individual susceptibility to hepatotoxicity, in vitro metabolism studies were undertaken using human plasma, hepatocytes, and liver subcellular fractions. Usnic acid was metabolized to form three monohydroxylated metabolites and two regio-isomeric glucuronide conjugates of the parent drug. Oxidative metabolism was mainly by cytochrome P450 (CYP) 1A2 and glucuronidation was carried out by uridine diphosphate-glucuronosyltransferase (UGT) 1A1 and UGT1A3. In human hepatocytes, usnic acid at 20 µM was not an inducer of CYP1A2, CYP2B6, or CYP3A4 relative to positive controls omeprazole, phenobarbital, and rifampicin, respectively. Usnic acid was a relatively weak inhibitor of CYP2D6 and a potent inhibitor of CYP2C19 (the concentration eliciting 50% inhibition (IC₅₀)?=?9 nM) and CYP2C9 (IC₅₀?=?94 nM), with less potent inhibition of CYP2C8 (IC₅₀?=?1.9 µM) and CYP2C18 (IC₅₀?=?6.3 µM). Pre-incubation of microsomes with usnic acid did not afford any evidence of time-dependent inhibition of CYP2C19, although evidence of slight time-dependent inhibition of CYP2C9 (K_I?=?2.79 µM and K_inact?=?0.022 min^?1) was obtained. In vitro data were used with SimCYP^Rto model potential drug interactions. Based on usnic acid doses in case reports of 450 mg to >1 g day^?1, these in vitro data indicate that usnic acid has significant potential to interact with other medications. Individual characteristics such as CYP1A induction status, co-administration of CYP1A2 inhibitors, UGT1A1 polymorphisms, and related hyperbilirubinaemias, or co-administration of low therapeutic index CYP2C substrates could work alone or in consort with other idiosyncrasy risk factors to increase the risk of adverse events and/or hepatotoxicity. Thus, usnic acid in nutritional supplements might be involved as both victim and/or perpetrator in clinically significant drug–drug interactions.     

Functional characterization of five CYP2C8 variants and prediction of CYP2C8 genotype-dependent effects on in vitro and in vivo drug–drug interactions

1. To analyze the polymorphic activities of CYP2C8 and evaluate their impact on drug inhibitory potential, three CYP2C8 allelic variants (CYP2C8.2, CYP2C8.3, and CYP2C8.4), two non-synonymous single nucleotide polymorphic variants (R139K and K399R, carried by CYP2C8.3), and wild-type CYP2C8 (CYP2C8.1) were heterologously expressed in yeast, and their enzymatic activities were characterized. CYP2C8 inhibition-based in vitro and in vivo drug–drug interactions (DDIs) in wild-type and variant CYP2C8s were then predicted.

2. Functional characterization of five CYP2C8 variants revealed similar enzymatic activity in R139K and low activity in CYP2C8.2, CYP2C8.3, CYP2C8.4, and K399R compared with CYP2C8.1. The systematic analysis of these CYP2C8 variants can provide more homogeneous data for predicting CYP2C8 phenotypes and could be applied to personalized drug therapy.

3. Prediction of DDIs indicated that CYP2C8.4, R139K, and K399R dramatically alter the IC₅₀ values of nifedipine, troglitazone, and raloxifene, and R139K qualitatively and quantitatively reduces the risk of in vivo paclitaxel–raloxifene and paclitaxel–troglitazone interactions. The results provide the first evidence that CYP2C8 inhibition-based DDIs may be influenced by CYP2C8 genetic polymorphisms. These inhibition data can be used by pharmacologists in the design of in vivo studies to further assess and address the potential role of CYP2C8 genotype-dependent inhibition in clinical DDIs.

     

The Role of CYP2C8 and CYP2C9 Genotypes in Losartan‐Dependent Inhibition of Paclitaxel Metabolism in Human Liver Microsomes

The aim of the present study was to further investigate a previously identified metabolic interaction between losartan and paclitaxel, which is one of the marker substrates of CYP2C8, by using human liver microsomes (HLMs) from donors with different CYP2C8 and CYP2C9 genotypes. Although CYP2C8 and CYP2C9 exhibit genetic linkage, previous studies have yet to determine whether losartan or its active metabolite, EXP‐3174 which is specifically generated by CYP2C9, is responsible for CYP2C8 inhibition. Concentrations of 6α‐hydroxypaclitaxel and EXP‐3174 were measured by high‐performance liquid chromatography after incubations with paclitaxel, losartan or EXP‐3174 in HLMs from seven donors with different CYP2C8 and CYP2C9 genotypes. The half maximal inhibitory concentration (IC₅₀) values were not fully dependent on CYP2C8 genotypes. Although the degree of inhibition was small, losartan significantly inhibited the production of 6α‐hydroxypaclitaxel at a concentration of 1 μmol/L in only HL20 with the CYP2C8*3/*3 genotype. HLMs with either CYP2C9*2/*2 or CYP2C9*1/*3 exhibited a lower losartan intrinsic clearance (V_max/K_m) than other HLMs including those with CYP2C9*1/*1 and CYP2C9*1/*2. Significant inhibition of 6α‐hydroxypaclitaxel formation by EXP‐3174 could only be found at levels that were 50 times higher (100 μmol/L) than the maximum concentration generated in the inhibition study using losartan. These results suggest that the metabolic interaction between losartan and paclitaxel is dependent on losartan itself rather than its metabolite and that the CYP2C8 inhibition by losartan is not affected by the CYP2C9 genotype. Further study is needed to define the effect of CYP2C8 genotypes on losartan–paclitaxel interaction.     

Assessment of the in vitro cytochrome P450 (CYP) inhibition potential of ZYTP1, a novel poly (ADP-ribose) polymerase inhibitor

Abstract

1. ZYTP1 is a novel Poly (ADP-ribose) polymerase protein inhibitor being developed for cancer indications.

2. The focus of the work was to determine if ZYTP1 had a perpetrator role in the in vitro inhibition of cytochrome P450 (CYP) enzymes to aid dosing decisions during the clinical development of ZYTP1.

3. ZYTP1 IC₅₀ for CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6 and 3A4/5 was determined using human liver microsomes and LC-MS/MS detection. CYP3A4/5 IC₅₀ of depropylated metabolite of ZYTP1 was also determined. Time dependent inhibition of CYP3A4/5 by ZYTP1 was also assessed using substrates, testosterone and midazolam.

4. The mean IC₅₀ values of ZYTP1 were >100 µM for CYP1A2, 2B6 and 2D6, while 56.1, 24.5, 39.5 and 23.3–58.7 µM for CYP2C8, 2C9, 2C19 and 3A4/5, respectively. The CYP3A4/5 IC₅₀ of depropylated metabolite was 11.95–24.51 µM. Time dependent CYP3A4/5 inhibition was noted for testosterone and midazolam with IC₅₀ shift of 10.9- and 39.9-fold, respectively. With midazolam, the kinact and KI values of ZYTP1 were 0.075?min^?1 and 4.47 µM for the CYP3A4/5 time dependent inhibition, respectively.

5. Because of potent inhibition of CYP3A4/5, drugs that undergo metabolism via CYP3A4/5 pathway should be avoided during ZYTP1 therapy.

     

Enantiospecific effects of chiral drugs on cytochrome P450 inhibition in vitro

1.?The aim of this work was to examine the differences in the inhibitory potency of individual enantiomers and racemic mixtures of selected chiral drugs on human liver microsomal cytochromes P450.

2.?The interaction of enantiomeric forms of six drugs (tamsulosin, tolterodine, citalopram, modafinil, zopiclone, ketoconazole) with nine cytochromes P450 (CYP3A4, CYP2E1, CYP2D6, CYP2C19, CYP2C9, CYP2C8, CYP2B6, CYP2A6, CYP1A2) was examined. HPLC methods were used to estimate the extent of the inhibition of specific activity in vitro.

3.?Tamsulosin (TAM) and tolterodine (TOL) inhibited CYP3A4 activity with an enantiospecific pattern. The inhibition of CYP3A4 activity differed for R-TAM (K_i 2.88?±?0.12?µM) and S-TAM (K_i 14.22?±?0.53?µM) as well as for S-TOL (K_i 1.71?±?0.03?µM) and R-TOL (K_i 4.78?±?0.17?µM). Also, the inhibition of CYP2C19 by ketoconazole (KET) cis-enantiomers exhibited enantioselective behavior: the (+)-KET (IC₅₀ 23.64?±?6.25?µM) was more potent than (?)-KET (IC₅₀ 66.12?±?12.6?µM). The inhibition of CYP2C19 by modafinil (MOD) enantiomers (R-MOD IC₅₀?=?51.79?±?8.58?µM, S-MOD IC₅₀?=?48.62?±?9.74?µM) and the inhibition of CYP2D6 by citalopram (CIT) enantiomers (R-CIT IC₅₀?=?68.17?±?5.70?µM, S-CIT IC₅₀?=?62.63?±?7.89?µM) was not enantiospecific.

4.?Although enantiospecific interactions were found (TAM, TOL, KET), they are probably not clinically relevant as the plasma levels are generally lower than the drug concentration needed for prominent inhibition (at least 50% of CYP activity).     

The Role of Human CYP2C8 and CYP2C9 Variants in Pioglitazone Metabolism In Vitro

Abstract: The cytochrome P450 enzyme CYP2C8 appears to have a major role in pioglitazone metabolism. The present study was conducted to further clarify the role of individual CYPs and of the CYP2C8/9 polymorphisms in the primary metabolism of pioglitazone in vitro. Pioglitazone (2–400 μM) was incubated with isolated cytochrome P450 enzymes or human liver microsomes, some of them carrying either the CYP2C8*3/*3 genotype (and also the CYP2C9*2/*2 genotype) or the CYP2C8*1/*1 genotype (five samples each). The formation of the primary pioglitazone metabolite M‐IV was monitored by HPLC. Enzyme kinetics were estimated assuming a single binding site. Mean intrinsic clearance of pioglitazone to the metabolite M‐IV was highest for CYP2C8 and CYP1A2 with 58 pmol M‐IV/min/nmol CYP P450/μM pioglitazone each, 53 for CYP2D6*1, 40 for CYP2C19*1, and 34 for CYP2C9*2, respectively. CYP2A6, CYP2B6, CYP2C9*1, CYP2C9*3, CYP2E1, CYP3A4 and CYP3A5 did not form quantifiable amounts of M‐IV. CYP2C8*1/*1 microsomes (25 ± 4 pmol M‐IV/min/mg protein/μM pioglitazone) showed lower intrinsic clearance of pioglitazone than CYP2C8*3/*3 microsomes (35 ± 9, p = 0.04). In all samples, metabolite formation showed substrate inhibition, while pioglitazone did not inhibit CYP2C8‐mediated paclitaxel metabolism. CYP2C8, CYP1A2 and CYP2D6 are major CYPs forming M‐IV in vitro. The higher activity of CYP2C8*3/CYP2C9*2 microsomes may result from a contribution of CYP2C9*2, or from differences in CYP2C8 expression. The evidence for substrate‐specific inhibitory effects of pioglitazone on CYP2C‐mediated metabolism needs to be tested in further studies.     

Identification of cytochromes<Emphasis Type="Italic"> P</Emphasis><Subscript>450</Subscript> 2C9 and 3A4 as the major catalysts of phenprocoumon hydroxylation in vitro

Objective This in-vitro study aimed at an identification of cytochrome P₄₅₀ (CYP) enzymes catalysing the (S)- and (R)-hydroxylation of the widely used anticoagulant phenprocoumon (PPC) to its major, inactive metabolites.Methods Relevant catalysts were identified by kinetic, correlation and inhibition experiments using human liver microsomes and recombinant enzymes.Results Kinetics revealed (S)-7-hydroxylation as quantitatively most important. Biphasic Eadie-Hofstee plots indicated more than one catalyst for the 4-, 6- and 7-hydroxylation of both enantiomers with mean K_m1 and K_m2 of 144.5±34.9 and 10.0±6.49 µM, respectively. PPC hydroxylation rates were significantly correlated with CYP2C9 and CYP3A4 activity and expression analysing 11 different CYP-specific probes. Complete inhibition of PPC hydroxylation was achieved by combined addition of the CYP3A4-specific inhibitor triacetyloleandomycin (TAO) and a monoclonal, inhibitory antibody (mAb) directed against CYP2C8, 9, 18 and 19, except for the (R)-4-hydroxylation that was, however, inhibited by ~80% using TAO alone. (S)-PPC hydroxylation was reduced by ~2/3 and ~1/3 using mAb2C8–9-18–19 and TAO, respectively, but (R)-6- and 7-hydroxylation by ~50% each. Experiments with mAbs directed against single CYP2C enzymes clearly indicated CYP2C9 as a major catalyst of the 6- and 7-hydroxylation for both enantiomers. However, CYP2C8 was equally important regarding the (S)-4-hydroxylation. Recombinant CYP2C8 and CYP2C9 were high-affinity catalysts (K_m <5 µM), whereas CYP3A4 operated with low affinity (K_m >100 µM).Conclusion CYP2C9 and CYP3A4 are major catalysts of (S)- and (R)-PPC hydroxylation, while CYP2C8 partly catalysed the (S)-4-hydroxylation. Increased vigilance is warranted when PPC treatment is combined with substrates, inhibitors, or inducers of these enzymes.Part of this work was presented at the 6th Congress of the European Association for Clinical Pharmacology and Therapeutics, Istanbul, June 2003.     

Assessment of interaction potential of AZD2066 using in vitro metabolism tools, physiologically based pharmacokinetic modelling and in vivo cocktail data

Purpose

Static and dynamic (PBPK) prediction models were applied to estimate the drug–drug interaction (DDI) risk of AZD2066. The predictions were compared to the results of an in vivo cocktail study. Various in vivo measures for tolbutamide as a probe agent for cytochrome P450 2C9 (CYP2C9) were also compared.

Methods

In vitro inhibition data for AZD2066 were obtained using human liver microsomes and CYP-specific probe substrates. DDI prediction was performed using PBPK modelling with the SimCYP simulator? or static model. The cocktail study was an open label, baseline, controlled interaction study with 15 healthy volunteers receiving multiple doses of AD2066 for 12 days. A cocktail of single doses of 100 mg caffeine (CYP1A2 probe), 500 mg tolbutamide (CYP2C9 probe), 20 mg omeprazole (CYP2C19 probe) and 7.5 mg midazolam (CYP3A probe) was simultaneously applied at baseline and during the administration of AZD2066. Bupropion as a CYP2B6 probe (150 mg) and 100 mg metoprolol (CYP2D6 probe) were administered on separate days. The pharmacokinetic parameters for the probe drugs and their metabolites in plasma and urinary recovery were determined.

Results

In vitro AZD2066 inhibited CYP1A2, CYP2B6, CYP2C9, CYP2C19 and CYP2D6. The static model predicted in vivo interaction with predicted AUC ratio values of >1.1 for all CYP (except CYP3A4). The PBPK simulations predicted no risk for clinical relevant interactions. The cocktail study showed no interaction for the CYP2B6 and CYP2C19 enzymes, a possible weak inhibition of CYP1A2, CYP2C9 and CYP3A4 activities and a slight inhibition (29 %) of CYP2D6 activity. The tolbutamide phenotyping metrics indicated that there were significant correlations between CL_form and AUC_TOL, CL, Ae_met and LnTOL_24h. The MR_Ae in urine showed no correlation to CL_form.

Conclusions

DDI prediction using the static approach based on total concentration indicated that AZD20066 has a potential risk for inhibition. However, no DDI risk could be predicted when a more in vivo-like dynamic prediction method with the PBPK with SimCYP? software based on early human PK data was used and more parameters (i.e. free fraction in plasma, no DDI risk) were taken into account. The clinical cocktail study showed no or low risks for clinical relevant DDI interactions. Our findings are in line with the hypothesis that the dynamic prediction method predicts DDI in vivo in humans better than the static model based on total plasma concentrations.     

In vitro and in vivo assessment of the effect of dalcetrapib on a panel of CYP substrates

ABSTRACT

Objective: The primary objective of this study was to investigate the drug–drug interaction potential of dalcetrapib on drugs metabolized via major cytochrome P450 (CYP) isoforms using both in vitro and clinical approaches. A secondary objective was to investigate the safety and tolerability of dalcetrapib alone or co-administered either with a combination of five probe drugs or with rosiglitazone.

Research design and methods: Human liver microsomes and a panel of substrates for CYP enzymes were used to determine IC₅₀ for inhibition of CYP1A2, CYP2C8, CYP2C9, CYP2C19, CYP2D6, and CYP3A4. In addition, two drug–drug interaction studies were conducted in healthy males: dalcetrapib 900?mg plus the Cooperstown 5?+?1 drug cocktail, which includes substrates for CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4, and dalcetrapib 900?mg plus rosiglitazone, a substrate for CYP2C8. Pharmacokinetic and safety parameters were assessed.

Results: In vitro, dalcetrapib was inhibitory to all CYP enzymes tested. IC₅₀ values ranged from 1.5?±?0.1?μM for CYP2C8 to 82?±?4?μM for CYP2D6. Co-administration of dalcetrapib plus drug cocktail showed no clinically relevant effect of 900?mg dalcetrapib on activity of CYP1A2, CYP2C19, CYP2D6, CYP2C9, or CYP3A4 following repeated administration. Co-administration of dalcetrapib plus rosiglitazone showed no clinically relevant effect of dalcetrapib 900?mg on activity of CYP2C8. Dalcetrapib was generally well tolerated.

Conclusions: Although in vitro studies indicated that dalcetrapib inhibits CYP activity, two clinical studies showed no clinically relevant effect on any of the major CYP isoforms at a 900?mg dose, which is higher than the 600?mg dose being explored in phase III studies. Dalcetrapib was generally well tolerated in these studies. However, these studies were limited to a small number of healthy males; additional, larger studies are necessary to study its safety.     

Assessment of drug–drug interaction potential and PBPK modeling of CC-223, a potent inhibitor of the mammalian target of rapamycin kinase

1.?CC-223 was studied in vitro for metabolism and drug–drug interactions (DDI), and in clinic for interaction with ketoconazole.

2.?In vitro, human metabolites of CC-223 included O-desmethyl CC-223 (M1), keto (M2), N-oxide (M3) and imine (M13), with M1 being the most prominent metabolite.

3.?CC-223 was metabolized by CYP2C9 and CYP3A, while metabolism of M1 was mediated by CYP2C8 and CYP3A. Ketoconazole increased CC-223 and M1 exposure by 60–70% in healthy volunteers.

4.?CC-223 (IC₅₀?≥?27?µM) and M1 (IC₅₀?≥?46?µM) were inhibitors of CYP2C9 and CYP2C19 in human liver microsomes. CC-223 and M1 were moderate inducers of CYP3A in human hepatocytes.

5.?CC-223 was a substrate of BCRP, and M1 was a substrate of P-gp and BCRP. CC-223 was an inhibitor of P-gp (IC₅₀?=?3.67?µM) and BCRP (IC₅₀?=?11.7?µM), but at a clinically relevant concentration showed no inhibition of other transporters examined. M1 is a weak inhibitor of P-gp and BCRP.

6.?PBPK model of CC-223 and M1 was developed and verified using clinical results. Model based predictions of DDI with ketoconazole were in agreement with observed results enabling prospective predictions of DDIs between CC-223 and CYP3A4 inhibitors.