Concurrent assessment of hepatic and intestinal cytochrome P450 3A activities using deuterated alfentanil

A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug-Drug Interaction Perpetrators

The antifungal ketoconazole, which is mainly used for dermal infections and treatment of Cushing's syndrome, is prone to drug-food interactions (DFIs) and is well known for its strong drug-drug interaction (DDI) potential. Some of ketoconazole's potent inhibitory activity can be attributed to its metabolites that predominantly accumulate in the liver. This work aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model of ketoconazole and its metabolites for fasted and fed states and to investigate the impact of ketoconazole's metabolites on its DDI potential. The parent-metabolites model was developed with PK-Sim^® and MoBi^® using 53 plasma concentration-time profiles. With 7 out of 7 (7/7) DFI AUC_last and DFI C_max ratios within two-fold of observed ratios, the developed model demonstrated good predictive performance under fasted and fed conditions. DDI scenarios that included either the parent alone or with its metabolites were simulated and evaluated for the victim drugs alfentanil, alprazolam, midazolam, triazolam, and digoxin. DDI scenarios that included all metabolites as reversible inhibitors of CYP3A4 and P-gp performed best: 26/27 of DDI AUC_last and 21/21 DDI C_max ratios were within two-fold of observed ratios, while DDI models that simulated only ketoconazole as the perpetrator underperformed: 12/27 DDI AUC_last and 18/21 DDI C_max ratios were within the success limits.

Physiologically-based pharmacokinetic modeling to predict CYP3A4-mediated drug-drug interactions of finerenone

Finerenone is a nonsteroidal, selective mineralocorticoid receptor antagonist that recently demonstrated its efficacy to delay chronic kidney disease (CKD) progression and reduce cardiovascular events in patients with CKD and type 2 diabetes. Here, we report the development of a physiologically‐based pharmacokinetic (PBPK) model for finerenone and its application as a victim drug of cytochrome P450 3A4 (CYP3A4)‐mediated drug‐drug interactions (DDIs) using the open‐source PBPK platform PK‐Sim, which has recently been qualified for this application purpose. First, the PBPK model for finerenone was developed using physicochemical, in vitro, and clinical (including mass balance) data. Subsequently, the finerenone model was validated regarding the contribution of CYP3A4 metabolism to total clearance by comparing to observed data from dedicated clinical interaction studies with erythromycin (simulated geometric mean ratios of the area under the plasma concentration‐time curve [AUCR] of 3.46 and geometric mean peak plasma concentration ratios [C_maxRs] of 2.00 vs. observed of 3.48 and 1.88, respectively) and verapamil (simulated AUCR of 2.91 and C_maxR of 1.86 vs. observed of 2.70 and 2.22, respectively). Finally, the finerenone model was applied to predict clinically untested DDI studies with various CYP3A4 modulators. An AUCR of 6.31 and a C_maxR of 2.37 was predicted with itraconazole, of 5.28 and 2.25 with clarithromycin, 1.59 and 1.40 with cimetidine, 1.57 and 1.38 with fluvoxamine, 0.19 and 0.32 with efavirenz, and 0.07 and 0.14 with rifampicin. This PBPK analysis provides a quantitative basis to guide the label and clinical use of finerenone with concomitant CYP3A4 modulators.

Perspectives from the Innovation and Quality Consortium Induction Working Group on Factors Impacting Clinical Drug-Drug Interactions Resulting from Induction: Focus on Cytochrome 3A Substrates

A recent publication from the Innovation and Quality Consortium Induction Working Group collated a large clinical data set with the goal of evaluating the accuracy of drug-drug interaction (DDI) prediction from in vitro data. Somewhat surprisingly, comparison across studies of the mean- or median-reported area under the curve ratio showed appreciable variability in the magnitude of outcome. This commentary explores the possible drivers of this range of outcomes observed in clinical induction studies. While recommendations on clinical study design are not being proposed, some key observations were informative during the aggregate analysis of clinical data. Although DDI data are often presented using median data, individual data would enable evaluation of how differences in study design, baseline expression, and the number of subjects contribute. Since variability in perpetrator pharmacokinetics (PK) could impact the overall DDI interpretation, should this be routinely captured? Maximal induction was typically observed after 5-7 days of dosing. Thus, when the half-life of the inducer is less than 30 hours, are there benefits to a more standardized study design? A large proportion of CYP3A4 inducers were also CYP3A4 inhibitors and/or inactivators based on in vitro data. In these cases, using CYP3A selective substrates has limitations. More intensive monitoring of changes in area under the curve over time is warranted. With selective CYP3A substrates, the net effect was often inhibition, whereas less selective substrates could discern induction through mechanisms not susceptible to inhibition. The latter included oral contraceptives, which raise concerns of reduced efficacy following induction. Alternative approaches for modeling induction, such as applying biomarkers and physiologically based pharmacokinetic modeling (PBPK), are also considered. SIGNIFICANCE STATEMENT: The goal of this commentary is to stimulate discussion on whether there are opportunities to optimize clinical drug-drug interaction study design. The overall aim is to reduce, understand and contextualize the variability observed in the magnitude of induction across reported clinical studies. A large clinical CYP3A induction dataset was collected and further analyzed to identify trends and gaps. Reporting individual victim PK data, characterizing perpetrator PK and including additional PK assessments for mixed-mechanism perpetrators may provide insights into how these factors impact differences observed in clinical outcomes. The potential utility of biomarkers and PBPK modeling are discussed in considering future directions.

Simultaneously predict pharmacokinetic interaction of rifampicin with oral versus intravenous substrates of cytochrome P450 3A/P‑glycoprotein to healthy human using a semi-physiologically based pharmacokinetic model involving both enzyme and transporter turnover

Several reports demonstrated that rifampicin affected pharmacokinetics of victim drugs following oral more than intravenous administration. We aimed to establish a semi-physiologically based pharmacokinetic (semi-PBPK) model involving both enzyme and transporter turnover to simultaneously predict pharmacokinetic interaction of rifampicin with oral versus intravenous substrates of cytochrome P450 (CYP) 3A4/P‑glycoprotein (P-GP) in human. Rifampicin was chosen as the CYP3A /P-GP inducer. Thirteen victim drugs including P-GP substrates (digoxin and talinolol), CYP3A substrates (alfentanil, midazolam, nifedipine, ondansetron and oxycodone), dual substrates of CYP3A/P-GP (quinidine, cyclosporine A, tacrolimus and verapamil) and complex substrates (S-ketamine and tramadol) were chosen to investigate drug-drug interactions (DDIs) with rifampicin. Corresponding parameters were cited from literatures. Before and after multi-dose of oral rifampicin, the pharmacokinetic profiles of victim drugs for oral or intravenous administration to human were predicted using the semi-PBPK model and compared with the observed values. Contribution of both CYP3A and P-GP induction in intestine and liver by rifampicin to pharmacokinetic profiles of victim drugs was investigated. The predicted pharmacokinetic profiles of drugs before and after rifampicin administration accorded with the observations. The predicted pharmacokinetic parameters and DDIs were successful, whose fold-errors were within 2. It was consistent with observations that the DDIs of rifampicin with oral victim drugs were larger than those with intravenous victim drugs. DDIs of rifampicin with CYP3A or P-GP substrates following oral versus intravenous administration to human were successfully predicted using the developed semi-PBPK model.

Prediction of Drug-Drug Interactions Arising from CYP3A induction Using a Physiologically Based Dynamic Model

Using physiologically based pharmacokinetic modeling, we predicted the magnitude of drug-drug interactions (DDIs) for studies with rifampicin and seven CYP3A4 probe substrates administered i.v. (10 studies) or orally (19 studies). The results showed a tendency to underpredict the DDI magnitude when the victim drug was administered orally. Possible sources of inaccuracy were investigated systematically to determine the most appropriate model refinement. When the maximal fold induction (Ind_max) for rifampicin was increased (from 8 to 16) in both the liver and the gut, or when the Ind_max was increased in the gut but not in liver, there was a decrease in bias and increased precision compared with the base model (Ind_max = 8) [geometric mean fold error (GMFE) 2.12 vs. 1.48 and 1.77, respectively]. Induction parameters (mRNA and activity), determined for rifampicin, carbamazepine, phenytoin, and phenobarbital in hepatocytes from four donors, were then used to evaluate use of the refined rifampicin model for calibration. Calibration of mRNA and activity data for other inducers using the refined rifampicin model led to more accurate DDI predictions compared with the initial model (activity GMFE 1.49 vs. 1.68; mRNA GMFE 1.35 vs. 1.46), suggesting that robust in vivo reference values can be used to overcome interdonor and laboratory-to-laboratory variability. Use of uncalibrated data also performed well (GMFE 1.39 and 1.44 for activity and mRNA). As a result of experimental variability (i.e., in donors and protocols), it is prudent to fully characterize in vitro induction with prototypical inducers to give an understanding of how that particular system extrapolates to the in vivo situation when using an uncalibrated approach.

CYP3A activity: towards dose adaptation to the individual

INTRODUCTION

Co-medication, gene polymorphisms and co-morbidity are main causes for high variability in expression and function of the CYP3A isoenzymes. Pharmacokinetic variability is a major source of interindividual variability of drug effect and response of CYP3A substrates. While CYP3A genotyping is of limited use, direct testing of enzyme function ('phenotyping') may be more promising to achieve individualized dosing of CYP3A substrates.

AREAS COVERED

We will discuss available phenotyping strategies for CYP3A isoenzymes and causes of intra- and interindividual variability of CYP3A. The impact of phenotyping on the dose selection and pharmacokinetics of CYP3A substrates (docetaxel, irinotecan, tyrosine kinase inhibitors, ciclosporin, tacrolimus) are reviewed. Pubmed searches were conducted during March-November 2015 to retrieve articles related to CYP3A enzyme, phenotyping, drug interactions with CYP3A probe substrates, and phenotyping-guided dosing algorithms.

EXPERT OPINION

While ample data is available on the choice appropriate phenotyping drugs (midazolam, alfentanil, aplrazolam, buspirone, triazolam), less clinical trial data is available concerning strategies to usefully guide dosing in the clinical practice. Implementation into the clinical routine necessitates further research to identify (1) an easy-to-use and cheap test for CYP3A activity that (2) adequately predicts drug exposure to (3) allow a sound decision on dose adaptation and hence (4) improve clinical outcome and/or reduce the intensity or frequency of adverse drug effects.

Cytochrome P4503A does not mediate the interaction between methadone and ritonavir-lopinavir

Plasma concentrations of orally administered methadone are reduced by the human immunodeficiency virus protease inhibitor combination ritonavir and lopinavir, but the mechanism is unknown. Methadone metabolism, clearance, and drug interactions have been attributed to CYP3A4, but this remains controversial. This investigation assessed the effects of acute (2 days) and steady-state (2 weeks) ritonavir-lopinavir on intravenous and oral methadone metabolism and clearance, hepatic and intestinal CYP3A4/5 activity (using the probe substrate intravenous and oral alfentanil), and intestinal transporter activity (using oral fexofenadine) in healthy volunteers. Plasma and urine concentrations of methadone and metabolite enantiomers, and other analytes, were determined by mass spectrometry. Acute and chronic ritonavir-lopinavir reduced plasma methadone enantiomer concentrations in half, with an average 2.6- and 1.5-fold induction of systemic and apparent oral methadone clearances. Induction was attributable to stereoselectively increased hepatic methadone N-demethylation, hepatic extraction, and hepatic clearance, and there was a strong correlation between methadone N-demethylation and clearance. Methadone renal clearance was unchanged. Alfentanil's systemic clearance and hepatic extraction, apparent oral clearance, and intestinal extraction were reduced to 25%, 16%, and 35% of control, indicating strong inhibition of hepatic and intestinal CYP3A activities. Ritonavir-lopinavir (acute > chronic) increased fexofenadine exposure, suggesting intestinal P-glycoprotein inhibition. No correlation was found between methadone clearance and CYP3A activity. Acute and steady-state ritonavir-lopinavir stereoselectively induced methadone N-demethylation and clearance, despite significant inhibition of hepatic and intestinal CYP3A activity. Ritonavir-lopinavir inhibited intestinal transporters activity but had no effect on methadone bioavailability. These results do not support a significant role for CYP3A or ritonavir-lopinavir-inhibitable intestinal transporters in single-dose methadone disposition.

CYP2D6 genotype dependent oxycodone metabolism in postoperative patients

BACKGROUND

The impact of polymorphic cytochrome P450 CYP2D6 enzyme on oxycodone's metabolism and clinical efficacy is currently being discussed. However, there are only spare data from postoperative settings. The hypothesis of this study is that genotype dependent CYP2D6 activity influences plasma concentrations of oxycodone and its metabolites and impacts analgesic consumption.

METHODS

Patients received oxycodone 0.05 mg/kg before emerging from anesthesia and patient-controlled analgesia (PCA) for the subsequent 48 postoperative hours. Blood samples were drawn at 30, 90 and 180 minutes after the initial oxycodone dose. Plasma concentrations of oxycodone and its metabolites oxymorphone, noroxycodone and noroxymorphone were analyzed by liquid chromatography-mass spectrometry with electrospray ionization. CYP2D6 genotyping was performed and 121 patients were allocated to the following genotype groups: PM (poor metabolizer: no functionally active CYP2D6 allele), HZ/IM (heterozygous subjects, intermediate metabolizers with decreased CYP2D6 activity), EM (extensive metabolizers, normal CYP2D6 activity) and UM (ultrarapid metabolizers, increased CYP2D6 activity). Primary endpoint was the genotype dependent metabolite ratio of plasma concentrations oxymorphone/oxycodone. Secondary endpoint was the genotype dependent analgesic consumption with calculation of equianalgesic doses compared to the standard non-CYP dependent opioid piritramide.

RESULTS

Metabolism differed between CYP2D6 genotypes. Mean (95%-CI) oxymophone/oxycodone ratios were 0.10 (0.02/0.19), 0.13 (0.11/0.16), 0.18 (0.16/0.20) and 0.28 (0.07/0.49) in PM, HZ/IM, EM and UM, respectively (p = 0.005). Oxycodone consumption up to the 12(th) hour was highest in PM (p = 0.005), resulting in lowest equianalgesic doses of piritramide versus oxycodone for PM (1.6 (1.4/1.8); EM and UM 2.2 (2.1/2.3); p<0.001). Pain scores did not differ between genotypes.

CONCLUSIONS

In this postoperative setting, the number of functionally active CYP2D6 alleles had an impact on oxycodone metabolism. The genotype also impacted analgesic consumption, thereby causing variation of equianalgesic doses piritramide : oxycodone. Different analgesic needs by genotypes were met by PCA technology in this postoperative cohort.

Sensitivity of intravenous and oral alfentanil and pupillary miosis as minimal and noninvasive probes for hepatic and first-pass CYP3A induction

Systemic and oral clearances of alfentanil (ALF) are in vivo probes for hepatic and first-pass cytochrome P450 (CYP) 3A. Both ALF single-point plasma concentrations and miosis are surrogates for area under the concentration-time curve (AUC) and clearance and are minimal and noninvasive CYP3A probes. This investigation determined ALF sensitivity for detecting graded CYP3A induction and compared it with that of midazolam (MDZ). Twelve volunteers (sequential crossover) received 0, 5, 10, 25, or 75 mg oral rifampin for 5 days. MDZ and ALF were given intravenously and orally on sequential days. Dark-adapted pupil diameter was measured with blood sampling. Graded rifampin decreased plasma MDZ AUCs to 83, 76, 62, and 59% (intravenous (i.v.)) and 78, 66, 39, and 24% (oral) of control. Hepatic and first-pass CYP3A induction were detected comparably by plasma MDZ and ALF AUCs. Single ALF concentrations detected all CYP3A induction, whereas MDZ was less sensitive. ALF miosis detected induction of first-pass but not hepatic CYP3A.