A semiphysiological population pharmacokinetic model for dynamic inhibition of liver and gut wall cytochrome P450 3A by voriconazole. Clin Pharmacokinet. 2013;52:763-781. [PMID: 23653047 DOI: 10.1007/s40262-013-0070-9]

Individual dose recommendations for drug interaction between tacrolimus and voriconazole in adult liver transplant recipients: A semiphysiologically based population pharmacokinetic modeling approach

The magnitude of drug-drug interaction between tacrolimus and voriconazole is highly variable, and individually tailoring the tacrolimus dose when concomitantly administered with voriconazole remains difficult. This study aimed to develop a semiphysiologically based population pharmacokinetic (semi-PBPK) model and a web-based dashboard to identify the dynamic inhibition of tacrolimus metabolism caused by voriconazole and provide individual tacrolimus regimens for Chinese adult liver transplant recipients. A total of 264 tacrolimus concentrations and 146 voriconazole concentrations were prospectively collected from 32 transplant recipients. A semi-PBPK model with physiological compartments including the gut wall, portal vein, and liver was developed using the nonlinear mixed-effects modeling software NONMEM (version 7.4). A web-based dashboard was established in R software (version 3.6.1) to recommend the individual tacrolimus regimens when concomitantly administered with voriconazole. The reversible inhibition of tacrolimus metabolism caused by voriconazole was investigated in both the liver and the gut wall. Moreover, voriconazole could highly inhibit the CYP3A activity in the gut wall more than in the liver. BMI and postoperative days were identified as significant covariates on intrinsic intestinal and hepatic clearance of tacrolimus, respectively. Age and postoperative days were identified as significant covariates on the volume of distribution of voriconazole. The individual tacrolimus regimens when concomitantly administered with voriconazole could be recommended in the dashboard (https://tac-vor-ddi.shinyapps.io/shinyapp3/). In conclusion, the semi-PBPK model successfully described the dynamic inhibition process between tacrolimus and voriconazole, and the web-based dashboard could provide individual tacrolimus regimens when concomitantly administered with voriconazole.

Effects of voriconazole and fluconazole on the pharmacokinetics of almonertinib in rats by UPLC-MS/MS

Almonertinib was included in the first-line treatment of non-small cell lung cancer with EGFR T790M mutations by the Chinese Society of Clinical Oncology in 2021. Considering that immunocompromised lung cancer patients are prone to opportunistic fungal infections, and most triazole antifungal drugs are moderate or strong inhibitors of CYP3A4, this study was conducted to develop and validate an accurate and rapid ultra-performance liquid chromatography tandem mass spectrometry method for quantifying almonertinib in plasma and for investigating the pharmacokinetic changes of almonertinib caused by voriconazole and fluconazole in rats. After liquid-liquid extraction with tert-butyl methyl ether, an XSelect HSS T3 column (2.1 × 100 mm, 2.5 μm, Waters) was used for the chromatographic separation of almonertinib and sorafenib-D₃ (internal standard). The analytes were detected using an AB Sciex Triple Quad 5,500 mass spectrometer in the positive ionization mode. The method exhibited great linearity (0.5-200 ng/ml, r > 0.997) and stability under the established experimental conditions. All validation experiments were in accordance with the guidelines, and the results were all within the acceptable limits. This method was successfully applied to the researches of pharmacokinetics and drug interactions for almonertinib in rats. Voriconazole and fluconazole significantly altered the pharmacokinetic profiles of almonertinib and increased the systemic exposure of almonertinib in rats to different degrees, but further human trials should be conducted to validate the results.

Quantification of the Time Course of CYP3A Inhibition, Activation, and Induction Using a Population Pharmacokinetic Model of Microdosed Midazolam Continuous Infusion

BACKGROUND

Cytochrome P450 (CYP) 3A contributes to the metabolism of many approved drugs. CYP3A perpetrator drugs can profoundly alter the exposure of CYP3A substrates. However, effects of such drug-drug interactions are usually reported as maximum effects rather than studied as time-dependent processes. Identification of the time course of CYP3A modulation can provide insight into when significant changes to CYP3A activity occurs, help better design drug-drug interaction studies, and manage drug-drug interactions in clinical practice.

OBJECTIVE

We aimed to quantify the time course and extent of the in vivo modulation of different CYP3A perpetrator drugs on hepatic CYP3A activity and distinguish different modulatory mechanisms by their time of onset, using pharmacologically inactive intravenous microgram doses of the CYP3A-specific substrate midazolam, as a marker of CYP3A activity.

METHODS

Twenty-four healthy individuals received an intravenous midazolam bolus followed by a continuous infusion for 10 or 36 h. Individuals were randomized into four arms: within each arm, two individuals served as a placebo control and, 2 h after start of the midazolam infusion, four individuals received the CYP3A perpetrator drug: voriconazole (inhibitor, orally or intravenously), rifampicin (inducer, orally), or efavirenz (activator, orally). After midazolam bolus administration, blood samples were taken every hour (rifampicin arm) or every 15 min (remaining study arms) until the end of midazolam infusion. A total of 1858 concentrations were equally divided between midazolam and its metabolite, 1'-hydroxymidazolam. A nonlinear mixed-effects population pharmacokinetic model of both compounds was developed using NONMEM^®. CYP3A activity modulation was quantified over time, as the relative change of midazolam clearance encountered by the perpetrator drug, compared to the corresponding clearance value in the placebo arm.

RESULTS

Time course of CYP3A modulation and magnitude of maximum effect were identified for each perpetrator drug. While efavirenz CYP3A activation was relatively fast and short, reaching a maximum after approximately 2-3 h, the induction effect of rifampicin could only be observed after 22 h, with a maximum after approximately 28-30 h followed by a steep drop to almost baseline within 1-2 h. In contrast, the inhibitory impact of both oral and intravenous voriconazole was prolonged with a steady inhibition of CYP3A activity followed by a gradual increase in the inhibitory effect until the end of sampling at 8 h. Relative maximum clearance changes were +59.1%, +46.7%, -70.6%, and -61.1% for efavirenz, rifampicin, oral voriconazole, and intravenous voriconazole, respectively.

CONCLUSIONS

We could distinguish between different mechanisms of CYP3A modulation by the time of onset. Identification of the time at which clearance significantly changes, per perpetrator drug, can guide the design of an optimal sampling schedule for future drug-drug interaction studies. The impact of a short-term combination of different perpetrator drugs on the paradigm CYP3A substrate midazolam was characterized and can define combination intervals in which no relevant interaction is to be expected.

CLINICAL TRIAL REGISTRATION

The trial was registered at the European Union Drug Regulating Authorities for Clinical Trials (EudraCT-No. 2013-004869-14).

Population Modelling of Dexmedetomidine Pharmacokinetics and Haemodynamic Effects After Intravenous and Subcutaneous Administration

Background and Objective

Dexmedetomidine is a potent agonist of α₂-adrenoceptors causing dose-dependent sedation in humans. Intravenous dexmedetomidine is commonly used perioperatively, but an extravascular route of administration would be favoured in palliative care. Subcutaneous infusions provide desired therapeutic plasma concentrations with fewer unwanted effects as compared with intravenous dosing. We aimed to develop semi-mechanistic population models for predicting pharmacokinetic and pharmacodynamic profiles of dexmedetomidine after intravenous and subcutaneous dosing.

Methods

Non-linear mixed-effects modelling was performed using previously collected concentration and haemodynamic effects data from ten (eight in the intravenous phase) healthy human subjects, aged 19–27 years, receiving 1 µg/kg of intravenous or subcutaneous dexmedetomidine during a 10-min infusion.

Results

The absorption of dexmedetomidine from the subcutaneous injection site, and distribution to local subcutaneous fat tissue was modelled using a semi-physiological approach consisting of a depot and fat compartment, while a two-compartment mammillary model explained further disposition. Dexmedetomidine-induced reductions in plasma norepinephrine concentrations were accurately described by an indirect response model. For blood pressure models, the net effect was specified as hyper- and hypotensive effects of dexmedetomidine due to vasoconstriction on peripheral arteries and sympatholysis mediated via the central nervous system, respectively. A heart rate model combined the dexmedetomidine-induced sympatholytic effect, and input from the central nervous system, predicted from arterial blood pressure levels. Internal evaluation confirmed the predictive performance of the final models, as well as the accuracy of the parameter estimates with narrow confidence intervals.

Conclusions

Our final model precisely describes dexmedetomidine pharmacokinetics and accurately predicts dexmedetomidine-induced sympatholysis and other pharmacodynamic effects. After subcutaneous dosing, dexmedetomidine is taken up into subcutaneous fat tissue, but our simulations indicate that accumulation of dexmedetomidine in this compartment is insignificant.

ClinicalTrials.org

NCT02724098 and EudraCT 2015-004698-34

Electronic supplementary material

The online version of this article (10.1007/s40262-020-00900-3) contains supplementary material, which is available to authorized users.

Pediatric Drug-Drug Interaction Evaluation: Drug, Patient Population, and Methodological Considerations

Hospitalized pediatric patients and those with complex or chronic conditions treated on an outpatient basis are commonly prescribed multiple drugs, resulting in increased risk for drug-drug interactions (DDIs). Although dedicated DDI evaluations are routinely performed in healthy adult volunteers during drug development, they are rarely performed in pediatric patients because of ethical, logistical, and methodological challenges. In the absence of pediatric DDI evaluations, adult DDI data are often extrapolated to pediatric patients. However, the magnitude of a DDI in pediatric patients may differ from adults because of age-dependent physiological changes that can impact drug disposition or response and because of other factors related to the drug (eg, dose, formulation) and the patient population (eg, disease state, obesity). Therefore, the DDI magnitude needs to be assessed in children separately from adults, although a lack of clinical DDI data in pediatric populations makes this evaluation challenging. As a result, pediatric DDI assessment relies on the predictive performance of the pharmacometric approaches used, such as population and physiologically based pharmacokinetic modeling. Therefore, careful consideration needs to be given to adequately account for the age-dependent physiological changes in these models to build high confidence for such untested DDI scenarios. This review article summarizes the key considerations related to the drug, patient population, and methodology, and how they can impact DDI evaluation in the pediatric population.

Differential Impacts of Azole Antifungal Drugs on the Pharmacokinetic Profiles of Dasatinib in Rats by LC-MS-MS

BACKGROUND

Dasatinib, as an oral multi-targeted inhibitor of BCR-ABL and SRC family kinases, has been widely used for the treatment of Philadelphia Chromosome Positive Leukemias in imatinib-acquired resistance and intolerance. The study aimed to develop and validate a simple and robust assay with a small volume of plasma based on liquid chromatography coupled with tandem mass spectrometry to determine the concentration of dasatinib and to investigate the impact of the cytochrome 3A4 inhibitors, including ketoconazole, voriconazole, itraconazole and posaconazole, on the pharmacokinetics of dasatinib in rats.

METHODS

Thirty rats were divided randomly into five groups, control group (0.5% carboxymethylcellulose sodium), ketoconazole (30 mg/kg) group, voriconazole group (30 mg/kg), itraconazole group (30 mg/kg) and posaconazole group (30 mg/kg). After 150 μL blood samples were collected at 0, 0.5, 1, 2, 4, 6, 8, 10, 12, 24, and 48 h and precipitated with acetonitrile, the plasma concentration of dasatinib was determined through Fluoro- Phenyl column (150 mm×2.1 mm, 3 μm) in a positive ionization mode.

RESULTS

The results suggested that ketoconazole, voriconazole, and posaconazole could increase the AUC0-t of dasatinib to varying degrees while significantly reducing its clearance. However, there was no significant impact on the pharmacokinetics of dasatinib, co-administered with itraconazole except for the CL and MRT0-t of dasatinib. Additionally, voriconazole could significantly increase Cmax of dasatinib by approximately 4.12 fold.

CONCLUSION

These data indicated that ketoconazole, posaconazole and voriconazole should be cautiously co-administered with dasatinib or close therapeutic drug monitoring of dasatinib concentration, which might cause the drug-drug interaction.

Composite midazolam and 1'-OH midazolam population pharmacokinetic model for constitutive, inhibited and induced CYP3A activity

CYP3A plays an important role in drug metabolism and, thus, can be a considerable liability for drug-drug interactions. Population pharmacokinetics may be an efficient tool for detecting such drug-drug interactions. Multiple models have been developed for midazolam, the typical probe substrate for CYP3A activity, but no population pharmacokinetic models have been developed for use with inhibition or induction. The objective of the current analysis was to develop a composite parent-metabolite model for midazolam which could adequately describe CYP3A drug-drug interactions. As an exploratory objective, parameters were assessed for potential cut-points which may allow for determination of drug-drug interactions when a baseline profile is not available. The final interaction model adequately described midazolam and 1′-OH midazolam concentrations for constitutive, inhibited, and induced CYP3A activity. The model showed good internal and external validity, both with full profiles and limited sampling (2, 2.5, 3, and 4 h), and the model predicted parameters were congruent with values found in clinical studies. Assessment of potential cut-points for model predicted parameters to assess drug-drug interaction liability with a single profile suggested that midazolam clearance may reasonably be used to detect inhibition (4.82–16.4 L/h), induction (41.8–88.9 L/h), and no modulation (16.4–41.8 L/h), with sensitivities for potent inhibition and induction of 87.9% and 83.3%, respectively, and a specificity of 98.2% for no modulation. Thus, the current model and cut-points could provide efficient and accurate tools for drug-drug liability detection, both during drug development and in the clinic, following prospective validation in healthy volunteers and patient populations.

A Novel Study Design Using Continuous Intravenous and Intraduodenal Infusions of Midazolam and Voriconazole for Mechanistic Quantitative Assessment of Hepatic and Intestinal CYP3A Inhibition

The extent of a drug-drug interaction (DDI) mediated by cytochrome P450 (CYP) 3A inhibitors is highly variable during a dosing interval, as it depends on the temporal course of victim and perpetrator drug concentrations at intestinal and hepatic CYP3A expression sites. Capturing the time course of inhibition is therefore difficult using standard DDI studies assessing changes in area under the curve; thus, a novel design was developed. In a 4-period changeover pilot study, 6 healthy men received intraduodenal or intravenous infusions of the CYP3A substrate midazolam (MDZ) at a rate of 0.26 mg/h for 24 hours. This was combined with intraduodenal or intravenous infusion of the CYP3A inhibitor voriconazole (VRZ), administered at rates of 7.5 mg/h from 8 to 16 hours and of 15 mg/h from 16 to 24 hours, after starting midazolam administration. Plasma and urine concentrations of VRZ, MDZ, and its major metabolites were quantified by liquid chromatography-tandem mass spectrometry and analyzed by semiphysiological population pharmacokinetic nonlinear mixed-effects modeling. A model including mechanism-based inactivation of the metabolizing enzymes (maximum inactivation rate constant k_inact , 2.83 h^-1 ; dissociation rate constant K I , 9.33 μM) described the pharmacokinetics of VRZ well. By introducing competitive inhibition by VRZ on primary and secondary MDZ metabolism, concentration-time profiles, MDZ and its metabolites were captured appropriately. The model provides estimates of local concentrations of substrate and inhibitor at the major CYP3A expression sites and thus of the respective dynamic extent of inhibition. A combination of intravenous and intraduodenal infusions of inhibitors and substrates has the potential to provide a more accurate assessment of DDIs occurring in both gut wall and liver.

A Pediatric Covariate Function for CYP3A-Mediated Midazolam Clearance Can Scale Clearance of Selected CYP3A Substrates in Children

Recently a framework was presented to assess whether pediatric covariate models for clearance can be extrapolated between drugs sharing elimination pathways, based on extraction ratio, protein binding, and other drug properties. Here we evaluate when a pediatric covariate function for midazolam clearance can be used to scale clearance of other CYP3A substrates. A population PK model including a covariate function for clearance was developed for midazolam in children aged 1–17 years. Commonly used CYP3A substrates were selected and using the framework, it was assessed whether the midazolam covariate function accurately scales their clearance. For eight substrates, reported pediatric clearance values were compared numerically and graphically with clearance values scaled using the midazolam covariate function. For sildenafil, clearance values obtained with population PK modeling based on pediatric concentration-time data were compared with those scaled with the midazolam covariate function. According to the framework, a midazolam covariate function will lead to systemically accurate clearance scaling (absolute prediction error (PE) < 30%) for CYP3A substrates binding to albumin with an extraction ratio between 0.35 and 0.65 when binding < 10% in adults, between 0.05 and 0.55 when binding > 90%, and with an extraction ratio ranging between these values when binding between 10 and 90%. Scaled clearance values for eight commonly used CYP3A substrates were reasonably accurate (PE < 50%). Scaling of sildenafil clearance was accurate (PE < 30%). We defined for which CYP3A substrates a pediatric covariate function for midazolam clearance can accurately scale plasma clearance in children. This scaling approach may be useful for CYP3A substrates with scarce or no available pediatric PK information.

[Interaction between atorvastatin and voriconazole in rat plasma: a HPLC-MS/MS-based study]

OBJECTIVE

To develop a high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method for simultaneous determination of atorvastatin and voriconazole in rat plasma and investigate the pharmacokinetics of atorvastatin and the changes in voriconazole concentration in rats after administration.

METHODS

Plasma samples were collected from rats after intragastric administration of atorvastatin alone or in combination with voriconazole. The samples were treated with sodium acetate acidification, and atorvastatin and voriconazole in the plasma were extracted using a liquidliquid extraction method with methyl tert-butyl ether as the extractant. The extracts were then separated on a Thermo Hypersil Gold C18 (2.1×100 mm, 1.9 μm) column within 6 min with gradient elution using acetonitrile and water (containing 0.1% formic acid) as the mobile phase; mass spectrometry detection was achieved in selective reaction monitoring (SRM) mode under the positive ion scanning mode of heated electrospray ion source (H-ESI) and using transition mass of m/z 559.2→440.2 for atorvastatin and m/z 350→280 for voriconazole, with m/z370.2→252 for lansoprazole (the internal standard) as the quantitative ion.

RESULTS

The calibration curves were linear within the concentration range of 0.01-100 ng/mL (r=0.9957) for atorvastatin and 0.025-100 ng/mL (r=0.9966) for voriconazole. The intra-day and inter-day precisions were all less than 13%, and the recovery was between 66.50% and 82.67%; the stability of the plasma samples met the requirements of testing. The AUC_{0-24 h} of atorvastatin in rat plasma after single and combined administration was 438.78±139.61 and 927.43±204.12 h·μg·L^-1, CLz/F was 23.89±8.14 and 10.43±2.58 L·h^-1·kg^-1, C_max was 149.62±131.10 and 159.37±36.83 μg/L, t_1/2 was 5.08±1.63 and (5.58±2.11 h, and T_max was 0.37±0.14 and 3.60±1.52 h, respectively; AUC_{0-24 h}, CLZ/F and T_max of atorvastatin in rat plasma differed significantly between single and combined administration. The HPLC-MS/MS system also allowed simultaneous determination of voriconazole concentration in rat plasma after combined administration.

CONCLUSIONS

The HPLC-MS/MS system we established in this study is simple, rapid and sensitive and allows simultaneous determination of atorvastatin and voriconazole in rat plasma. Some pharmacokinetic parameters of atorvastatin are changed in the presence of voriconazole, and their clinical significance needs further investigation.

Predicting the Effects of Different Triazole Antifungal Agents on the Pharmacokinetics of Tamoxifen

Tamoxifen is an antiestrogen drug that is widely used in the adjuvant chemotherapy of estrogen receptor-α (ERα)-positive breast cancer. Chemotherapy could suppress immune function in breast cancer patients, which may cause invasive fungal infections (IFIs). Triazoles (voriconazole, fluconazole, and itraconazole) were commonly used for IFI. The physiologically based pharmacokinetic (PBPK) models were developed to investigate the influence of different triazoles on tamoxifen pharmacokinetics in this paper. To investigate the influence of different triazoles (voriconazole, fluconazole, itraconazole) on tamoxifen pharmacokinetics. Adjusted physicochemical data and pharmacokinetic parameters of voriconazole, fluconazole, itraconazole, and tamoxifen were obtained from published literatures. PBPK models were built and verified in healthy subjects using GastroPlus™. Voriconazole, itraconazole, and tamoxifen were administered orally. Fluconazole was administered intravenously. Simulated plasma concentration-time curves of the voriconazole, fluconazole, itraconazole, and tamoxifen showed good agreement with the observed profiles, respectively. The DDI simulations showed that the pharmacokinetic parameters of tamoxifen were increased by various degrees when coadministered with different triazoles. In healthy subjects, the area under the plasma concentration-time curve from 0 to t h (AUC_0-t) of tamoxifen was increased by 41%, 5%, and1% when coadministrated with voriconazole, fluconazole, and itraconazole, respectively. The PBPK models adequately characterized the pharmacokinetics of tamoxifen and triazoles. Among the three triazoles, voriconazole exhibited the greatest effect on tamoxifen pharmacokinetics. In clinical practice, an effective dosage adjustment of tamoxifen may need to be considered and TDM for tamoxifen is advisable to guide dosing and optimize therapy when coadministered with voriconazole.

Semimechanistic Population Pharmacokinetic Model to Predict the Drug-Drug Interaction Between S-ketamine and Ticlopidine in Healthy Human Volunteers

Low‐dose oral S‐ketamine is increasingly used in chronic pain therapy, but extensive cytochrome P450 (CYP) mediated metabolism makes it prone to pharmacokinetic drug‐drug interactions (DDIs). In our study, concentration‐time data from five studies were used to develop a semimechanistic model that describes the ticlopidine‐mediated inhibition of S‐ketamine biotransformation. A mechanistic model was implemented to account for reversible and time‐dependent hepatic CYP2B6 inactivation by ticlopidine, which causes elevated S‐ketamine exposure in vivo. A pharmacokinetic model was developed with gut wall and hepatic clearances for S‐ketamine, its primary metabolite norketamine, and ticlopidine. Nonlinear mixed effects modeling approach was used (NONMEM version 7.3.0), and the final model was evaluated with visual predictive checks and the sampling‐importance‐resampling procedure. Our final model produces biologically plausible output and demonstrates that ticlopidine is a strong inhibitor of CYP2B6 mediated S‐ketamine metabolism. Simulations from our model may be used to evaluate chronic pain therapy with S‐ketamine.

Voriconazole greatly increases the exposure to oral buprenorphine

PURPOSE

Buprenorphine has low oral bioavailability. Regardless of sublingual administration, a notable part of buprenorphine is exposed to extensive first-pass metabolism by the cytochrome P450 (CYP) 3A4. As drug interaction studies with buprenorphine are limited, we wanted to investigate the effect of voriconazole, a strong CYP3A4 inhibitor, on the pharmacokinetics and pharmacodynamics of oral buprenorphine.

METHODS

Twelve healthy volunteers were given either placebo or voriconazole (orally, 400 mg twice on day 1 and 200 mg twice on days 2-5) for 5 days in a randomized, cross-over study. On day 5, they ingested 0.2 mg (3.6 mg during placebo phase) oral buprenorphine. We measured plasma and urine concentrations of buprenorphine and norbuprenorphine and monitored their pharmacological effects. Pharmacokinetic parameters were normalized for a buprenorphine dose of 1.0 mg.

RESULTS

Voriconazole greatly increased the mean area under the plasma concentration-time curve (AUC_0-18) of buprenorphine (4.3-fold, P < 0.001), its peak concentration (C_max) (3.9-fold), half-life (P < 0.05), and excretion into urine (A_e; P < 0.001). Voriconazole also markedly enhanced the C_max (P < 0.001), AUC_0-18 (P < 0.001), and A_e (P < 0.05) of unconjugated norbuprenorphine but decreased its renal clearance (P < 0.001). Mild dizziness and nausea occurred during both study phases.

CONCLUSIONS

Voriconazole greatly increases exposure to oral buprenorphine, mainly by inhibiting intestinal and liver CYP3A4. Effect on some transporters may explain elevated norbuprenorphine concentrations. Although oral buprenorphine is not commonly used, this interaction may become relevant in patients receiving sublingual buprenorphine together with voriconazole or other CYP3A4 or transporter inhibitors.

Assessment of Pharmacokinetic Drug-Drug Interactions in Humans: In Vivo Probe Substrates for Drug Metabolism and Drug Transport Revisited

Pharmacokinetic parameters of selective probe substrates are used to quantify the activity of an individual pharmacokinetic process (PKP) and the effect of perpetrator drugs thereon in clinical drug-drug interaction (DDI) studies. For instance, oral caffeine is used to quantify hepatic CYP1A2 activity, and oral dagibatran etexilate for intestinal P-glycoprotein (P-gp) activity. However, no probe substrate depends exclusively on the PKP it is meant to quantify. Lack of selectivity for a given enzyme/transporter and expression of the respective enzyme/transporter at several sites in the human body are the main challenges. Thus, a detailed understanding of the role of individual PKPs for the pharmacokinetics of any probe substrate is essential to allocate the effect of a perpetrator drug to a specific PKP; this is a prerequisite for reliably informed pharmacokinetic models that will allow for the quantitative prediction of perpetrator effects on therapeutic drugs, also in respective patient populations not included in DDI studies.

Characterization of Intestinal and Hepatic CYP3A-Mediated Metabolism of Midazolam in Children Using a Physiological Population Pharmacokinetic Modelling Approach

Purpose

Changes in drug absorption and first-pass metabolism have been reported throughout the pediatric age range. Our aim is to characterize both intestinal and hepatic CYP3A-mediated metabolism of midazolam in children in order to predict first-pass and systemic metabolism of CYP3A substrates.

Methods

Pharmacokinetic (PK) data of midazolam and 1-OH-midazolam from 264 post-operative children 1–18 years of age after oral administration were analyzed using a physiological population PK modelling approach. In the model, consisting of physiological compartments representing the gastro-intestinal tract and liver,intrinsic intestinal and hepatic clearances were estimated to derive values for bioavailability and plasma clearance.

Results

The whole-organ intrinsic clearance in the gut wall and liver were found to increase with body weight, with a 105 (95% confidence interval (CI): 5–405) times lower intrinsic gut wall clearance than the intrinsic hepatic clearance (i.e. 5.08 L/h (relative standard error (RSE) 10%) versus 527 L/h (RSE 7%) for a 16 kg individual, respectively). When expressed per gram of organ, intrinsic clearance increases with increasing body weight in the gut wall, but decreases in the liver, indicating that CYP3A-mediated intrinsic clearance and local bioavailability in the gut wall and liver do not change with age in parallel. The resulting total bioavailability was found to be age-independent with a median of 20.8% in children (95%CI: 3.8–50.0%).

Conclusion

In conclusion, the intrinsic CYP3A-mediated gut wall clearance is substantially lower than the intrinsic hepatic CYP3A-mediated clearance in children from 1 to 18 years of age, and contributes less to the overall first-pass metabolism compared to adults.

Electronic supplementary material

The online version of this article (10.1007/s11095-018-2458-6) contains, which is available to authorized users.

First-Pass CYP3A-Mediated Metabolism of Midazolam in the Gut Wall and Liver in Preterm Neonates

To predict first‐pass and systemic cytochrome P450 (CYP) 3A‐mediated metabolism of midazolam in preterm neonates, a physiological population pharmacokinetic model was developed describing intestinal and hepatic midazolam clearance in preterm infants. On the basis of midazolam and 1‐OH‐midazolam concentrations from 37 preterm neonates (gestational age 26–34 weeks) receiving midazolam orally and/or via a 30‐minute intravenous infusion, intrinsic clearance in the gut wall and liver were found to be very low, with lower values in the gut wall (0.0196 and 6.7 L/h, respectively). This results in a highly variable and high total oral bioavailability of 92.1% (range, 67–95%) in preterm neonates, whereas this is around 30% in adults. This approach in which intestinal and hepatic clearance were separately estimated shows that the high bioavailability in preterm neonates is explained by, likely age‐related, low CYP3A activity in the liver and even lower CYP3A activity in the gut wall.

Autoinhibitory properties of the parent but not of the N-oxide metabolite contribute to infusion rate-dependent voriconazole pharmacokinetics

AIMS

The pharmacokinetics of voriconazole show a nonlinear dose-exposure relationship caused by inhibition of its own CYP3A-dependent metabolism. Because the magnitude of autoinhibition also depends on voriconazole concentrations, infusion rate might modulate voriconazole exposure. The impact of four different infusion rates on voriconazole pharmacokinetics was investigated.

METHODS

Twelve healthy participants received 100 mg voriconazole intravenous over 4 h, 400 mg over 6 h, 4 h, and 2 h in a crossover design. Oral midazolam (3 μg) was given at the end of infusion. Blood and urine samples were collected up to 48 h. Voriconazole and its N-oxide metabolite were quantified using high-performance liquid chromatography coupled to tandem mass spectrometry. Midazolam estimated metabolic clearance (eCLmet) was calculated using a limited sampling strategy. Voriconazole-N-oxide inhibition of cytochrome P450 (CYP) isoforms 2C19 and 3A4 were assessed with the P450-Glo luminescence assay.

RESULTS

Area under the concentration-time curve for 400 mg intravenous voriconazole was 16% (90% confidence interval: 12-20%) lower when administered over 6 h compared to 2 h infusion. Dose-corrected area under the concentration-time curve for 100 mg over 4 h was 34% lower compared to 400 mg over 4 h. Midazolam eCLmet was 516 ml min^-1 (420-640) following 100 mg 4 h^-1 voriconazole, 152 ml min^-1 (139-166) for 400 mg 6 h^-1 , 192 ml min^-1 (167-220) for 400 mg 4 h^-1 , and 202 ml min^-1 (189-217) for 400 mg 2 h^-1 . Concentration giving 50% CYP inhibition of voriconazole N-oxide was 146 ± 23 μmol l^-1 for CYP3A4, and 40.2 ± 4.2 μmol l^-1 for CYP2C19.

CONCLUSIONS

Voriconazole pharmacokinetics is modulated by infusion rate, an autoinhibitory contribution voriconazole metabolism by CYP3A and 2C19 and to a lesser extent its main N-oxide metabolite for CYP2C19. To avoid reduced exposure, the infusion rate should be 2 h.

Methods and strategies for assessing uncontrolled drug-drug interactions in population pharmacokinetic analyses: results from the International Society of Pharmacometrics (ISOP) Working Group

The purpose of this work was to present a consolidated set of guidelines for the analysis of uncontrolled concomitant medications (ConMed) as a covariate and potential perpetrator in population pharmacokinetic (PopPK) analyses. This white paper is the result of an industry-academia-regulatory collaboration. It is the recommendation of the working group that greater focus be given to the analysis of uncontrolled ConMeds as part of a PopPK analysis of Phase 2/3 data to ensure that the resulting outcome in the PopPK analysis can be viewed as reliable. Other recommendations include: (1) collection of start and stop date and clock time, as well as dose and frequency, in Case Report Forms regarding ConMed administration schedule; (2) prespecification of goals and the methods of analysis, (3) consideration of alternate models, other than the binary covariate model, that might more fully characterize the interaction between perpetrator and victim drug, (4) analysts should consider whether the sample size, not the percent of subjects taking a ConMed, is sufficient to detect a ConMed effect if one is present and to consider the correlation with other covariates when the analysis is conducted, (5) grouping of ConMeds should be based on mechanism (e.g., PGP-inhibitor) and not drug class (e.g., beta-blocker), and (6) when reporting the results in a publication, all details related to the ConMed analysis should be presented allowing the reader to understand the methods and be able to appropriately interpret the results.

Integrated semi-physiological pharmacokinetic model for both sunitinib and its active metabolite SU12662

AIMS

Previously published pharmacokinetic (PK) models for sunitinib and its active metabolite SU12662 were based on a limited dataset or lacked important elements such as correlations between sunitinib and its metabolite. The current study aimed to develop an improved PK model that circumvented these limitations and to prove the utility of the PK model in treatment optimization in clinical practice.

METHODS

One thousand two hundred and five plasma samples from 70 cancer patients were collected from three PK studies with sunitinib and SU12662. A semi-physiological PK model for sunitinib and SU12662 was developed incorporating pre-systemic metabolism using non-linear mixed effects modelling (nonmem). Allometric scaling based on body weight was applied. The final model was used for simulation of the PK of different treatment regimens.

RESULTS

Sunitinib and SU12662 PK were best described by a one and two compartment model, respectively. Introduction of pre-systemic formation of SU12662 strongly improved model fit, compared with solely systemic metabolism. The clearance of sunitinib and SU12662 was estimated at 35.7 (relative standard error (RSE) 5.7%) l h(-1) and 17.1 (RSE 7.4%) l h(-1), respectively for 70 kg patients. Correlation coefficients were estimated between inter-individual variability of both clearances, both volumes of distribution and between clearance and volume of distribution of SU12662 as 0.53, 0.48 and 0.45, respectively. Simulation of the PK model predicted correctly the ratio of patients who did not reach proposed PK targets for efficacy.

CONCLUSIONS

A semi-physiological PK model for sunitinib and SU12662 in cancer patients was presented including pre-systemic metabolism. The model was superior to previous PK models in many aspects.

Semiphysiologically based pharmacokinetic model for midazolam and CYP3A mediated metabolite 1-OH-midazolam in morbidly obese and weight loss surgery patients

This study aimed to describe the pharmacokinetics of midazolam and its cytochrome P450 3A (CYP3A) mediated metabolite 1‐OH‐midazolam in morbidly obese patients receiving oral and i.v. midazolam before (n = 20) and one year after weight loss surgery (n = 18), thereby providing insight into the influence of weight loss surgery on CYP3A activity in the gut wall and liver. In a semiphysiologically based pharmacokinetic (semi‐PBPK) model in which different blood flow scenarios were evaluated, intrinsic hepatic clearance of midazolam (CL_int,H) was 2 (95% CI 1.40–1.64) times higher compared to morbidly obese patients before surgery (P < 0.01). Midazolam gut wall clearance (CL_int,G) was slightly lower in patients after surgery (P > 0.05), with low values for both groups. The results of the semi‐PBPK model suggest that, in patients after weight loss surgery, CYP3A hepatic metabolizing capacity seems to recover compared to morbidly obese patients, whereas CYP3A mediated CL_int,G was low for both populations and showed large interindividual variability.

Evacetrapib: in vitro and clinical disposition, metabolism, excretion, and assessment of drug interaction potential with strong CYP3A and CYP2C8 inhibitors

Evacetrapib is an investigational cholesteryl ester transfer protein inhibitor (CETPi) for reduction of risk of major adverse cardiovascular events in patients with high-risk vascular disease. Understanding evacetrapib disposition, metabolism, and the potential for drug-drug interactions (DDI) may help guide prescribing recommendations. In vitro, evacetrapib metabolism was investigated with a panel of human recombinant cytochromes P450 (CYP). The disposition, metabolism, and excretion of evacetrapib following a single 100-mg oral dose of (14)C-evacetrapib were determined in healthy subjects, and the pharmacokinetics of evacetrapib were evaluated in the presence of strong CYP3A or CYP2C8 inhibitors. In vitro, CYP3A was responsible for about 90% of evacetrapib's CYP-associated clearance, while CYP2C8 accounted for about 10%. In the clinical disposition study, only evacetrapib and two minor metabolites circulated in plasma. Evacetrapib metabolism was extensive. A mean of 93.1% and 2.30% of the dose was excreted in feces and urine, respectively. In clinical DDI studies, the ratios of geometric least squares means for evacetrapib with/without the CYP3A inhibitor ketoconazole were 2.37 for area under the curve (AUC)(0-∞) and 1.94 for C max. There was no significant difference in evacetrapib AUC(0-τ) or C max with/without the CYP2C8 inhibitor gemfibrozil, with ratios of 0.996 and 1.02, respectively. Although in vitro results indicated that both CYP3A and CYP2C8 metabolized evacetrapib, clinical studies confirmed that evacetrapib is primarily metabolized by CYP3A. However, given the modest increase in evacetrapib exposure and robust clinical safety profile to date, there is a low likelihood of clinically relevant DDI with concomitant use of strong CYP3A or CYP2C8 inhibitors.

Voriconazole and the liver

Voriconazole is an azole useful for the prophylaxis and the treatment of aspergillosis and other fungal infections in immunosuppressed subjects, as those found in aplasia after aggressive polychemotherapy treatments, after hematopoietic stem cell, liver or lung transplantation. Its administration in therapeutic doses lead to extremely varied serum levels from patient to patient and even to the same patient. The explanations are varied: nonlinear pharmacokinetics, certain patient-related factors, including genetic polymorphisms in the cytochrome P450 2C19 gene, the kidney and liver function, simultaneous administration with other drugs metabolised by the same cytochrome. It is recommended to maintain the serum concentrations of voriconazole between 1.5 and 4 μg/mL. At lower values its efficacy decreases and at higher values the risk of neurological toxicity increases. Even at these concentrations it is not excluded the possible appearance of a variety of toxic effects, including on the liver, manifested by cholestasis, hepatocytolisis, or their combination. It is recommended to monitor the clinical and laboratory evolution of all patients treated with voriconazole, and of the serum levels of the drug of those who belong to risk groups, even if there is still no consensus on this issue, given the lack of correlation between the serum level and the occurrence of adverse effects in many patients.

Population pharmacokinetic analysis of circadian rhythms in hepatic CYP3A activity using midazolam

Diurnal changes in the activity of drug metabolizing enzymes may contribute to the variability in drug disposition and drug effects. The aim of this study was to quantify the circadian rhythmicity exhibited by hepatic CYP3A. A 10 μg/kg intravenous bolus dose, followed by a 30-hour 4 μg/kg/h intravenous infusion of midazolam, used as a probe substrate for hepatic CYP3A activity, was administered to 16 healthy volunteers (8 males and 8 females). Blood samples were drawn hourly for 24 hours after achieving steady state, and plasma concentrations of midazolam and its main metabolite 1-OH midazolam were determined. Population pharmacokinetic analysis was performed using nonlinear mixed effects modeling. One-compartment pharmacokinetic models best described midazolam and 1-OH midazolam pharmacokinetic disposition. An unequivocal but minor diurnal pattern was identified in the midazolam plasma concentration profiles, which was described using a cosine function with a 24-hours period. The fluctuation in the relative CYP3A activity ranged between 10% above average around 15:00, and 10% below average around 03:00. None of the covariates tested had a significant impact on the parameters estimated. Although a diurnal pattern in hepatic CYP3A activity was identified, its magnitude suggests that it is small and without clinical significance for drug therapy.