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

Decreased citalopram concentration caused by enzyme induction effect of rifampin: a case report

Citalopram is a selective serotonin reuptake inhibitor used to treat depression and various anxiety disorders, which is mainly metabolized by the P450 (CYP) enzyme. Rifampin is a rifamycin with bactericidal activity against Mycobacterium tuberculosis. Rifampin significantly induces the P450 (CYP) enzyme system, which makes it susceptible to potential drug interactions with other medications. However, there have been few reports on the possible interaction between rifampin and citalopram. We report a 76-year-old patient who had been taking citalopram 20 mg daily for long-term treatment of depression. After 2 months of rifampin treatment for tuberculosis, the patient presented with intractable depressive symptoms, insomnia, and a profound sense of hopelessness. The trough plasma concentration of citalopram was monitored, which was 8.19 ng/mL, and failed to reach the guideline-recommended effective therapeutic range (50-110 ng/mL). Based on the therapeutic drug monitoring results of citalopram, the dosage of citalopram was adjusted to 40 mg daily, resulting in a significant improvement in depressive symptoms. This case provides further evidence of a clinically significant interaction that may occur between rifampin and citalopram.

Pharmacogenetic implementation for CYP2C19 and pharmacokinetics of voriconazole in children with malignancy or inborn errors of immunity

BACKGROUND

Voriconazole pharmacokinetics (PK) are known to be affected by genetic polymorphisms of drug-metabolizing enzymes such as CYP2C19; however, such information is limited for the pediatric population. The primary aim of this study is to establish a voriconazole PK model incorporating CYP2C19 phenotypes in Japanese children with malignancy or inborn errors of immunity.

METHODS

CYP2C19 genotypes were assessed by whole-genome genotyping and defined as follows: *17/*17: ultrarapid metabolizer (URM), *1/*17: rapid metabolizer (RM), *1/*1:normal metabolizer (NM), *1/*2, *1/*3, *2/*17:intermediate metabolizer (IM), and *2/*2, *2/*3, *3/*3: poor metabolizer (PM). Population PK analysis was performed. The voriconazole serum concentration profile was described by a two-compartment model with first-order absorption, mixed linear and nonlinear (Michaelis-Menten) elimination.

RESULTS

Voriconazole concentration data were available from 60 patients with a median age of 5.3 years. The phenotypes predicted from CYP2C19 genotypes were RM in 1 (2 %), NM in 21 (35 %) patients, IM in 27 (45 %) patients, and PM in 11 (18 %) patients. Underlying diseases included 38 (63%) patients with hematological malignancy and 18 (30 %) patients with inborn errors of immunity. Among the CYP2C19 phenotypes, PM was predicted to show complete inhibition (the degree of Vmax inhibition [Vmax, inh] = 100 %; Vmax = 0). The estimated parameters of Vmax,inh were +0.8 higher in patients with gamma-glutamyl transpeptidase (γ-GTP) Grade 2 or higher and +2.7 higher when C-reactive protein (CRP) levels were 2.0 mg/dL or higher.

CONCLUSION

CYP2C19 genetic polymorphisms, γ-GTP, and CRP affect Vmax,inh of voriconazole in children with malignancy or inborn errors of immunity.

Understanding Voriconazole Metabolism: A Middle-Out Physiologically-Based Pharmacokinetic Modelling Framework Integrating In Vitro and Clinical Insights

BACKGROUND AND OBJECTIVE

Voriconazole (VRC), a broad-spectrum antifungal drug, exhibits nonlinear pharmacokinetics (PK) due to saturable metabolic processes, autoinhibition and metabolite-mediated inhibition on their own formation. VRC PK is also characterised by high inter- and intraindividual variability, primarily associated with cytochrome P450 (CYP) 2C19 genetic polymorphism. Additionally, recent in vitro findings indicate that VRC main metabolites, voriconazole N-oxide (NO) and hydroxyvoriconazole (OHVRC), inhibit CYP enzymes responsible for VRC metabolism, adding to its PK variability. This variability poses a significant risk of therapeutic failure or adverse events, which are major challenges in VRC therapy. Understanding the underlying processes and sources of these variabilities is essential for safe and effective therapy. This work aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) modelling framework that elucidates the complex metabolism of VRC and the impact of its metabolites, NO and OHVRC, on the PK of the parent, leveraging both in vitro and in vivo clinical data in a middle-out approach.

METHODS

A coupled parent-metabolite PBPK model for VRC, NO and OHVRC was developed in a stepwise manner using PK-Sim® and MoBi®. Based on available in vitro data, NO formation was assumed to be mediated by CYP2C19, CYP3A4, and CYP2C9, while OHVRC formation was attributed solely to CYP3A4. Both metabolites were assumed to be excreted via renal clearance, with hepatic elimination also considered for NO. Inhibition functions were implemented to describe the complex interaction network of VRC autoinhibition and metabolite-mediated inhibition on each CYP enzyme.

RESULTS

Using a combined bottom-up and middle-out approach, incorporating data from multiple clinical studies and existing literature, the model accurately predicted plasma concentration-time profiles across various intravenous dosing regimens in healthy adults, of different CYP2C19 genotype-predicted phenotypes. All (100%) of the predicted area under the concentration-time curve (AUC) and 94% of maximum concentration (Cmax) values of VRC met the 1.25-fold acceptance criterion, with overall absolute average fold errors of 1.12 and 1.14, respectively. Furthermore, all predicted AUC and Cmax values of NO and OHVRC met the twofold acceptance criterion.

CONCLUSION

This comprehensive parent-metabolite PBPK model of VRC quantitatively elucidated the complex metabolism of the drug and emphasised the substantial impact of the primary metabolites on VRC PK. The comprehensive approach combining bottom-up and middle-out modelling, thereby accounting for VRC autoinhibition, metabolite-mediated inhibition, and the impact of CYP2C19 genetic polymorphisms, enhances our understanding of VRC PK. Moreover, the model can be pivotal in designing further in vitro experiments, ultimately allowing for extrapolation to paediatric populations, enhance treatment individualisation and improve clinical outcomes.

An open-label study to explore the optimal design of CYP3A drug-drug interaction clinical trials in healthy Chinese people

A drug-drug interaction (DDI) trial of cytochrome P450 3A (CYP3A) is a necessary part of early-phase trials of drugs mainly metabolized by this enzyme, but CYP3A DDI clinical trials do not have a standard design, especially for Chinese people. We aimed to offer specific recommendations for CYP3A DDI clinical trial design. This was an open, three-cycle, self-controlled study. Healthy subjects were given different administration strategies of CYP3A4 perpetrators. In each cycle, blood samples were collected before and within 24 h after the administration of midazolam, the CYP3A indicator substrate. The plasma concentrations of midazolam and 1-hydroxymidazolam was obtained using liquid chromatography tandem mass spectrometry assay. For CYP3A inhibition, itraconazole exposure with a loading dose could increase the exposure of midazolam by 3.21-fold based on maximum plasma concentration (Cmax), 8.37-fold based on area under the curve Pharmacology Research & Perspectives for review only from zero to the time point (AUC0-t), and 11.22-fold based on area under the curve from zero to infinity (AUC0-∞). The data were similar for itraconazole pretreatment without a loading dose. For CYP3A induction, the exposure of rifampin for 7 days decreased the plasma concentration of midazolam ~0.27-fold based on Cmax, ~0.18-fold based on AUC0-t, and ~0.18-fold based on AUC0-∞. Midazolam exposure did not significantly change when the pretreatment of rifampin increased to 14 days. This study showed that itraconazole pretreatment for 3 days without a loading dose was enough for CYP3A inhibition, and pretreatment with rifampin for 7 days could induce near-maximal CYP3A levels.

Dose-dependent induction of CYP3A activity by St. John's wort alone and in combination with rifampin

The dose dependence of the effect of enzyme inducers and the effect of the combined administration of two inducers that exert their effect via the same induction pathway (pregnane X receptor) have not been well studied. Using oral midazolam microdoses (30 μg), we have investigated CYP3A4 induction by St. John's wort (SJW) in 11 healthy volunteers using low (300 mg/day containing 7.48 mg hyperforin), therapeutic (900 mg/day), and supratherapeutic doses of SJW (1800 mg/day) for 14 days. SJW was then co-administered with rifampin (600 mg/day) for a further 7 days to evaluate the effect of the combined administration of two inducers. In addition, intravenous midazolam microdoses (10 μg) were administered before SJW, at SJW 1800 mg/day, and during administration of the two inducers to assess the hepatic contribution to total induction (semi-simultaneous administration). Administration of SJW increased oral midazolam clearance 1.96-fold (300 mg/day), 3.86-fold (900 mg/day), and 5.62-fold (1800 mg/day), and 17.5-fold after the addition of rifampin. Concurrently, the clearance of intravenous midazolam increased 2.05-fold (1800 mg/day) and 2.93-fold (SJW + rifampin). These results show that rifampin significantly enhances the induction of the highest SJW doses both hepatically and overall and suggest that these metabolic effects occur predominantly in the gut. These findings also suggest that in drug interactions involving strong and moderate enzyme inducers, the perpetrator effects of the strong inducer are decisive for the interaction.

The Use of Microdosing for In vivo Phenotyping of Cytochrome P450 Enzymes: Where Do We Stand? A Narrative Review

Cytochrome P450 (CYP) enzymes play a central role in the elimination of approximately 80% of all clinically used drugs. Differences in CYP enzyme activity between individuals can contribute to interindividual variability in exposure and, therefore, treatment outcome. In vivo CYP enzyme activity could be determined with phenotyping. Currently, (sub)therapeutic doses are used for in vivo phenotyping, which can lead to side effects. The use of microdoses (100 µg) for in vivo phenotyping for CYP enzymes could overcome the limitations associated with the use of (sub)therapeutic doses of substrates. The aim of this review is to provide a critical overview of the application of microdosing for in vivo phenotyping of CYP enzymes. A literature search was performed to find drug-drug interaction studies of CYP enzyme substrates that used microdoses of the respective substrates. A substrate was deemed sensitive to changes in CYP enzyme activity when the pharmacokinetics of the substrate significantly changed during inhibition and induction of the enzyme. On the basis of the currently available evidence, the use of microdosing for in vivo phenotyping for subtypes CYP1A2, CYP2C9, CYP2D6, and CYP2E1 is not recommended. Microdosing can be used for the in vivo phenotyping of CYP2C19 and CYP3A. The recommended microdose phenotyping test for CYP2C19 is measuring the omeprazole area-under-the-concentration-time curve over 24 h (AUC0-24) after administration of a single 100 µg dose. CYP3A activity could be best determined with a 0.1-75 µg dose of midazolam, and subsequently measuring AUC extrapolated to infinity (AUC∞) or clearance. Moreover, there are two metrics available for midazolam using a limited sampling strategy: AUC over 10 h (AUC0-10) and AUC from 2 to 4 h (AUC2-4).

PBTK model-based analysis of CYP3A4 induction and the toxicokinetics of the pyrrolizidine alkaloid retrorsine in man

Cytochrome P450 (CYP)3A4 induction by drugs and pesticides plays a critical role in the enhancement of pyrrolizidine alkaloid (PA) toxicity as it leads to increased formation of hepatotoxic dehydro-PA metabolites. Addressing the need for a quantitative analysis of this interaction, we developed a physiologically-based toxicokinetic (PBTK) model. Specifically, the model describes the impact of the well-characterized CYP3A4 inducer rifampicin on the kinetics of retrorsine, which is a prototypic PA and contaminant in herbal teas. Based on consumption data, the kinetics after daily intake of retrorsine were simulated with concomitant rifampicin treatment. Strongest impact on retrorsine kinetics (plasma AUC24 and C max reduced to 67% and 74% compared to the rifampicin-free reference) was predicted directly after withdrawal of rifampicin. At this time point, the competitive inhibitory effect of rifampicin stopped, while CYP3A4 induction was still near its maximum. Due to the impacted metabolism kinetics, the cumulative formation of intestinal retrorsine CYP3A4 metabolites increased to 254% (from 10 to 25 nmol), while the cumulative formation of hepatic CYP3A4 metabolites was not affected (57 nmol). Return to baseline PA toxicokinetics was predicted 14 days after stop of a 14-day rifampicin treatment. In conclusion, the PBTK model showed to be a promising tool to assess the dynamic interplay of enzyme induction and toxification pathways.

High-throughput assay to simultaneously evaluate activation of CYP3A and the direct and time-dependent inhibition of CYP3A, CYP2C9, and CYP2D6 using liquid chromatography-tandem mass spectrometry

In the early stages of drug discovery, adequate evaluation of the potential drug-drug interactions (DDIs) of drug candidates is important. Several CYP3A activators are known to lead to underestimation of DDIs. These compounds affect midazolam 1'-hydroxylation but not midazolam 4-hydroxylation.We used both metabolic reactions of midazolam to evaluate the activation and inhibition of CYP3A activators simultaneously. For our CYP inhibition assay using cocktail probe substrates, simultaneous liquid chromatography-tandem mass spectrometry monitoring of 1'-hydroxymidazolam and 4-hydroxymidazolam for CYP3A was established in addition to monitoring of 4-hydroxydiclofenac and 1'-hydroxybufuralol for CYP2C9 and CYP2D6.The results of our cocktail inhibition assay were well correlated with those of a single inhibition assay, as were the estimated inhibition parameters for typical CYP3A inhibitors. In our assay, a proprietary compound that activated midazolam 1'-hydroxylation and tended to inhibit 4-hydroxylation was evaluated along with known CYP3A activators. All compounds were well characterised by comparison of the results of midazolam 1'- and 4-hydroxylation.In conclusion, our CYP cocktail inhibition assay can detect CYP3A activation and assess the direct and time-dependent inhibition potentials for CYP3A, CYP2C9, and CYP2D6. This method is expected to be very efficient in the early stages of drug discovery.

Towards Model-Informed Precision Dosing of Voriconazole: Challenging Published Voriconazole Nonlinear Mixed-Effects Models with Real-World Clinical Data

BACKGROUND AND OBJECTIVES

Model-informed precision dosing (MIPD) frequently uses nonlinear mixed-effects (NLME) models to predict and optimize therapy outcomes based on patient characteristics and therapeutic drug monitoring data. MIPD is indicated for compounds with narrow therapeutic range and complex pharmacokinetics (PK), such as voriconazole, a broad-spectrum antifungal drug for prevention and treatment of invasive fungal infections. To provide guidance and recommendations for evidence-based application of MIPD for voriconazole, this work aimed to (i) externally evaluate and compare the predictive performance of a published so-called 'hybrid' model for MIPD (an aggregate model comprising features and prior information from six previously published NLME models) versus two 'standard' NLME models of voriconazole, and (ii) investigate strategies and illustrate the clinical impact of Bayesian forecasting for voriconazole.

METHODS

A workflow for external evaluation and application of MIPD for voriconazole was implemented. Published voriconazole NLME models were externally evaluated using a comprehensive in-house clinical database comprising nine voriconazole studies and prediction-/simulation-based diagnostics. The NLME models were applied using different Bayesian forecasting strategies to assess the influence of prior observations on model predictivity.

RESULTS

The overall best predictive performance was obtained using the aggregate model. However, all NLME models showed only modest predictive performance, suggesting that (i) important PK processes were not sufficiently implemented in the structural submodels, (ii) sources of interindividual variability were not entirely captured, and (iii) interoccasion variability was not adequately accounted for. Predictive performance substantially improved by including the most recent voriconazole observations in MIPD.

CONCLUSION

Our results highlight the potential clinical impact of MIPD for voriconazole and indicate the need for a comprehensive (pre-)clinical database as basis for model development and careful external model evaluation for compounds with complex PK before their successful use in MIPD.