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

Physiologically Based Pharmacokinetic Modeling to Refine Dosing of Posaconazole in Young Children

PURPOSE

Posaconazole is a broad-spectrum antifungal for treating and preventing invasive fungal infections (IFIs) in immunocompromised individuals, including children as young as 2 years. Available in delayed-release (DR) oral suspension, intravenous formulation, and older immediate-release (IR) formulation (off-label in younger children), dosing harmonization across age groups and formulations remains inconsistent. This inconsistency arises from the unique physiology of young children and posaconazole's pH-dependent absorption. Limited pharmacokinetic (PK) data for children under 2 years complicates dosing, as absorption, distribution, metabolism, and excretion processes are underdeveloped and age-dependent. This work aims to harmonize pediatric dosing for children aged 2 to 7 years and extend dosing guidance for those aged 6 months to 2 years using physiologically-based PK (PBPK) modeling.

METHODS

An adult PBPK model was created using posaconazole's physicochemical properties and ADME characteristics with virtual populations from PK-Sim. Calibrated with single-dose data from healthy subjects, the model was verified by predicting PK following multiple doses in adults at risk for IFIs. The model was then scaled to children, accounting for developmental anatomy and physiology, including UGT1A4 ontogeny. The pediatric model was evaluated against observed data from children aged 2 to 7 years. Simulations were conducted to harmonize dosing across formulations and extend dosing to children as young as 6 months, acknowledging standard plasma concentration targets for treatment of IFIs (1000 ng/mL) as well as prophylaxis (700 ng/mL).

FINDINGS

The pediatric model adequately captured observed PK data from literature following all three formulations. The IR oral suspension is impractical and likely subtherapeutic for most children under 7 years due to solubility limits. Intravenous doses of 11-13 mg/kg once daily (QD) may be optimal for treatment, and 8 to 9 mg/kg QD for prophylaxis, varying by age. Oral DR suspension doses of 12 to 14 mg/kg QD for treatment and 8.5 to 10 mg/kg QD for prophylaxis may be optimal, also age-dependent. Dividing the total daily dose by a factor of 0.7 and administering twice daily can achieve similar trough levels.

IMPLICATIONS

PBPK modeling for posaconazole bridges the gap between PK principles and clinical practice, potentially improving therapeutic outcomes and minimizing risks associated with inadequate dosing in pediatric patients.

Physiologically Based Pharmacokinetic Modeling of Tofacitinib: Predicting Drug Exposure and Optimizing Dosage in Special Populations and Drug-Drug Interaction Scenarios

Background: Tofacitinib is mainly used in the adult population for immune-mediated inflammatory diseases. There is little information available on the pharmacokinetics of tofacitinib in pediatric patients, populations with hepatic impairment and renal impairment, and patients with drug-drug interactions (DDIs). This study aimed to develop a physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of tofacitinib in the populations mentioned above. Methods: We developed the PBPK models in PK-Sim® and evaluated the models with observed clinical PK data. The Monte Carlo algorithm was used for parameter identification. Results: The adult PBPK model accurately simulated the pharmacokinetic profiles of all administration scenarios. The geometric mean fold errors for the predicted/observed maximum concentration and area under the curve are 1.17 and 1.16, respectively. The extrapolated models accurately simulated the pharmacokinetic characteristics of tofacitinib. The pediatric patients aged 12-to-<18 years and 2-to-<6 years need to adjust the dose to 4 mg BID and 1.7 mg BID, respectively, to achieve comparable steady-state exposures to 5 mg BID in adults. The populations with moderate hepatic impairment and severe renal impairment need to reduce the dose to 50% and 75% of the original dose, respectively. Tofacitinib should be reduced to 50% and 65% of the original dose for concomitant use with fluconazole and ketoconazole, respectively, and increased to 150% of the original dose for concomitant use with rifampicin. Conclusions: We developed a tofacitinib PBPK model and extrapolated it to special populations and DDIs. The predictive results of the models can help the rational use of tofacitinib in these populations.

A Comprehensive CYP2D6 Drug-Drug-Gene Interaction Network for Application in Precision Dosing and Drug Development

Conducting clinical studies on drug-drug-gene interactions (DDGIs) and extrapolating the findings into clinical dose recommendations is challenging due to the high complexity of these interactions. Here, physiologically-based pharmacokinetic (PBPK) modeling networks present a new avenue for exploring such complex scenarios, potentially informing clinical guidelines and handling patient-specific DDGIs at the bedside. Moreover, they provide an established framework for drug-drug interaction (DDI) submissions to regulatory agencies. The cytochrome P450 (CYP) 2D6 enzyme is particularly prone to DDGIs due to the high prevalence of genetic variation and common use of CYP2D6 inhibiting drugs. In this study, we present a comprehensive PBPK network covering CYP2D6 drug-gene interactions (DGIs), DDIs, and DDGIs. The network covers sensitive and moderate sensitive substrates, and strong and weak inhibitors of CYP2D6 according to the United States Food and Drug Administration (FDA) guidance. For the analyzed CYP2D6 substrates and inhibitors, DD(G)Is mediated by CYP3A4 and P-glycoprotein were included. Overall, the network comprises 23 compounds and was developed based on 30 DGI, 45 DDI, and seven DDGI studies, covering 32 unique drug combinations. Good predictive performance was demonstrated for all interaction types, as reflected in mean geometric mean fold errors of 1.40, 1.38, and 1.56 for the DD(G)I area under the curve ratios as well as 1.29, 1.43, and 1.60 for DD(G)I maximum plasma concentration ratios. Finally, the presented network was utilized to calculate dose adaptations for CYP2D6 substrates atomoxetine (sensitive) and metoprolol (moderate sensitive) for clinically untested DDGI scenarios, showcasing a potential clinical application of DDGI model networks in the field of model-informed precision dosing.

Pharmacokinetic Boosting of Calcineurin Inhibitors in Transplantation: Pros, Cons, and Perspectives

ABSTRACT

The concept of pharmacokinetic (PK) boosting of calcineurin inhibitors (CNI) emerged after the FDA approval of cyclosporine-A. Several studies followed, and the proof of concept was well established by the late 1990s. This also continued for the next blockbuster immunosuppressant, tacrolimus. The driver for such research was an endeavor to save costs, as both drugs were expensive due to patent protection. Two CYP inhibitors, ketoconazole and diltiazem, have been extensively studied in this context and continue to be prescribed off-label along with the CNI. It has been observed that using ketoconazole reduces the dose requirement of tacrolimus by about 50% and 30% with diltiazem, which is in conformity with their pharmacological actions. Off-label co-prescription of these drugs with CNI is often encountered in low and middle-income countries. The foremost reason cited is economic. This article collates the evidence from the clinical studies that evaluate the PK-boosting effects of CNI and also reviews the gaps in the current evidence base. The current knowledge prevents the transplant community from making meaningful inferences about the risks and benefits of such strategies. Although the PK-boosting strategy can lead to serious adverse events, emerging evidence suggests that it may be advantageous for individuals with high CNI dose requirements. Hence, PK boosting may be an unmet need in the therapeutics of CNI. Nevertheless, there are several unanswered questions surrounding such use, and therefore, this merits testing in well-designed clinical studies. Moreover, drugs with better safer profiles and a history of successful PK boosting may be considered for evaluation with CNI.

Utility of physiologically based pharmacokinetic modeling in predicting and characterizing clinical drug interactions

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic dynamic modeling approach that can be used to predict or retrospectively describe changes in drug exposure due to drug-drug interactions (DDIs). With advancements in commercially available PBPK software, PBPK DDI modeling has become a mainstream approach from early drug discovery through to late-stage drug development and is often used to support regulatory packages for new drug applications. This Minireview will briefly describe the approaches to predicting DDI using PBPK and static modeling approaches, the basic model structures and features inherent to PBPK DDI models, and key examples where PBPK DDI models have been used to describe complex DDI mechanisms. Future directions aimed at using PBPK models to characterize transporter-mediated DDI, predict DDI in special populations, and assess the DDI potential of protein therapeutics will be discussed. A summary of the 209 PBPK DDI examples published to date in 2023 will be provided. Overall, current data and trends suggest a continued role for PBPK models in the characterization and prediction of DDI for therapeutic molecules. SIGNIFICANCE STATEMENT: Physiologically based pharmacokinetic (PBPK) models have been a key tool in the characterization of various pharmacokinetic phenomena, including drug-drug interactions. This Minireview will highlight recent advancements and publications around physiologically based pharmacokinetic drug-drug interaction modeling, an important area of drug discovery and development research in light of the increasing prevalence of polypharmacology in clinical settings.

3D spheroid HepaRG and fluorescent biphasic tracer for CYP3A4-mediated antibiotic interaction monitoring in sepsis

Cytochrome P450 3A4 (CYP3A4) is a crucial enzyme in the metabolism of xenobiotics, particularly in drug metabolism interactions (DDIs), making it a significant factor in clinical drug use. However, current assay techniques are both laborious and costly, making it difficult to construct a high-throughput monitoring method that can be used in conjunction with the clinic. This poses certain safety hazards for drug combination. Therefore, it is crucial to develop a synchronized monitoring method for the inhibition and induction of CYP3A4. In this study, we utilized 3D culture technology to develop a HepaRG cells spheroid model. The CYP450 and transporter expression, the albumin secretion, and urea synthesis capacity characteristics were analyzed. The NEN probe was utilized as a tracer molecule for CYP3A4. The fluorescence intensity of metabolites was characterized by laser confocal technique to determine the inhibition and expression of CYP3A4 in the HepaRG cell spheroid model by the antibiotics for sepsis. The results indicate that the HepaRG sphere model successfully possessed the physiological phenotype of the liver, which could be used for drug interaction monitoring. Through positive drug testing, NEN probe was able to achieve bidirectional characterization of CYP3A4 induction and inhibition. The monitoring method described in this paper was successfully applied to drug interaction monitoring of commonly used antibiotics in sepsis patients, which is a convenient and rapid monitoring method. The proposed method offers a new strategy for monitoring CYP3A4-mediated drug-drug interactions with a high-throughput assay, which will help to improve the safety of clinical drug combination.

Physiologically based kinetic (PBK) modeling of propiconazole using a machine learning-enhanced read-across approach for interspecies extrapolation

A significant challenge in the traditional human health risk assessment of agrochemicals is the uncertainty in quantifying the interspecies differences between animal models and humans. To work toward a more accurate and animal-free risk determination, new approaches such as physiologically based kinetic (PBK) modeling have been used to perform dosimetry extrapolation from animals to humans. However, the regulatory use and acceptance of PBK modeling is limited for chemicals that lack in vivo animal pharmacokinetic (PK) data, given the inability to evaluate models. To address these challenges, this study developed PBK models in the absence of in vivo PK data for the fungicide propiconazole, an activator of constitutive androstane receptor (CAR)/pregnane X receptor (PXR). A fit-for-purpose read-across approach was integrated with hierarchical clustering - an unsupervised machine learning algorithm, to bridge the knowledge gap. The integration allowed the incorporation of a broad spectrum of attributes for analog consideration, and enabled the analog selection in a simple, reproducible, and objective manner. The applicability was evaluated and demonstrated using penconazole (source) and three pseudo-unknown target chemicals (epoxiconazole, tebuconazole and triadimefon). Applying this machine learning-enhanced read-across approach, difenoconazole was selected as the most appropriate analog for propiconazole. A mouse PBK model was developed and evaluated for difenoconazole (source), with the mode of action of CAR/PXR activation incorporated to simulate the in vivo autoinduction of metabolism. The difenoconazole mouse model then served as a template for constructing the propiconazole mouse model. A parallelogram approach was subsequently applied to develop the propiconazole rat and human models, enabling a quantitative assessment of interspecies differences in dosimetry. This integrated approach represents a substantial advancement toward refining risk assessment of propiconazole within the framework of animal alternative safety assessment strategies.

A physiologically-based pharmacokinetic precision dosing approach to manage dasatinib drug-drug interactions

Dasatinib, a second-generation tyrosine kinase inhibitor, is approved for treating chronic myeloid and acute lymphoblastic leukemia. As a sensitive cytochrome P450 (CYP) 3A4 substrate and weak base with strong pH-sensitive solubility, dasatinib is susceptible to enzyme-mediated drug-drug interactions (DDIs) with CYP3A4 perpetrators and pH-dependent DDIs with acid-reducing agents. This work aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) model of dasatinib to describe and predict enzyme-mediated and pH-dependent DDIs, to evaluate the impact of strong and moderate CYP3A4 inhibitors and inducers on dasatinib exposure and to support optimized dasatinib dosing. Overall, 63 plasma profiles from perorally administered dasatinib in healthy volunteers and cancer patients were used for model development. The model accurately described and predicted plasma profiles with geometric mean fold errors (GMFEs) for area under the concentration-time curve from the first to the last timepoint of measurement (AUClast) and maximum plasma concentration (Cmax) of 1.27 and 1.29, respectively. Regarding the DDI studies used for model development, all (8/8) predicted AUClast and Cmax ratios were within twofold of observed ratios. Application of the PBPK model for dose adaptations within various DDIs revealed dasatinib dose reductions of 50%-80% for strong and 0%-70% for moderate CYP3A4 inhibitors and a 2.3-3.1-fold increase of the daily dasatinib dose for CYP3A4 inducers to match the exposure of dasatinib administered alone. The developed model can be further employed to personalize dasatinib therapy, thereby help coping with clinical challenges resulting from DDIs and patient-related factors, such as elevated gastric pH.

Physiologically based pharmacokinetic modeling of imatinib and N-desmethyl imatinib for drug-drug interaction predictions

The first-generation tyrosine kinase inhibitor imatinib has revolutionized the development of targeted cancer therapy and remains among the frontline treatments, for example, against chronic myeloid leukemia. As a substrate of cytochrome P450 (CYP) 2C8, CYP3A4, and various transporters, imatinib is highly susceptible to drug-drug interactions (DDIs) when co-administered with corresponding perpetrator drugs. Additionally, imatinib and its main metabolite N-desmethyl imatinib (NDMI) act as inhibitors of CYP2C8, CYP2D6, and CYP3A4 affecting their own metabolism as well as the exposure of co-medications. This work presents the development of a parent-metabolite whole-body physiologically based pharmacokinetic (PBPK) model for imatinib and NDMI used for the investigation and prediction of different DDI scenarios centered around imatinib as both a victim and perpetrator drug. Model development was performed in PK-Sim® using a total of 60 plasma concentration-time profiles of imatinib and NDMI in healthy subjects and cancer patients. Metabolism of both compounds was integrated via CYP2C8 and CYP3A4, with imatinib additionally transported via P-glycoprotein. The subsequently developed DDI network demonstrated good predictive performance. DDIs involving imatinib and NDMI were simulated with perpetrator drugs rifampicin, ketoconazole, and gemfibrozil as well as victim drugs simvastatin and metoprolol. Overall, 12/12 predicted DDI area under the curve determined between first and last plasma concentration measurements (AUClast) ratios and 12/12 predicted DDI maximum plasma concentration (Cmax) ratios were within twofold of the respective observed ratios. Potential applications of the final model include model-informed drug development or the support of model-informed precision dosing.

Interaction of the antifungal ketoconazole and its diphenylphosphine derivatives with lipid bilayers: Insights into their antifungal action

Ketoconazole (Ke) is an important antifungal drug, and two of its diphenylphosphinemethyl derivatives (KeP: Ph2PCH2-Ke and KeOP: Ph2P(O)CH2-Ke) have shown improved antifungal activity, namely against a yeast strain lacking ergosterol, suggesting alternative modes of action for azole compounds. In this context, the interactions of these compounds with a model of the cell membrane were investigated, using POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) large unilamellar vesicles and taking advantage of the intrinsic fluorescence of Ke, KeP and KeOP. Steady-state fluorescence spectra and anisotropy, including partition and aggregation studies, as well as fluorescence lifetime measurements, were carried out. In addition, the ability of the compounds to increase membrane permeability was assessed through carboxyfluorescein leakage. The membrane/water mole fraction partition coefficients (Kp,x): (3.31 ± 0.36) x105, (8.31 ± 1.60) x105 and (4.66 ± 0.72) x106, for Ke, KeP and KeOP, respectively, show that all three compounds have moderate to high affinity for the lipid bilayer. Moreover, KeP, and particularly KeOP interact more efficiently with POPC bilayers than Ke, which correlates well with their in vitro antifungal activity. Furthermore, although the three compounds disturb the lipid bilayer, KeOP is the quickest and most efficient one. Hence, the higher affinity and ability to permeabilize the membrane of KeOP when compared to that of KeP, despite the higher lipophilicity of the latter, points to an important role of Ph2P(O)CH2- oxygen. Overall, this work suggests that membrane interactions are important for the antifungal activity of these azoles and should be considered in the design of new therapeutic agents.

Enhancing drug-drug Interaction Prediction by Integrating Physiologically-Based Pharmacokinetic Model with Fraction Metabolized by CYP3A4

BACKGROUND

Enhancing the precision of drug-drug interaction (DDI) prediction is essential for improving drug safety and efficacy. The aim is to identify the most effective fraction metabolized by CY3A4 (fm) for improving DDI prediction using physiologically based pharmacokinetic (PBPK) models.

RESEARCH DESIGN AND METHODS

The fm values were determined for 33 approved drugs using a human liver microsome for in vitro measurements and the ADMET Predictor software for in silico predictions. Subsequently, these fm values were integrated into PBPK models using the GastroPlus platform. The PBPK models, combined with a ketoconazole model, were utilized to predict AUCR (AUCcombo with ketoconazole/AUCdosing alone), and the accuracy of these predictions was evaluated by comparison with observed AUCR.

RESULTS

The integration of in vitro fm method demonstrates superior performance compared to the in silico fm method and fm of 100% method. Under the Guest-limits criteria, the integration of in vitro fm achieves an accuracy of 76%, while the in silico fm and fm of 100% methods achieve accuracies of 67% and 58%, respectively.

CONCLUSIONS

Our study highlights the importance of in vitro fm data to improve the accuracy of predicting DDIs and demonstrates the promising potential of in silico fm in predicting DDIs.

Antibacterial and antifungal action of CTAB-containing silica nanoparticles against human pathogens

New antibiotic agents are urgently needed worldwide to combat the increasing tolerance and resistance of pathogenic fungi and bacteria to current antimicrobials. Here, we looked at the antibacterial and antifungal effects of minor quantities of cetyltrimethylammonium bromide (CTAB), ca. 93.8 mg g^-1, on silica nanoparticles (MPSi-CTAB). Our results show that MPSi-CTAB exhibits antimicrobial activity against Methicillin-resistant Staphylococcus aureus strain (S. aureus ATCC 700698) with MIC and MBC of 0.625 mg mL^-1 and 1.25 mg mL^-1, respectively. Additionally, for Staphylococcus epidermidis ATCC 35984, MPSi-CTAB reduces MIC and MBC by 99.99% of viable cells on the biofilm. Furthermore, when combined with ampicillin or tetracycline, MPSi-CTAB exhibits reduced MIC values by 32- and 16-folds, respectively. MPSi-CTAB also exhibited in vitro antifungal activity against reference strains of Candida, with MIC values ranging from 0.0625 to 0.5 mg mL^-1. This nanomaterial has low cytotoxicity in human fibroblasts, where over 80% of cells remained viable at 0.31 mg mL^-1 of MPSi-CTAB. Finally, we developed a gel formulation of MPSi-CTAB, which inhibited in vitro the growth of Staphylococcus and Candida strains. Overall, these results support the efficacy of MPSi-CTAB with potential application in the treatment and/or prevention of infections caused by methicillin-resistant Staphylococcus and/or Candida species.