European Journal of Drug Metabolism and Pharmacokinetics最新文献

Background and objectives: A pharmacokinetic model has been developed to quantify the drug-drug interactions of tacrolimus with concentration-dependent inhibition of cytochrome P450 (CYP) 3A4 from voriconazole and clarithromycin based on the CYP3A5 and CYP2C19 genotypes.

Methods: This retrospective study recruited unrelated bone marrow transplant recipients receiving oral tacrolimus concomitantly with voriconazole and clarithromycin. The published population pharmacokinetic model that implemented genotypes of CYP3A5 (tacrolimus) and CYP2C19 (voriconazole) was integrated. The tested CYP3A4 inhibition models (Sigmoid efficacy maximum [E_max], E_max, log-linear, and linear) were a function of competitive inhibition of voriconazole and mechanism-based inhibition of clarithromycin in a virtual enzyme compartment.

Results: The total tacrolimus trough concentrations were 119 points, with a median of 4.3 (range: 2.0-9.9) ng/mL (n = 3). The final model comprised the Sigmoid E_max model for voriconazole and clarithromycin, which depicted time-course alterations in tacrolimus concentration and clearance when given voriconazole and clarithromycin.

Conclusions: These findings could facilitate the model-informed precision dosing of tacrolimus after unrelated bone marrow transplant.

Background and objectives: Finerenone, a novel selective non-steroidal mineralocorticoid receptor antagonist, has been indicated in chronic kidney disease associated with type 2 diabetes mellitus. Considering the potential complications of diabetes, finerenone can be co-administered with various drugs, including fluconazole, diltiazem, and ritonavir. Given that finerenone is a substrate of cytochrome P450 (CYP) 3A4, the concurrent administration of finerenone with CYP3A4 inhibitors (diltiazem or fluconazole or ritonavir) could potentially lead to drug interactions, which may cause adverse events such as hyperkalemia. No studies have investigated interactions between finerenone and diltiazem or fluconazole or ritonavir. Therefore, this study aims to investigate the pharmacokinetic interaction of finerenone with diltiazem or fluconazole or ritonavir and to evaluate the impact of fluconazole on the pharmacodynamics of finerenone.

Methods: The pharmacokinetic study included four rat groups (n = 8 rats/group), including a control group (finerenone alone) and test groups (finerenone pretreated with diltiazem or fluconazole or ritonavir) using both non-compartment analysis (NCA) and population pharmacokinetic (pop-PK) modeling. The pop-PK model was developed using non-linear mixed-effects modeling in NONMEM^® (version 7.5.0). In the pharmacodynamic study, serum potassium (K⁺) levels were measured to assess the effects of fluconazole on finerenone-induced hyperkalemia.

Results: The NCA results indicated that the area under the plasma concentration-time curve (AUC) of finerenone increased by 1.86- and 1.95-fold when coadministered with fluconazole and ritonavir, respectively. In contrast, diltiazem did not affect the pharmacokinetics of finerenone. The pharmacokinetic profiles of finerenone were best described by a one-compartment disposition with first-order elimination and dual first-order absorption kinetics. The pop-PK modeling results demonstrated that the apparent clearance of finerenone decreased by 50.3% and 49.2% owing to the effects of fluconazole and ritonavir, respectively. Additionally, the slow absorption rate, which represents the absorption in the distal intestinal tract of finerenone, increased by 55.7% due to the effect of ritonavir. Simultaneously, a pharmacodynamic study revealed that finerenone in the presence of fluconazole caused a significant increase in K⁺ levels compared with finerenone alone.

Conclusions: Coadministration of finerenone with fluconazole or ritonavir increased finerenone exposure in rats. Additionally, the administration of finerenone in the presence of fluconazole resulted in elevated K⁺ levels in rats. Further clinical studies are required to validate these findings.

BACKGROUND AND OBJECTIVE: Currently, there is no available method for the prediction of first-in-human (FIH) dose for chimeric antigen receptor-T (CAR-T) cells. The objective of this work was to predict the FIH dose of CAR-T cells from different doses given to mice.

Methods: In this study, six scaling methods were evaluated for the prediction of FIH dose for CAR-T cells. The methods were body weight-based fixed exponents such as 1.0 and 0.75, human equivalent dose (HED) using exponents 0.33, two modified HED methods such as using total animal dose (in place of per kg basis) and body surface area in place of body weight using total animal dose with exponent 0.33 and a physiological factor derived from physiological parameters. The FIH doses of six CAR-T cells were predicted in this study. The predicted human doses were compared with the recommended human dose by the US-FDA for four CAR-T cell products, and the literature data were used for the remaining two CAR-T cells.

Results: The results indicated that the two modified HED methods and physiological factor are the best and reliable methods for the prediction of FIH dose for CAR-T cells.

Conclusions: The proposed methods are simple and accurate in their predictive power and can be used on a spreadsheet.

Background

An increase in Staphylococcus aureus infections has been reported in pediatric patients with cystic fibrosis (CF) over the last few years. This pathogen is commonly treated with vancomycin, an antibiotic for which therapeutic drug monitoring (TDM) is recommended. Updated guidelines were recently published regarding new targets of exposure for the TDM of vancomycin through a Bayesian approach, using population pharmacokinetic (popPK) models.

Objectives

This study aims to assess the predictive performance of vancomycin popPK models in pediatric patients with CF and to recommend optimal initial dosing regimens based on simulations.

Methods

Patient data were collected from two centers in Canada, and a literature review was conducted to identify all published vancomycin popPK models for pediatric CF patients. External evaluation and simulations were performed according to patient and occasion of treatment.

Results

A total of 53 vancomycin concentrations were collected from six pediatric CF patients. Only two popPK models of vancomycin for pediatric CF patients were identified through the literature review. The external evaluation results for both centers combined revealed a population bias of 28.1% and an imprecision of 33.7%. A re-estimation of parameters was performed to improve predictive performance. The optimal initial dosing regimen was 15 mg/kg/dose administered every 6 hours according to the per occasion remodel.

Conclusion

The predictive performance and identified optimal initial dosing regimens associated with the model were different depending on the data used, showing external evaluation’s importance before implementing a model in clinical practice.

Background and Objectives

Voriconazole administered concomitantly with flucloxacillin may result in subtherapeutic plasma concentrations as shown in a patient with Staphylococcus aureus sepsis and a probable pulmonary aspergillosis. After switching our patient to posaconazole, therapeutic concentrations were reached. The aim of this study was to first test our hypothesis that flucloxacillin competes with voriconazole not posaconazole for binding to albumin ex vivo, leading to lower total concentrations in plasma.

Methods

A physiologically based pharmacokinetic (PBPK) model was then applied to predict the mechanism of action of the drug–drug interaction (DDI). The model included non-linear hepatic metabolism and the effect of a severe infectious disease on cytochrome P450 (CYP) enzymes activity.

Results

The unbound voriconazole concentration remained unchanged in plasma after adding flucloxacillin, thereby rejecting our hypothesis of albumin-binding site competition. The PBPK model was able to adequately predict the plasma concentration of both voriconazole and posaconazole over time in healthy volunteers. Upregulation of CYP3A4, CYP2C9, and CYP2C19 through the pregnane X receptor (PXR) gene by flucloxacillin resulted in decreased voriconazole plasma concentrations, reflecting the DDI observations in our patient. Posaconazole metabolism was not affected, or was only limitedly affected, by the changes through the PXR gene, which agrees with the observed plasma concentrations within the target range in our patient.

Conclusions

Ex vivo experiments reported that the unbound voriconazole plasma concentration remained unchanged after adding flucloxacillin. The PBPK model describes the potential mechanism driving the drug–drug and drug–disease interaction of voriconazole and flucloxacillin, highlighting the large substantial influence of flucloxacillin on the PXR gene and the influence of infection on voriconazole plasma concentrations, and suggests a more limited effect on other triazoles.

Cytochrome P450 enzymes (CYPs) represent a diverse family of heme-thiolate proteins involved in the metabolism of a wide range of endogenous compounds and xenobiotics. In recent years, proteomics and metabolomics have been used to obtain a comprehensive insight into the role of CYPs in health and disease aspects. The objective of the present work is to better understand the status of proteomics and metabolomics in CYP research in optimizing therapeutics and patient safety from a personalized medicine approach. The literature used in this narrative review was procured by electronic search of PubMed, Medline, Embase, and Google Scholar databases. The following keywords were used in combination to identify related literature: “proteomics,” “metabolomics,” “cytochrome P450,” “drug metabolism,” “disease conditions,” “proteome,” “liquid chromatography-mass spectrometry,” “integration,” “metabolites,” “pathological conditions.” We reviewed studies that utilized proteomics and metabolomics approaches to explore the multifaceted roles of CYPs in identifying disease markers and determining the contribution of CYP enzymes in developing treatment strategies. The applications of various cutting-edge analytical techniques, including liquid chromatography-mass spectrometry, nuclear magnetic resonance, and bioinformatics analyses in CYP proteomics and metabolomics studies, have been highlighted. The identification of CYP enzymes through metabolomics and/or proteomics in various disease conditions provides key information in the diagnostic and therapeutic landscape. Leveraging both proteomics and metabolomics presents a powerful approach for an exhaustive exploration of the multifaceted roles played by CYP enzymes in personalized medicine. Proteomics and metabolomics have enabled researchers to unravel the complex connection between CYP enzymes and metabolic markers associated with specific diseases. As technology and methodologies evolve, an integrated approach promises to further elucidate the role of CYPs in human health and disease, potentially ushering in a new era of personalized medicine.

Background and objectives: Numerous clinical concerns have been expressed regarding the potential worsening of cyclin-dependent kinase 4/6 inhibitor effects in breast cancer patients because of co-administration of proton pump inhibitors. Hence, this study evaluated the effects of proton pump inhibitors on the pharmacokinetics of palbociclib and ribociclib in terms of  cytochrome P450 (CYP) 3A4 and P-glycoprotein involvement.

Methods: The effects of omeprazole and rabeprazole on drug metabolism and efflux of these drugs were investigated using molecular docking, metabolic stability assay in rat liver microsomes, human recombinant CYP3A4 (rCYP3A4) enzymes, and Caco-2 cell monolayers, and in vivo pharmacokinetics with omeprazole and rabeprazole in (5 and 10 mg/kg) 30 min and 7 days before orally dosing palbociclib and ribociclib (10 mg/kg).

Results: Omeprazole and rabeprazole inhibited CYP3A4 enzyme activity in rCYP3A4 baculosomes with a 50-60% inhibition at 30 μM. Additionally, both omeprazole and rabeprazole (10 µm) significantly reduced the P-glycoprotein-mediated drug efflux of palbociclib and ribociclib. The 7-day pretreatment of omeprazole at a dose of 10 mg/kg resulted in 24% and 26% reductions in palbociclib's mean maximum plasma concentration) C_max and area under the plasma concentration-time curve (AUC_{0-24 h}), respectively. Palbociclib's pharmacokinetics were not significantly altered by the pretreatment with rabeprazole; however, ribociclib pharmacokinetics exhibited an 83.94% increase in AUC_{0-24 h}.

Conclusion: The findings indicate that long-term treatment with therapeutic doses of both omeprazole and rabeprazole can alter the pharmacokinetics of palbociclib and ribociclib. The co-administration of rabeprazole may alter the pharmacokinetics of palbociclib and ribociclib via CYP enzyme and P-glycoprotein inhibition.

The identification of substrates for solute carriers (SLCs) handling drugs is an important challenge, owing to the major implication of these plasma membrane transporters in pharmacokinetics and drug-drug interactions. In this context, the competitive counterflow (CCF) assay has been proposed as a practical and less expensive approach than the reference functional uptake assays for discriminating SLC substrates and non-substrates. The present article was designed to summarize and discuss key-findings about the CCF assay, including its principle, applications, challenges and limits, and perspectives. The CCF assay is based on the decrease of the steady-state accumulation of a tracer substrate in SLC-positive cells, caused by candidate substrates. Reviewed data highlight the fact that the CCF assay has been used to identify substrates and non-substrates for organic cation transporters (OCTs), organic anion transporters (OATs), and organic anion transporting polypeptides (OATPs). The performance values of the CCF assay, calculated from available CCF study data compared with reference functional uptake assay data, are, however, rather mitigated, indicating that the predictability of the CCF method for assessing SLC-mediated transportability of drugs is currently not optimal. Further studies, notably aimed at standardizing the CCF assay and developing CCF-based high-throughput approaches, are therefore required in order to fully precise the interest and relevance of the CCF assay for identifying substrates and non-substrates of SLCs.

Due to interindividual variability in drug metabolism and pharmacokinetics, traditional isoniazid fixed-dose regimens may lead to suboptimal or toxic isoniazid concentrations in the plasma of patients with tuberculosis, contributing to adverse drug reactions, therapeutic failure, or the development of drug resistance. Achieving precision therapy for isoniazid requires a multifaceted approach that could integrate various clinical and genomic factors to tailor the isoniazid dose to individual patient characteristics. This includes leveraging molecular diagnostics to perform the comprehensive profiling of host pharmacogenomics to determine how it affects isoniazid metabolism, such as its metabolism by N-acetyltransferase 2 (NAT2), and studying drug-resistant mutations in the Mycobacterium tuberculosis genome for enabling targeted therapy selection. Several other molecular signatures identified from the host pharmacogenomics as well as other omics-based approaches such as gut microbiome, epigenomic, proteomic, metabolomic, and lipidomic approaches have provided mechanistic explanations for isoniazid pharmacokinetic variability and/or adverse drug reactions and thereby may facilitate precision therapy of isoniazid, though further validations in larger and diverse populations with tuberculosis are required for clinical applications. Therapeutic drug monitoring and population pharmacokinetic approaches allow for the adjustment of isoniazid dosages based on patient-specific pharmacokinetic profiles, optimizing drug exposure while minimizing toxicity and the risk of resistance. Current evidence has shown that with the integration of the host pharmacogenomics-particularly NAT2 and Mycobacterium tuberculosis genomics data along with isoniazid pharmacokinetic concentrations in the blood and patient factors such as anthropometric measurements, comorbidities, and type and timing of food administered-precision therapy approaches in isoniazid therapy can be tailored to the specific characteristics of both the host and the pathogen for improving tuberculosis treatment outcomes.

Background and objective: Ciclopirox is a widely used antifungal drug, redisposition of which has drawn increasing attentions due to multiple promising activities. The drug undergoes extensive glucuronidation, which acts as a major obstacle in the ongoing novel application and still remains poorly understood. The current study aims to phenotype ciclopirox glucuronidation pathway and as well to decipher the related species differences.

Methods: Ciclopirox glucuronidation was investigated in liver microsomes from humans (HLM) and various experimental animals. Assays with recombinant uridine diphosphate glucuronosyltransferases (UGTs), enzyme kinetic analyses and selective inhibitors were used to determine the role of individual UGTs in ciclopirox glucuronidation.

Results: HLM is highly active in ciclopirox glucuronidation with Michaelis-Menten constant (K_m), maximum velocity (V_max), and intrinsic clearance (CL_int) values of 139 μM, 7.89 nmol/min/mg, and 56 μL/min/mg, respectively. UGT1A9 displays by far the highest activity, whereas several other isoforms (UGT1A6, UGT1A7, and UGT1A8) catalyze formation of traced glucuronides. Further kinetic analysis demonstrates that UGT1A9 has a closed K_m value (167 μM) to HLM. UGT1A9 selective inhibitor (magnolol) can potently inhibit ciclopirox glucuronidation in HLM with the IC₅₀ value of 0.12 μM. The reaction displays remarkable differences across liver microsomes from mice, rats, cynomolgus monkey, minipig, and beagle dog, with the CL_int values in the range of 26-369 μL/min/mg. In addition, ciclopirox glucuronidation activities of experimental animals' liver microsomes were less sensitive to magnolol than that of HLM.

Conclusions: Ciclopirox glucuronidation displays remarkable species differences with UGT1A9 as a dominant contributor in humans. It is suggested that the pharmacological or toxicological effects of ciclopirox may be UGT1A9 and species dependent.