European Journal of Drug Metabolism and Pharmacokinetics最新文献

Cardiovascular disease (CVD) is one of the leading causes of death worldwide, and its internal medicine treatments are mostly single/few-target chemical drugs. Long-term use of cardiovascular drugs for complex chronic diseases may lead to serious adverse drug reactions. Traditional Chinese medicine (TCM) has been used to treat heart diseases for thousands of years, helping to ease symptoms and prolong patients' lifespan in ancient China. TCM has the pharmacological characteristics of being multi-component, multi-target and multi-pathway, and the combined application of TCM and western medicine can be an alternative treatment for chronic and intractable diseases with high safety levels. This article reviewed the interactions and synergistic effect of TCM and cardiovascular drugs. In the treatment of arrhythmia, TCM combined with western medicine can more effectively regulate patients' cardiac electrophysiological characteristics, reduce the onsets of premature beat and heart rate variability, lower the levels of QT interval dispersion and serum inflammatory factors, alleviate clinical symptoms and TCM syndromes, and improve cardiac function with good safety levels. In the treatment of hypertension, integrative medicine can more steadily reduce blood pressure and levels of serum inflammatory factors and improve hemodynamic indexes and exercise tolerance, and it has high safety levels, especially for pregnant women. As for coronary heart disease, the combination of TCM and antiplatelet drugs may promote the absorption of each other. However, the interaction risk of pharmacokinetic mechanism between them is low at the dose of efficacy. Integrative medicine can reduce the level of N-terminal pro-brain natriuretic peptide, delay cardiac remodeling and improve heart function and quality of life for patients with heart failure with high safety levels.

Background and objective: Recent studies have highlighted the key role of the ATP-binding cassette (ABC) transporters, including the P-glycoprotein (P-gp), the breast cancer resistance protein (BCRP), and the multi-drug resistance protein 4 (MRP4) in limiting the brain distribution of several antiviral agents. In this study, we investigated whether the inhibition of these transporters increases the permeability of the blood-brain barrier (BBB) to ganciclovir.

Methods: A microdialysis and high-performance liquid chromatographic method was developed to monitor the concentrations of unbound ganciclovir in the brain interstitial fluid and plasma, with and without the administration of ABC transporter inhibitors. Pharmacokinetic parameters, including the area under the plasma concentration-time curve from time 0 to time of the last measurable analyte concentration (AUC_0-t,plasma), the area under the brain interstitial fluid concentration-time curve from time 0 to time of the last measurable analyte concentration (AUC_0-t,brain), and the unbound brain-to-plasma concentration ratio (K_p,uu,brain) were calculated.

Results: The mean AUC_0-t,plasma, AUC_0-t,brain, and K_p,uu,brain in rats who received ganciclovir (30 mg/kg, intraperitoneal) alone were 1090 min·µg/mL, 150 min·µg/mL, and 14%, respectively. After the administration of tariquidar (inhibitor of P-gp), Ko143 (inhibitor of BCRP), or MK-571 (inhibitor of MRP4), the K_p,uu,brain of ganciclovir increased to 31 ± 2.1%, 26 ± 1.3%, and 32 ± 2.0%, respectively.

Conclusions: The findings of this study suggest that ABC transporters P-gp, BCRP, and MRP4 mediate the efflux of ganciclovir at the BBB and that the inhibition of these transporters facilitates the penetration of the BBB by ganciclovir.

Background and objective: A model-informed drug development (MIDD) approach was implemented for paliperidone palmitate (PP) 6-month (PP6M) clinical development, using pharmacokinetics and pharmacokinetic/pharmacodynamic model-based simulations.

Methods: PP6M pharmacokinetics were simulated by extending the PP 3-month (PP3M) pharmacokinetic model to account for increased injection volume, and hence dose. Contribution of the MIDD approach to the design of the pivotal PP6M phase-3 study (PP6M/PP3M noninferiority study, NCT03345342) investigating schizophrenia relapse rates was twofold: (1) PP6M dose selection, and (2) hypothesis generation that lower trough concentrations (C_trough) associated with PP6M, relative to PP3M, were not associated with lower efficacy, which was to be evaluated in the phase-3 study. Moreover, accompanied by an intense sampling scheme to adequately characterize paliperidone pharmacokinetics and to elucidate the potential relationship between concentration and safety/efficacy, the bridging strategy eliminated the need for additional phase-1/phase-2 clinical studies.

Results: Using a MIDD bridging strategy, PP6M doses were selected that, compared with PP3M, were expected to have a similar range of exposures and a noninferior relapse rate and safety profile. Clinical data from PP6M/PP3M noninferiority study confirmed that PP6M, compared with PP3M, had a similar range of exposures (T'jollyn et al. in Eur J Drug Metab Pharmacokinet 2024), as well as a noninferior relapse rate and safety profile (this manuscript).

Conclusions: Consistency of the MIDD approach with observed clinical outcomes confirmed the hypothesis that lower C_trough did not lead to increased relapse rates at the doses administered. Although higher paliperidone peak concentrations are achieved with corresponding doses of PP6M relative to PP3M in the phase-3 clinical study, types and incidences of treatment-related adverse events were comparable between PP6M and PP3M groups and no new safety concerns emerged for PP6M (Najarian et al. in Int J Neuropsychopharmacol 25(3):238-251, 2022).

Background and objectives: Precision dosing requires selecting the appropriate population pharmacokinetic model, which can be assessed through external evaluations (EEs). The lack of understanding of how different study design factors influence EE study outcomes makes it challenging to select the most suitable model for clinical use. This study aimed to evaluate the impact of sample size, sampling strategy, and handling of concentrations below the lower limit of quantification (BLQ) on the outcomes of EE for four population pharmacokinetic models using vancomycin and tobramycin as examples.

Methods: Three virtual patient populations undergoing vancomycin or tobramycin therapy were simulated with varying sample size and sampling scenarios. The three approaches used to handle BLQ data were to (1) discard them, (2) impute them as LLOQ/2, or (3) use a likelihood-based approach. EEs were performed with NONMEM and R.

Results: Sample size did not have an important impact on the EE results for a given scenario. Increasing the number of samples per patient did not improve predictive performance for two out of the three evaluated models. Evaluating a model developed with rich sampling did not result in better performance than those developed with regular therapeutic drug monitoring. A likelihood-based method to handle BLQ samples impacted the outcomes of the EE with lower bias for predicted troughs.

Conclusions: This study suggests that a large sample size may not be necessary for an EE study, and models selected based on TDM may be more generalizable. The study highlights the need for guidelines for EE of population pharmacokinetic models for clinical use.

Background and objective: The oral first-pass metabolism is a crucial factor that plays a key role in a drug's pharmacokinetic profile. Prediction of the oral first-pass metabolism based on chemical structural parameters can be useful in the drug-design process. Developing an orally administered drug with an acceptable pharmacokinetic profile is necessary to reduce the cost and time associated with evaluating the extent of the first-pass metabolism of a candidate compound in preclinical studies. The aim of this study is to estimate the first-pass metabolism of an orally administered drug.

Methods: A set of compounds with reported first-pass metabolism data were collected. Moreover, human intestinal absorption percentage and oral bioavailability data were extracted from the literature to propose a classification system that split the drugs up based on their first-pass metabolism extents. Various structural parameters were calculated for each compound. The relations of the structural and physicochemical values of each compound to the class the compound belongs to were obtained using logistic regression.

Results: Initial analysis showed that compounds with logD_7.4 > 1 or a rugosity factor of > 1.5 are more likely to have high first-pass metabolism. Four different models that can predict the oral first-pass metabolism with acceptable error were introduced. The overall accuracies of the models were in the range of 72% (for models with simple descriptors) to 78% (for models with complex descriptors). Although the models with simple descriptors have lower accuracies compared to complex models, they are more interpretable and easier for researchers to utilize.

Conclusion: A novel classification of drugs based on the extent of the oral first-pass metabolism was introduced, and mechanistic models were developed to assign candidate compounds to the appropriate proposed classes.

Background and objective: Model-based bioequivalence (MBBE) encompasses the use of nonlinear mixed effect models supporting the estimation of pharmacokinetic endpoints to assess the relative bioavailability between multi-source drug products. This application emerges as a valuable alternative to the standard non-compartmental analysis (NCA) in bioequivalence (BE) studies in which dense sampling is not possible. In this work, we aimed to assess the application of MBBE compared to traditional methods in evaluating the relative bioavailability of two formulations with different drug release properties. Additionally, we sought to predict the performance of a modified-release formulation in a multiple-dose scenario, leveraging data from a single-dose study.

Methods: MBBE analysis was implemented to estimate the BE endpoints (90% CI for the Test/Reference geometric mean ratio, T/R GMR) in area under the concentration-time curve (AUC) and maximum concentration (Cmax) using data from a single-dose, 2-period, 2-sequence BE study performed in 14 healthy subjects between a locally developed valproic acid extended-release formulation (Test) and the brand-name delayed-release formulation (Reference).

Results: Results were compared with the standard approach, revealing that MBBE analysis achieved higher discrimination between formulations for Cmax, addressing limitations of the experimental sampling design and highlighting an advantage for this model-based analysis even when rich data are available. Additionally, the bioequivalence outcome under the multiple-dose scenario was predicted through a simulation-based study for both total and unbound valproic acid concentrations, considering the impact of valproic acid saturable binding on BE conclusions.

Conclusions: The MBBE analysis was superior to the NCA approach in detecting product-related differences, overcoming limitations in the study experimental design. Predictions for the multiple-dose scenario preclude that the extended-release properties of the Test formulation would persist at steady state, resulting in lower peak-to-trough fluctuation and bioequivalent performance in terms of the extent of drug absorption. Overall, these results should discourage unnecessary experimentation in healthy subjects.

Background and objective: Paliperidone palmitate 6-month (PP6M) intramuscular (IM) injection is the longest-acting treatment available for patients with schizophrenia. A population pharmacokinetic (popPK) modeling and simulation approach was deployed to inform dosing strategies for PP6M.

Methods: The extensive analysis database included 15,932 paliperidone samples from 700 patients receiving gluteal paliperidone palmitate 3-month (PP3M) or PP6M injections in the double-blind phase of a phase-3 noninferiority study (NCT03345342). Exposure parameters for paliperidone appeared to increase dose-proportionally within each dosing schedule (PP3M/PP6M). The range of paliperidone exposures after IM administration of PP6M overlaps with that of corresponding doses of oral paliperidone extended release, PP 1-month (PP1M), and PP3M. Model-based simulations were performed to investigate paliperidone exposures in different PP6M dosing scenarios and relevant subpopulations.

Results: A dosing window of ≤ 2 weeks earlier and ≤ 3 weeks later than the target 6-month interval for maintenance treatment with PP6M dosing maintains paliperidone exposures at levels that are not expected to substantially impact its safety and efficacy. For missed-dose scenarios, tailored re-initiation regimens are proposed that should be applied before resuming PP6M maintenance dosing. Regarding subpopulations, PP6M 700 mg eq. is the highest dose recommended in mild renal-impairment patients; the paliperidone pharmacokinetics after PP6M administration is not affected by sex, body mass index, or age in a clinically meaningful way.

Conclusion: Paliperidone concentration-time profiles after PP6M and PP3M dosing were adequately described by the popPK model. Model-based simulation results provide guidance for clinicians on initiating PP6M therapy, transitioning between paliperidone formulations, the dosing windows to use for maintenance dosing, and managing missed PP6M doses.

Background and objective: Several population pharmacokinetic (popPK) studies have been reported that can guide the prediction of osimertinib plasma concentrations in individual patients. It is currently unclear which popPK model offers the best predictive performance and which popPK models are most suitable for nonadherence management and model-informed precision dosing. Therefore, the objective of this study was to externally validate all osimertinib popPK models available in the current literature.

Methods: Published popPK models for osimertinib were constructed using NONMEM version 7.4.4. The predictive quality of the identified models was assessed with goodness-of-fit (GoF) plots, conditional weighted residuals (CWRES) plots and a prediction-corrected visual predictive check (pcVPC) for osimertinib and its active metabolite AZ5104. A subset from the Dutch OSIBOOST trial, where 11 patients with low osimertinib exposure were included, was used as evaluation cohort.

Results: The population GoF plots for all four models poorly followed the line of identity. For the individual GoF plots, all models performed comparable and were closely distributed among the line of identity. CWRES of the four models were skewed. The pcVPCs of all four models showed a similar trend, where all observed concentrations fell in the simulated shaded areas, but in the lower region of the simulated areas.

Conclusion: All four popPK models can be used to individually predict osimertinib concentrations in patients with low osimertinib exposure. For population predictions, all four popPK models performed poorly in patients with low osimertinib exposure. A novel popPK model with good predictive performance should be developed for patients with low osimertinib exposure. Ideally, the cause for the relatively low osimertinib exposure in our evaluation cohort should be known.

Clinical trials registration: NCT03858491.

Background and objective: Astaxanthin is a naturally occurring carotenoid with high anti-oxidant properties, but it is a very lipophilic compound with low oral bioavailability. This study was conducted to compare the pharmacokinetic parameters of a novel astaxanthin preparation based on micellar solubilization technology, NovaSOL^® 400-mg capsules (Test product), and those of astaxanthin 400-mg capsules (reference product), after single oral dose administration to healthy male adults.

Methods: A single oral dose (400 mg equivalent to 8 mg astaxanthin) of test and reference astaxanthin were administered with 240 mL of water to 12 volunteers according to crossover design, in two phases, with a washout period of 1 week in between. Blood samples were collected at hourly intervals for the first 12 h, then at 24.0, 48.0, and 72.0 h after administration. Aliquots of plasma were centrifuged and the clear supernatant was injected into the high performance liquid chromatography-diode array detection (HPLC-DAD) system. Plasma concentration of astaxanthin versus time profiles were constructed, and the primary pharmacokinetic parameters, maximum concentration (C_max), area under concentration time curve from time of administration (0) to time (t) [AUC_0-t] or to infinity ∞, [AUC_0-∞],  half-life (T_½) and time to reach C_max (T_max) were calculated.

Results: The test micellar astaxanthin reached a C_max of 7.21 µg/ml after 3.67 h compared to only 3.86 µg/ml after 8.5 h for the reference native astaxanthin.

Conclusion: Micellar formulation of astaxanthin is capable of producing a high concentration of astaxanthin in plasma in a shorter time, thereby expected to provide faster potential therapeutic efficacy.