Clinical Pharmacokinetics最新文献

Background and objective: Different empirical lactation models have been published to predict the milk-to-plasma (M/P) ratio of drugs to gain knowledge on the extent of drug distribution to the breastmilk. M/P ratios will likely vary across the lactation period due to differences in physiological milk pH and fat content, which are not routinely reported in clinical lactation pharmacokinetic studies. This work aims to evaluate the sensitivity of two (a theory-based phase distribution and a log-transformed regression) lactation models for M/P prediction at different physiological milk pH and fat content.

Methods: A literature search was conducted to collate reported M/P ratios for different drugs and their physicochemical parameters required for the prediction of the M/P ratio. Two distribution models were used for M/P ratio predictions. The M/P ratio of drugs was predicted under the physiological milk pHs of 6.8, 7.0, 7.2, and 7.4 and at of 1%, 3%, and 6% fat content. Calculated M/P ratios were compared with the observed M/P ratios.

Results: A total of 200 M/P ratios for 130 compounds (40 acids and 90 bases) were collected from clinical studies and included in the analysis. For both model, precision decreases and bias increases outside the milk pH range 7.0-7.2 and fat contents more than 3%. Significant variability exists in the observed M/P ratios. Both milk pH and fat content are important parameters for model prediction.

Conclusion: Calculated M/P ratios are influenced by multiple covariates, including milk pH and fat content. The phase distribution model is less sensitive to these covariates than the log-transformed model, especially for acidic compounds. For complex matrices such as breastmilk, the actual physiological parameters of the sampled milk, at least milk fat and pH, and their distributions are required covariates to improve the prediction outcomes, design lactation pharmacokinetic studies, and inform the potential breastfed infant dose.

Background and objective: Valproic acid (VPA) demonstrates nonlinear pharmacokinetics (PK) due to a capacity-limited protein binding, which has potential implications on its total and unbound plasma concentrations, especially during hypoalbuminemia. A physiologically based pharmacokinetic (PBPK) model was developed to assess the nonlinear dose-exposure relationship of VPA with special emphasis on pediatric patients with hypoalbuminemia.

Methods: A PBPK model was first developed and evaluated in adults using PK-Sim^® and MoBi^® (v.11) and the scaled to children 1 year and older. The capacity-limited protein binding was characterized by second-order kinetics between VPA and albumin with a 2:1 molar ratio. All drug-specific parameters were informed by literature and optimized using published PK data of VPA. PK simulations were performed in virtual populations with normal and low albumin levels.

Results: The reported concentration-time profiles of total and unbound VPA were adequately predicted by the PBPK model across the age and dose range (3-120 mg/kg). The model was able to characterize the nonlinear PK, as the concentration-dependent fraction unbound (f_u) and the related dose-dependent clearance values were well predicted. Simulated steady-state trough concentrations of total VPA were less than dose-proportional and were within the therapeutic drug monitoring range of 50-100 mg/L for doses between 30 and 45 mg/kg per day in children with normal albumin concentrations. However, virtual children with hypoalbuminemia largely failed to achieve the target exposure.

Conclusion: The PBPK model helped assess the nonlinear dose-exposure relationship of VPA and the impact of albumin concentrations on the achievement of target exposure.

Background and objective: Tucatinib is a highly selective, oral, reversible, human epidermal growth factor receptor 2 (HER2)-specific tyrosine kinase inhibitor. Tucatinib is approved at a 300-mg twice-daily dose in adults in combination with trastuzumab and capecitabine for advanced HER2-postitive (HER2+) unresectable or metastatic breast cancer and in combination with trastuzumab for RAS wild-type HER2+ unresectable or metastatic colorectal cancer. This study sought to characterize the pharmacokinetics (PK) and assess sources of PK variability of tucatinib in healthy volunteers and in patients with HER2+ metastatic breast or colorectal cancers.

Methods: A population pharmacokinetic model was developed based on data from four healthy participant studies and three studies in patients with either HER2+ metastatic breast cancer or metastatic colorectal cancer using a nonlinear mixed-effects modeling approach. Clinically relevant covariates were evaluated to assess their impact on exposure, and overall model performance was evaluated by prediction-corrected visual predictive checks.

Results: A two-compartment pharmacokinetic model with linear elimination and first-order absorption preceded by a lag time adequately described tucatinib pharmacokinetic profiles in 151 healthy participants and 132 patients. Tumor type was identified as a significant covariate affecting tucatinib bioavailability and clearance, resulting in a 1.2-fold and 2.1-fold increase in tucatinib steady-state exposure (area under the concentration-time curve) in HER2+ metastatic colorectal cancer and HER2+ metastatic breast cancer, respectively, compared with healthy participants. No other covariates, including mild renal or hepatic impairment, had an impact on tucatinib pharmacokinetics.

Conclusions: The impact of statistically significant covariates identified was not considered clinically meaningful. No tucatinib dose adjustments are required based on the covariates tested in the final population pharmacokinetic model.

Clinical trial registration: NCT03723395, NCT03914755, NCT03826602, NCT03043313, NCT01983501, NCT02025192.

Stable iron isotope techniques are critical for developing strategies to combat iron deficiency anemia, a leading cause of global disability. There are four primary stable iron isotope methods to assess ferrokinetics in humans. (i) The fecal recovery method applies the principles of a metabolic balance study but offers enhanced accuracy because the amount of iron isotope present in feces can be directly traced back to the labeled dose, distinguishing it from endogenous iron lost in stool from shed intestinal cells. (ii) In the plasma isotope appearance method, plasma samples are collected for several hours after oral dosing to evaluate the rate, quantity, and pattern of iron absorption. Key metrics include the time of peak isotope concentration and the area under the curve. (iii) The erythrocyte iron incorporation method measures iron bioavailability (absorption and erythrocyte iron utilization) from a whole blood sample collected 2 weeks after oral dosing. Simultaneous administration of oral and intravenous tracers allows for separate measurements of iron absorption and iron utilization. These three methods determine iron absorption by measuring tracer concentrations in feces, serum, or erythrocytes after administration of a tracer. In contrast, (iv) in iron isotope dilution, an innovative new approach, iron of natural composition acts as the tracer, diluting an ad hoc modified isotopic signature obtained via prior isotope administration and equilibration with body iron. This technique enables highly accurate long-term studies of iron absorption, loss, and gain. This review discusses the application of these kinetic methods and their potential to address important questions in hematology and iron biology.

Background and objective: When utilizing population pharmacokinetic (popPK) models for a priori dosage individualization, selecting the best model is crucial to obtain adequate doses. We developed and evaluated several model-selection and ensembling methods, using external evaluation on the basis of therapeutic drug monitoring (TDM) samples to identify the best (set of) models per patient for a priori dosage individualization.

Methods: PK data and models describing both hospitalized patients (n = 134) receiving continuous vancomycin (26 models) and patients (n = 92) receiving imatinib in an outpatient setting (12 models) are included. Target attainment of four model-selection methods was compared with standard dosing: the best model based on external validation, uninformed model ensembling, model ensembling using a weighting scheme on the basis of covariate-stratified external evaluation, and model selection using covariates in decision trees that were subsequently ensembled.

Results: Overall, the use of PK models improved the proportion of patients exposed to concentrations within the therapeutic window for both cohorts. Relative improvement of proportion on target for best model, unweighted, weighted, and decision trees were - 7.0%, 2.3%, 11.4%, and 37.0% (vancomycin method-development); 23.2%, 7.9%, 15.6%, and, 77.2% (vancomycin validation); 40.7%, 50.0%, 59.5%, and 59.5% (imatinib method-development); and 19.0%, 28.5%, 38.0%, and 23.8% (imatinib validation), respectively.

Conclusions: The best (set of) models per patient for a priori dosage individualization can be identified using a relatively small set of TDM samples as external evaluation. Adequately performing popPK models were identified while also excluding poor-performing models. Dose recommendations resulted in more patients within the therapeutic range for both vancomycin and imatinib. Prospective validation is necessary before clinical implementation.

Antibody-drug conjugates (ADCs) have become a pivotal area in the research and development of antitumor drugs. They provide innovative possibilities for tumor therapy by integrating the tumor-targeting capabilities of monoclonal antibodies with the cytotoxic effect of small molecule drugs. Pharmacometrics, an important discipline, facilitates comprehensive understanding of the pharmacokinetic characteristics of ADCs by integrating clinical trial data through modeling and simulation. However, due to the complex structure of ADCs, their modeling approaches are still unclear. In this review, we analyzed published population pharmacokinetic models for ADCs and classified them into single-analyte, two-analyte, and three-analyte models. We also described the benefits, limitations, and recommendations for each model. Furthermore, we suggested that the development of population pharmacokinetic models for ADCs should be rigorously considered and established based on four key aspects: (1) research objectives; (2) available in vitro and animal data; (3) accessible clinical information; and (4) the capability of bioanalytical methods. This review offered insights to guide the application of pharmacometrics in the clinical research of ADCs, thereby contributing to more effective therapeutic development.

Background and objectives: Recently a number of antibody-drug conjugate (ADC) pharmacometric models have been reported in the literature, describing one or two ADC-related analytes. The objective of this analysis was to build a population pharmacokinetic (popPK) three-analyte ADC model to describe efficacy and safety of zilovertamab vedotin, an ROR1-targeting ADC conjugated to monomethyl auristatin E (MMAE).

Methods: Data from a phase 1 study of zilovertamab vedotin in subjects with hematologic malignancies was used in a stepwise ADC modeling strategy based on the simplified ADC popPK model proposed by Gibiansky. This choice provided opportunity to model three analytes: conjugated monomethyl auristatin E (acMMAE), total monoclonal antibody (total mAb), and free MMAE. The model was extrapolated to the pediatric population using a clearance maturation function and accounting for weight dependent pharmacokinetic (PK) changes.

Results: The simplified model provided a good structure to fit the adult acMMAE, total mAb, and free MMAE data. Analysis showed that MMAE was released through deconjugation of the payload and full proteolytic degradation of the acMMAE. Deconjugation was associated with an immediate release of MMAE, proteolytic clearance introduced a delay in the release of MMAE. Simulation of the model extrapolated to the pediatric population was the basis for pediatric dosing strategies for zilovertamab vedotin that were approved in the United States and European Union.

Conclusions: The total mAb, acMMAE, and free MMAE model showed a good fit to the data. The pediatric population can match the acMMAE adult exposure at the same weight-based dose regimen without concerns that the toxic MMAE concentration will reach higher levels than found in adults.

Background: Cetagliptin is a novel dipeptidyl peptidase-4 (DPP-4) inhibitor developed for the treatment of patients with type 2 diabetes (T2D). Several phase 1 studies have been conducted in China. Modelling and simulation were used to obtain cetagliptin dose for phase 3 trials in T2D patients.

Methods: A pharmacokinetic (PK)/pharmacodynamic (PD) model and model-based analysis of the relationship between hemoglobin A1c (HbA1c) and dosage was explored to guide dose selection of cetagliptin for phase 3 trials. The PK/PD data were derived from four phase 1 clinical studies, and sitagliptin 100 mg was employed as a positive control in studies 1, 3, and 4.

Results: The PK profiles of cetagliptin were well described by a two-compartment model with first-order absorption, saturated efflux, and first-order elimination. The final PD model was a sigmoid maximum inhibitory efficacy (E_max) model with the Hill coefficient. The final model accurately captured cetagliptin PK/PD, demonstrated by goodness-of-fit plots. Based on weighted average inhibition (WAI), the relationship between HbA1c and dose was well displayed. Cetagliptin 50 mg once daily or above as monotherapy or as add-on therapy appeared more effective in HbA1c reduction than sitagliptin 100 mg. Cetagliptin 50 mg or 100 mg once daily was selected as the dose for phase 3 trials of cetagliptin in T2D patients.

Conclusions: The PK/PD model supports dose selection of cetagliptin for phase 3 trials. A model‑informed approach can be used to replace a dose-finding trial and accelerate cetagliptin's development.

Background and objective: Tacrolimus is an immunosuppressant commonly administered after solid organ transplantation. It is characterized by a narrow therapeutic window and high variability in exposure, demanding personalized dosing. In recent years, population pharmacokinetic models have been suggested to guide model-informed precision dosing of tacrolimus. We aimed to provide a comprehensive overview of population pharmacokinetic models and model-informed precision dosing software modules of tacrolimus in all solid organ transplant settings, including a simulation-based investigation of the impact of covariates on exposure and target attainment.

Methods: We performed a systematic literature search to identify population pharmacokinetic models of tacrolimus in solid organ transplant recipients. We integrated selected population pharmacokinetic models into an interactive software tool that allows dosing simulations, Bayesian forecasting, and investigation of the impact of covariates on exposure and target attainment. We conducted a web survey amongst model-informed precision dosing software tool providers and benchmarked publicly available tools in terms of models, target populations, and clinical integration.

Results: We identified 80 population pharmacokinetic models, including 44 one-compartment and 36 two-compartment models. The most frequently retained covariates on clearance and distribution parameters were cytochrome P450 3A5 polymorphisms and body weight, respectively. Our simulation tool, hosted at https://lpmx.shinyapps.io/tacrolimus/ , allows thorough investigation of the impact of covariates on exposure and target attainment. We identified 15 model-informed precision dosing software tool providers, of which ten offer a tacrolimus solution and nine completed the survey.

Conclusions: Our work provides a comprehensive overview of the landscape of available tacrolimus population pharmacokinetic models and model-informed precision dosing software modules. Our simulation tool allows an interactive thorough exploration of covariates on exposure and target attainment.