Clinical Pharmacokinetics最新文献

Study design and objective: Randomised, double-blind, crossover trial to confirm bioequivalence of somapacitan, a long-acting growth hormone (GH), in 5 mg/1.5 mL and 10 mg/1.5 mL strengths in equimolar doses.

Methods: Healthy participants were randomised (1:1:1) to subcutaneous somapacitan treatment in one dosing period with 5 mg/1.5 mL and two periods with 10 mg/1.5 mL. Eligibility criteria included age 18-45 years and body mass index 18.5-24.9 kg/m². Exclusion criteria included history of GH deficiency, previous GH treatment, weight > 100.0 kg and participation in any clinical trial of an investigational medicinal product within 45 days or five times the half-life of the previous investigational product before screening. Area under the curve from time 0 until last quantifiable observation (AUC_0-t), maximum serum concentration (C_max), time to C_max and terminal half-life of somapacitan and safety were assessed.

Results: In total, 33 participants were randomised. For AUC_0-t, estimated treatment ratio (ETR) (5 mg/1.5 mL versus 10 mg/1.5 mL) was 0.95 (90% confidence interval [CI] 0.89-1.01). Point estimate and 90% CIs were within the acceptance range (0.80-1.25). For C_max, ETR was 0.77 (90% CI 0.68-0.89). Point estimate and 90% CIs were outside the acceptance range (0.80-1.25). Mean insulin-like growth factor-I (IGF-I) and IGF-I standard deviation score concentration-time curves for each strength were almost identical. No new safety issues were identified.

Conclusions: Bioequivalence criterion for somapacitan 5 mg/1.5 mL and 10 mg/1.5 mL was met for AUC_0-t but not for C_max. The two strengths had equivalent IGF-I responses.

Trial registration: ClinicalTrials.gov, NCT03905850 (3 April 2019).

Introduction and objective: Pazopanib is registered for metastatic renal cell carcinoma and soft-tissue sarcoma (STS). Its variable pharmacokinetic (PK) characteristics and narrow therapeutic range provide a strong rationale for therapeutic drug monitoring (TDM). Prior studies have defined target levels of drug exposure (≥ 20.5 mg/L) linked to prolonged progression-free survival (PFS), but the added value of using TDM remains unclear. This study investigates the effect of TDM of pazopanib in patients with STS on survival outcomes and dose-limiting toxicities (DLTs) and evaluates the feasibility of TDM-guided dosing.

Methods: A TDM-guided cohort was compared to a non-TDM-guided cohort for PFS, overall survival (OS) and DLTs. PK samples were available from all patients, though not acted upon in the non-TDM-guided cohort. We evaluated the feasibility of TDM by comparing the proportion of underdosed patients in our TDM cohort with data from previous publications.

Results: A total of 122 STS patients were included in the TDM-guided cohort (n = 95) and non-TDM-guided cohort (n = 27). The average exposure in the overall population was 30.5 mg/L and was similar in both groups. Median PFS and OS did not differ between the TDM-guided cohort and non-TDM-guided cohort (respectively 5.5 vs 4.4 months, p = 0.3, and 12.6 vs 10.1 months, p = 0.8). Slightly more patients in the non-TDM-guided cohort experienced DLTs (54%) compared to the TDM-guided cohort (44%). The proportion of underdosed patients (13.3%) was halved compared to historical data (26.7%).

Conclusion: TDM reduced the proportion of patients with subtherapeutic exposure levels by ~ 50%. Nonetheless, the added value of TDM for achieving target trough levels of ≥ 20.5 mg/L for pazopanib on survival outcomes could not be confirmed in STS patients.

Introduction: Isoniazid is a first-line antituberculosis agent with high variability, which would profit from individualized dosing. Concentrations of isoniazid at 2 h (C_2h), as an indicator of safety and efficacy, are important for optimizing therapy.

Objective: The objective of this study was to establish machine learning (ML) models to predict the C_2h, that can be used for establishing an individualized dosing regimen in clinical practice.

Methods: Published population pharmacokinetic (PopPK) models for adults were searched based on PubMed and ultimately four reliable models were selected for simulating individual C_2h datasets under different conditions (demographics, genotype, ethnicity, etc.). Machine learning models were trained on simulated C_2h obtained from the four PopPK models. Five different algorithms were used for ML model building to predict C_2h. Real-world data were used for predictive performance evaluations. Virtual trials were used to compare ML-optimized doses with PopPK model-optimized doses.

Results: Categorical boosting (CatBoost) exhibited the highest prediction ability. Target C_2h can be predicted using the ML model combined with the dosing regimen and three covariates (N-acetyltransferase 2 [NAT2] genotypes, weight and race [Asians and Africans]). Real-world data validation results showed that the ML model can achieve an overall prediction accuracy of 93.4%. Using the final ML model, the mean absolute prediction error value decreased by 45.7% relative to the average of PopPK models. Using the ML-optimized dosing regimen, the probability of target attainment increased by 43.7% relative to the PopPK model-optimized dosing regimens.

Conclusion: Machine learning models were developed with great predictive performance, which can be used to determine the individualized initial dose of isoniazid in adult patients.

Polypharmacy is commonly employed in clinical settings. The potential risks of drug-drug interactions (DDIs) can compromise efficacy and pose serious health hazards. Integrating pharmacokinetics (PK) and pharmacodynamics (PD) models into DDIs research provides a reliable method for evaluating and optimizing drug regimens. With advancements in our comprehension of both individual drug mechanisms and DDIs, conventional models have begun to evolve towards more detailed and precise directions, especially in terms of the simulation and analysis of physiological mechanisms. Selecting appropriate models is crucial for an accurate assessment of DDIs. This review details the theoretical frameworks and quantitative benchmarks of PK and PD modeling in DDI evaluation, highlighting the establishment of PK/PD modeling against a backdrop of complex DDIs and physiological conditions, and further showcases the potential of quantitative systems pharmacology (QSP) in this field. Furthermore, it explores the current advancements and challenges in DDI evaluation based on models, emphasizing the role of emerging in vitro detection systems, high-throughput screening technologies, and advanced computational resources in improving prediction accuracy.

Introduction: Cabozantinib is one of the preferred treatment options in the latest metastatic renal cell carcinoma (mRCC) guidelines. Cabozantinib is also associated with high drug expenses irrespective of the used dose, because a flat-prizing model has been implemented. In addition, concomitant intake with a high-fat meal increases its bioavailability on average by 57%. Combined with the long terminal half-life of cabozantinib (99 h), this creates possibilities to extend the dosing interval to reduce drug expenses whilst maintaining equivalent exposure.

Objectives: The primary objective was to evaluate the population pharmacokinetic (POPPK) model of cabozantinib developed for its registration using real-world patients' therapeutic drug monitoring (TDM) data. The secondary objective was to design, simulate, and evaluate alternative dose regimens with the aim to reduce drug expenses whilst maintaining comparable exposure.

Methods: Retrospective TDM data from mRCC patients treated with cabozantinib were obtained. The data were evaluated using the published Food and Drug Administration (FDA) cabozantinib POPPK model, a two-compartment disposition model with a dual (fast and slow) lagged first-order absorption process derived from FDA registration documents, as a basis. Subsequently, simulations of alternative drug expenses saving regimens were evaluated.

Results: Twenty-seven mRCC patients with 75 pharmacokinetic observations were included. Patients were treated for a median of 75 days with a median dose of 40 mg. Model evaluation results showed that the cabozantinib TDM concentrations were adequately predicted by the published FDA cabozantinib POPPK model, except for a slightly higher clearance (CL) of 3.11 L/h compared to the reported value (2.23 L/h). The simulation study indicated that an alternative dose regimen that consists of taking 60 mg of cabozantinib for 2 days and then skipping 1 day results in comparable average exposure when compared with a 40 mg daily dose, both without food interaction, while saving 33.3% of the total drug expenses per month. The food effect of a high-fat meal was also taken into account when simulating other alternative dose regimens; 40 mg every 72 h combined with a high-fat meal resulted in comparable exposure when compared with a 20 mg daily dose fasted, while saving 66.7% in drug expenses.

Conclusions: In this study, the optimized cabozantinib POPPK model resulted in adequate prediction of real-world cabozantinib pharmacokinetic data. Alternative dosing regimens with and without using known food interactions were proposed that resulted in potential strategies to significantly reduce cabozantinib drug expenses.

Introduction: The activity changes of cytochrome P450 (CYP450) enzymes, along with the complicated medication scenarios in diabetes mellitus (DM) patients, result in the unanticipated pharmacokinetics (PK), pharmacodynamics (PD), and drug-drug interactions (DDIs). Physiologically based pharmacokinetic (PBPK) modeling has been a useful tool for assessing the influence of disease status on CYP enzymes and the resulting DDIs. This work aims to develop a novel diabetic PBPK population model to facilitate the prediction of PK and DDI in DM patients.

Methods: First, mathematical functions were constructed to describe the demographic and non-CYP physiological characteristics specific to DM, which were then incorporated into the PBPK model to quantify the net changes in CYP enzyme activities by comparing the PK of CYP probe drugs in DM versus non-DM subjects.

Results: The results show that the enzyme activity is reduced by 32.3% for CYP3A4/5, 39.1% for CYP2C19, and 27% for CYP2B6, while CYP2C9 activity is enhanced by 38% under DM condition. Finally, the diabetic PBPK model was developed through integrating the DM-specific CYP activities and other parameters and was further used to perform PK simulations under 12 drug combination scenarios, among which 3 combinations were predicted to result in significant PK changes in DM, which may cause DDI risks in DM patients.

Conclusions: The PBPK modeling applied herein provides a quantitative tool to assess the impact of disease factors on relevant enzyme pathways and potential disease-drug-drug-interactions (DDDIs), which may be useful for dosing regimen optimization and minimizing the DDI risks associated with the treatment of DM.