Clinical Pharmacokinetics最新文献

Background: DPP-4 inhibitors are a class of oral hypoglycemic drugs commonly used in the treatment of type 2 diabetes mellitus (T2DM). Fultagliptin benzoate is a novel and high-selective DPP-4 inhibitor. The renal excretion of many medications, including DPP-4 inhibitors, can be altered in patients with renal impairment. This study investigates the pharmacokinetics, pharmacodynamics, and safety of fultagliptin benzoate tablets in mild and moderate renal impairment (RI) compared with normal renal function to ensure safety and efficacy across these groups.

Methods: A single-dose, non-randomized, open-label, and parallel-control phase I study was performed. Pharmacokinetics, pharmacodynamics, and safety of fultagliptin benzoate tablets were evaluated.

Results: A total of 18 participants were enrolled and completed the study. After oral administration of a single dose of fultagliptin benzoate tablets 12 mg under fasted conditions, fultagliptin was absorbed with median time to reach peak concentration (T_max) of 4.50, 3.00, and 5.50 hours in healthy participants, patients with mild RI, and patients with moderate RI, respectively, and eliminated with mean elimination half-life (t_½) of 32.27, 40.22, and 46.38 h, respectively. Compared with healthy participants, patients with mild RI had similar peak concentration (C_max), while area under the concentration-time curve from time zero to the last measured concentration (AUC_0-t) and AUC from time zero to infinity (AUC_0-∞) increased by 24.7% and 25.0%, respectively. In patients with moderate RI, C_max, AUC_0-t, and AUC_0-∞ increased by 27.5%, 86.4%, and 87.9%, respectively. Comparing the renal clearance rate (CL_R) with that of the healthy participants (8.96 ± 2.24 L/h), the CL_R values (mean ± SD) of patients with mild RI and patients with moderate RI were 6.615 ± 1.34 L/h and 4.54 ± 1.20 L/h, respectively, which were approximately 73.8% and 50.6% of that of the healthy participants. For pharmacodynamic biomarkers, the DPP-4 inhibition rate (E_max) in all groups of participants was > 90%. The duration of DPP-4 inhibition rate over 80% in the three groups showed an increasing trend. Fultagliptin benzoate tablets were well tolerated in all participants during the study.

Conclusion: In patients with mild RI, the small increase in exposure to fultagliptin was not considered clinically relevant. Although a significant increase in exposure was observed in patients with moderate RI without eliciting issues related to efficacy and safety, a half-dose reduction may be considered for the protection of participants.

Trial registration: ClinicalTrials.gov (NCT06883656); registered 12 March 2025 (retrospectively registered).

Introduction: Peptide-based therapeutics represent a rapidly expanding class of drugs. Endogenous peptides typically exhibit short elimination half-lives due to proteolytic cleavage and renal filtration. However, modifications such as amino acid substitutions and fatty-acid conjugation can significantly prolong their half-lives, enabling, for example, once weekly dosing. This study revisits the systemic pharmacokinetics of peptide drugs using classical pharmacokinetic principles and elucidates the scaling of peptide pharmacokinetics across species.

Methods: Preclinical and clinical pharmacokinetic data were collected from published literature and AstraZeneca internal sources, covering four unconjugated peptides (teduglutide, apraglutide, pramlintide, and exenatide) and five fatty-acid conjugated peptides (tirzepatide, cotadutide, liraglutide, semaglutide and pemvidutide). Algebraic equations for clearance, volume of distribution, and plasma half-life were derived.

Results: Theoretical predictions from these models were broadly consistent with collected data; however, there was a tendency to overpredict the volume of distribution. Furthermore, for each peptide drug, these pharmacokinetic parameters were well described by inter-species allometric relationships. The allometric exponents for apparent clearance ranged from 0.58 to 0.88 (geometric mean: 0.72; n = 9; R² ≥ 0.93), while those for apparent volume of distribution ranged from 0.89 to 1.1 (geometric mean: 0.98; n = 8; R² ≥ 0.88). Notably, there were no differences in scaling exponents between unconjugated and fatty-acid conjugated peptides.

Conclusion: In summary, our results underscore that the systemic pharmacokinetics of peptide drugs generally follow size-related physiological scaling patterns and provide quantitative tools to facilitate translational assessments in the drug discovery process.

Background and objective: This study evaluated the probability of pharmacokinetic/pharmacodynamic efficacy target attainment and developed a dosing interval identification nomogram to minimize toxicity risks of high-dose amikacin.

Methods: Therapeutic drug monitoring was performed in critically ill patients receiving high-dose (30 mg/kg) amikacin intravenously. Population pharmacokinetic modeling and Monte Carlo simulation were performed in NONMEM^® 7.5.1. The probability of target attainment and the cumulative fraction of response for the local Klebsiella pneumoniae population were assessed using maximum concentration/minimum inhibitory concentration ≥ 8 as the efficacy target. Nomograms stratified by the renal function group were established to guide dosing intervals to avoid exceeding the toxicity minimum concentration threshold of 2.5 mg/L.

Results: A total of 251 patients with 488 amikacin concentrations were included. The amikacin pharmacokinetics was best described by a two-compartment model. The creatinine clearance and adjusted body weight were significant covariates for clearance and the central volume of distribution, respectively. Amikacin 30 mg/kg achieved a probability of target attainment > 90% for minimum inhibitory concentrations < 8 mg/L and around 80% for minimum inhibitory concentrations of 8 mg/L. This regimen showed a cumulative fraction of response of 63.5% for K. pneumoniae, while attaining a cumulative fraction of response of 96.8% for susceptible isolates. The 30-mg/kg nomograms in patients with creatinine clearance < 60 and ≥ 60 mL/min showed accurate dosing intervals with around 90% of virtual patients for the post-infusion period from 20 to 32 h and from 6 to 32 h, respectively.

Conclusions: Nomogram-aided dosing interval adjustment of high-dose amikacin (30 mg/kg) maximized the efficacy and safety target attainment for critically ill patients with infections caused by susceptible K. pneumoniae.

Background and objective: Dose optimization of vancomycin in distinct pediatric subpopulations is inherently complex due to altered vancomycin pharmacokinetics associated with certain conditions or circumstances, such as cancer or postoperative cardiac surgery. Numerous population pharmacokinetic models have been developed that aim to capture these alterations; however, it is currently unclear whether these specialized models are necessary, or if covariates in a well-specified general model can adequately capture pharmacokinetic variation between special subpopulations. Here, we conduct an external evaluation comparing the predictive performance of published general and specialized population pharmacokinetic models in pediatric oncology and pediatric cardiovascular intensive care unit (CVICU) patients in order to address this question, and guide model selection decisions for model-informed precision dosing of vancomycin in these subpopulations.

Methods: The predictive error, bias, and accuracy of two general, six oncology-supporting, and three CVICU-supporting pharmacokinetic models were compared in two multi-site data sets of pediatric oncology (N = 371, 1392 drug levels, 20 sites) and pediatric CVICU (N = 219, 1136 drug levels, 11 sites) patients, respectively. The best performing model(s) in each subpopulation were refit to evaluate whether predictive performance could be further improved over the published models.

Results: We find that although specialized models performed better than general population models for pediatric CVICU patients, a general model (Colin 2019) performed better than all specialized models for pediatric oncology patients. We additionally report a refit version of the Shimamoto 2024 model for pediatric CVICU patients, which performed better than all published CVICU-supporting models in our data set.

Conclusion: Population pharmacokinetic models developed on distinct pediatric subpopulations are not necessarily more fit-for-purpose than models developed on a general population. Both well-specified general models and specialized models may be capable of achieving suitable clinical performance in these subpopulations, and this assessment of model fit-for-purpose must be made on a case-by-case basis.

Model-informed precision dosing (MIPD) is increasingly used to guide drug dosing based on population pharmacokinetic (popPK) models developed mainly using plasma concentration data. However, plasma levels may not always correlate well with drug concentrations at the site of action, potentially leading to under- or overestimation of target-site exposure. It is, therefore, important to evaluate which popPK modeling approaches effectively describe drug concentrations at target sites other than plasma to support the selection and implementation of appropriate modeling techniques. This review outlines four general modeling strategies described in literature characterizing the relationship between plasma and target-site drug concentrations. The first approach includes rate constants describing inflow and outflow, which is especially useful for unidirectional transport or large flow rate differences. Second, intercompartmental clearance models capture bidirectional transport with a single parameter that is directly comparable with elimination clearance or blood flow. Third, effect compartment models are used to describe delayed tissue distribution. Lastly, the target site can be modeled as part of either the central or the peripheral compartment. Although therapeutic drug monitoring (TDM) based on target-site concentrations has been suggested, implementation is limited by invasive sampling procedures, limited sample volumes, and the lack of established pharmacokinetic/pharmacodynamic targets. Nevertheless, even small differences in target-site exposure can result in clinical implications, and the applicability of drug monitoring using target-site concentrations has been shown in critically ill patients. In conclusion, target-site concentrations have been successfully predicted using different modeling methodologies and have demonstrated potential to optimize therapy in select clinical cases.

Background: Mycophenolic acid (MPA) has complex pharmacokinetics in part due to enterohepatic recirculation (EHR). A deeper understanding of MPA pharmacokinetics and specifically how EHR and patterns of EHR affect exposure will improve immunosuppression outcomes. This study provides a contemporary and comprehensive assessment of MPA and metabolites in the blood and urine with a focus on EHR characteristics.

Methods: Kidney transplant recipients (n = 84) receiving mycophenolate mofetil (MMF) and tacrolimus underwent an intensive MPA pharmacokinetic assessment. Pharmacokinetics of MPA and its metabolites, EHR%, and number of secondary peaks were determined. The associations between EHR, the number of secondary peaks, and achievement of MPA therapeutic range were studied. MMF dose proportionality with MPA exposure was evaluated.

Results: MPA exhibited high pharmacokinetic variability, with 5.5-fold differences in AUC_0-12, 18.4-fold differences in trough concentrations, and a 3.4-fold difference in EHR%. Median MPA EHR% was 40.5%. There were no significant associations between EHR% and MPA AUC_0-12 or trough. MPA AUC_0-12 and trough were significantly associated with the number of MPA secondary peaks (0, 1, or ≥ 2 peaks). Participants without secondary peaks showed the highest percentage of subtherapeutic MPA AUC_0-12 and troughs, while participants with ≥ 2 peaks were more likely to be supratherapeutic.

Conclusions: There was no association between the EHR% and MPA AUC_0-12 or trough. We identified three distinct patterns of MPA secondary peaks (0, 1, or ≥ 2 peaks), which were significantly associated with MPA AUC_0-12 and trough. Studies to evaluate the relationship of MPA EHR measures and clinical outcomes are needed.

Trial registry: Clinical Trial Notation: clinicaltrials.gov, NCT04953715.

Obesity is becoming more prevalent, and it significantly alters drug pharmacokinetics (PK), potentially impacting the efficacy and safety of β-lactam antibiotics. Population pharmacokinetic (popPK) modeling plays a crucial role in optimizing dosing strategies, yet there is variability in how these models account for obesity-related physiological changes. This review aims to synthesize published popPK studies on β-lactams in obese populations, highlighting key findings and modeling approaches. A literature review was conducted to identify popPK studies evaluating β-lactam antibiotics in obese individuals. Relevant databases were searched for studies reporting PK parameters, covariates, and dosing recommendations. A total of 24 studies met the predefined selection criteria. The majority employed two-compartment models with creatinine clearance (CL_CR) and total body weight (TBW) as the most common covariates for clearance (CL) and volume of distribution (V_d). Estimated PK parameters differed within the same β-lactam, which indicates that obesity influences the PK of β-lactams, though the extent of its impact differs based on the specific drug, patient characteristics, and selected covariates. This review highlights the key aspects of popPK models in obese populations treated with β-lactams. Despite the growing prevalence of obesity, its influence on β-lactam antibiotics remains uncertain.

Background: GalNAc-conjugated short interfering RNAs (GalNAc-siRNAs) lack a biologically plausible mechanism for QT/QTc prolongation based on their unique physicochemical and absorption, distribution, metabolism, and excretion (ADME) properties. Nevertheless, regulatory agencies require characterization of QTc effects via either a thorough QT (TQT) study or a concentration-QTc (C-QTc) analysis using early-phase data. This manuscript summarizes platform-level experience across eight GalNAc-siRNAs to assess QTc prolongation risk via C-QTc analysis.

Methods: Time-matched electrocardiogram (ECG) data and plasma concentration data collected from phase 1/2 studies (N = 686), involving healthy subjects and/or patients receiving single ascending doses of study drug or placebo, were used for C-QTc analysis for eight GalNAc-siRNAs. For each drug, individual and mean change from baseline in placebo-corrected values of Fridericia corrected QT interval (∆ΔQTcF) were assessed over time. Linear regression analysis evaluated the relationship between plasma concentrations of parent/metabolite and ∆ΔQTcF. A concentration-dependent QTc effect was defined as an increase of ≥ 10 ms in the upper bound of the 90% confidence interval (CI) for predicted ∆∆QTcF at the clinically relevant exposures. As per International Council for Harmonisation (ICH) E14 guidance, categorical analyses of QTcF interval data were also performed.

Results: GalNAc-siRNAs demonstrated similar pharmacokinetics with rapid absorption and short plasma half-lives, with concentrations typically declining to below quantifiable levels within 24-48 h. Across the eight GalNAc-siRNAs, there were no dose-dependent increases in ∆∆QTcF over time. No subject had a QTcF interval > 500 ms or a change from baseline > 60 ms. Slopes of the linear regression of concentration versus ∆∆QTcF were close to zero, and the upper bound of 90% CI for ∆∆QTcF values at mean C_max values of the highest doses tested was well below 10 ms.

Conclusions: No concentration-dependent increase in QTcF was observed with multiple GalNAc-siRNA molecules across diverse targets and indications, confirming that GalNAc-siRNAs are highly unlikely to pose a QT prolongation risk. Hence, dedicated TQT studies are not necessary for the molecules in this platform and assessment of C-QTc relationships from early clinical studies up to maximum feasible clinical doses, which might be less than twofold of the likely therapeutic dose, is sufficient to assess the QT liability.

Clinical trial registration nos: NCT02797847, NCT04565717, NCT02706886, NCT05256810, NCT03338816, NCT03934307, NCT05661916, and NCT05761301.

Background and objective: Targeted liposomal doxorubicin (TLD-1) is a novel PEGylated liposomal doxorubicin (PLD) with optimized formulation characteristics, developed to improve the benefit-risk profile of PLD. This randomized intrapatient crossover amendment to the phase 1 SAKK 65/16 trial (NCT03387917) compared the pharmacokinetics (PK) of TLD-1 and Caelyx™ and included a pooled analysis of safety and preliminary antitumor activity at the recommended phase 2 dose (RP2D).

Methods: Patients with advanced breast or platinum-resistant ovarian cancer in the comparative PK part were randomized to receive TLD-1 in cycle 1 and Caelyx™ in cycle 2, or vice versa, followed by TLD-1 thereafter. Both formulations were administered intravenously at 40 mg/m² and PK was assessed using non-compartmental analysis. Safety and antitumor activity were analyzed across 23 patients treated with TLD-1 at the RP2D, including 13 from the comparative PK part and 10 from the published dose-escalation part.

Results: In 10 evaluable patients from the comparative PK part, TLD-1 showed higher encapsulated doxorubicin exposure (AUC_0-inf: 3222 vs 2139 mg·h/L) and longer median half-life (118 vs 70 h) than Caelyx™. Severe treatment-related events occurred in 43% of patients (10/23) in the full RP2D cohort, most commonly grade 3 palmar-plantar erythrodysesthesia, oral mucositis, and anemia (2 patients each). The investigator-assessed objective response rate was 8.7% (2/23), with partial responses in patients with breast cancer.

Conclusions: Targeted liposomal doxorubicin demonstrated prolonged systemic circulation and low variability in liposomal drug release, likely due to its formulation characteristics. At 40 mg/m² every 3 weeks, TLD-1 was well tolerated and showed modest preliminary antitumor activity in advanced breast cancer.

Clinicaltrials:

Gov identifier: NCT03387917, registered 2017-11-21.

Background and objective: Population pharmacokinetic (popPK) models in pediatric patients are essential to optimize dosing and ensure therapeutic efficacy. However, study designs are often not fully optimized, leaving room to improve efficiency, which is an important goal in this population, where patients are limited and resources scarce. The aim of the present work is to optimize study designs for the development of popPK models for teicoplanin, piperacillin and meropenem in pediatric patients, with or without continuous kidney replacement therapy (CKRT), to achieve greater model precision while reducing patient burden and economic cost.

Methods: Methodology based on the optimization of the Fisher information matrix (FIM) was followed, using the $DESIGN option in NONMEM 7.5. A previously developed model was selected for each of the antibiotics. The number of subjects in the optimized designs was fixed to 28 patients (14 with and 14 without CKRT). It was assumed that only plasma samples were extracted from patients without CKRT, while prefilter, postfilter, and effluent samples could be extracted simultaneously from patients undergoing CKRT. Sensitivity to different proportions of patients with and without CKRT was tested. The optimized designs were evaluated through simulation and re-estimation procedures, including the impact of covariates.

Results: The number of sampling times per individual needed to achieve precise parameter estimates was 3 in teicoplanin, 4 in piperacillin, and 6 in meropenem. The optimized designs reduced the total number of samples per patient by 25, 51, and 21% for teicoplanin, piperacillin, and meropenem, respectively, compared with the original studies used in the previous studies. The resulting samples were taken during 0-40 h from the beginning of the study in teicoplanin and piperacillin, while in the case of meropenem optimal sampling times went between 0-64 h. The optimized designs remained robust under different proportions of patients with and without CKRT and under different covariate values.

Conclusions: This work emphasizes the importance of optimizing study designs to improve accuracy and precision in the model parameters while reducing the number of samples needed. This is a relevant advantage especially when dealing with critically ill pediatric patients.