Clinical Pharmacokinetics最新文献

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: 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: 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.

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.

Objectives: To develop and validate a physiologically based pharmacokinetic (PBPK) population model for the Chinese obese population.

Methods: A Chinese adult population database was established in PK-Sim through recalibration of the East Asian population database, using newly collected anatomical and physiological data from Chinese adults. All three drugs (dexmedetomidine, omeprazole, and propofol) possessing Chinese obese population PK data were selected, with their PBPK models then developed and validated using matched clinical data. These models with the fixed drug-specific parameters were applied to the Chinese adult database to simulate drug concentrations, with results compared with the built-in East Asian database. Then, physiological parameters were adjusted using real-world and literature data from Chinese patients with obesity to establish a Chinese obese adult database. Similarly, drug concentrations in this population were simulated and compared with the simulation results based on the published White obese population database.

Results: The predicted C_max and AUC_last values were within 0.5-2 fold of the observed values, demonstrating all drug models were validated. The Chinese adult database showed superior accuracy to the East Asian database (90% versus 75% of AUC_last and 80% versus 70% of C_max within 0.8-1.25 fold). Similarly, the Chinese obese database outperformed the White obese database (83% versus 33% for AUC_last and 33% versus 0% for C_max within 0.8-1.25 fold).

Conclusions: The validated drug models, combined with the Chinese adult and obese adult databases, reliably predicted drug concentrations in Chinese adults and adults with obesity, outperforming the East Asian population database and White obese population database.

Background: Therapeutic drug monitoring (TDM) is a tool used for dose optimization to achieve therapeutic concentrations associated with improved outcomes. However, evidence supporting its benefits for tuberculosis (TB) treatment remains limited. This scoping review evaluated clinical studies on TDM and its impact on TB treatment outcomes.

Methods: A scoping review was performed using a systematic search in PubMed, Embase, Web of Science, ClinicalTrials.gov, and the World Health Organization (WHO) Clinical Trials Registry for interventional and observational studies published until 3 May 2025. We included studies evaluating TDM in adults or children treated for drug-susceptible or drug-resistant TB at any setting worldwide, which reported treatment outcomes, adverse events, or clinical/microbiological surrogate markers. The PRISMA guidelines for scoping reviews were followed to report the findings.

Results: Of the 5820 studies screened by title and abstract, 31 studies from 10 countries were eligible for inclusion in this review. No published clinical trials on the implementation of TDM were identified, although two are currently ongoing. Overall, compared with the non-TDM group, TDM was associated with faster culture conversion (mean 34 versus 49 days), shorter treatment duration (mean 32 versus 36 weeks) and fewer adverse events. Although all included studies reported high treatment success rates (ranging from 67% to 100%), no statistically significant differences were observed in end-of-treatment outcomes between TDM and non-TDM groups. Dose adjustments guided by TDM were recommended by all included studies, despite variability in results.

Conclusions: Observational data suggest that TDM in TB treatment was associated with improved effectiveness and fewer adverse events. However, further investigation through well-controlled studies is needed to minimize potential bias and justify its routine use.