Journal of Pharmacokinetics and Pharmacodynamics最新文献

The landscape of pharmaceutical research and drug development is undergoing a significant evolution, with Model-Informed Drug Discovery and Development (MID3) as a transformative approach to accelerate innovation. Realizing the full potential of MID3 required a concerted global effort to enhance education, foster collaboration, and drive scientific advancement. In this issue, we propose that true progress and equitable outcomes hinge on embracing a multifaceted approach, encompassing not only the inclusion of data from diverse patient populations, such as pediatric and pregnant individuals, but also fostering an inclusive environment for a globally diverse group of scientists. We highlight the critical role of globalization in expanding pharmacometrics collaborations across national boundaries and cultural contexts, recognizing that varied perspectives and expertise drive richer insights. Furthermore, we emphasize the importance of equitable access to education and training, particularly for non-native English-speaking institutions, in cultivating a truly global talent pool. Finally, we demonstrate how this expanded diversity fuels innovation, encouraging the adoption of a broader spectrum of quantitative approaches-from classical PK/PD to Physiologically Based Pharmacokinetics (PBPK), Quantitative Systems Pharmacology and Toxicology (QSP/T), and artificial intelligence driven modeling, thereby addressing complex biological challenges and ultimately achieving the "right dose for the right patient at the right time." This editorial emphasizes that by intentionally integrating globalization, education, and innovation, the pharmacometrics community can catalyze profound change in MID3, leading to more effective and inclusive medicines for all.

Pharmacometric modelling is traditionally performed using individual level data. Recently a new method was developed to fit pharmacometric models to summary level - or aggregate - data. This methodology allows for jointly modelling different data sources, once transformed into aggregate data. As such, the method can be applied to a combination of individual data, pharmacometric models, and aggregate data. In this study we aimed to (1) implement this methodological framework into an accessible R package (admr) and (2) develop a novel algorithm with enhanced computational efficiency. The developed R-package allows calculating aggregate data from different data sources, jointly fitting one or multiple data sources and assessing model performance. The implementation of the newly developed algorithm improves computational efficiency by iteratively reweighting internal Monte Carlo predictions. Three simulation scenarios using different data generating models demonstrated an improvement of 3 to 100-fold speed-up when using the novel Iterative Reweighting Monte Carlo (IR-MC) algorithm, while maintaining the convergence properties of the original MC algorithm. These analyses demonstrated that estimation with the IR-MC algorithm is increasingly more efficient as model complexity rises as compared to the standard MC algorithm, indicating the utility for more complex pharmacometric models. In conclusion, the aggregate data modelling implementation in the admr R package allows for a fast and user-friendly application of the aggregate data modelling framework.

The usefulness of mathematical modeling of biological systems and their responses to exogenous products is now well recognized. However, this recognition is marred by problems of unreliability of representations of real populations and predictions of responses to treatments. To remedy this, the generation of virtual populations combined with quantitative systems pharmacology models is increasingly being adopted. However, the complexity of these models and the large number of parameters they involve, generally within a context of lack of information or data, raise the question of nonidentifiability as a potential source affecting the quality of model predictions. This article attempts to present a vision that confronts the management of nonidentifiability with the concerns linked to the classification of virtual populations and their corresponding parametric signatures, as a potential tool for the evaluation of therapeutic interventions.

Understanding the pharmacokinetics of therapeutic antibodies often requires a detailed investigation of the mechanisms governing their distribution and clearance. Two of the most important mechanisms are the salvage and recycling of antibodies by the neonatal Fc receptor (FcRn), and target-mediated drug disposition (TMDD). While the two mechanisms have been analysed individually in detail, their combination and coupling is yet to be addressed. An important point of consideration is the characteristic time scales pertaining to the processes in each mechanism and how they can be related and thus integrated into a single framework. To this end a minimal 'physiology-based' pharmacokinetic model incorporating specific (TMDD) and non-specific (FcRn) antibody elimination is investigated in the high binding-affinity limit using the method of matched asymptotic expansions. The theory builds on previous asymptotic frameworks corresponding to each mechanism individually. The combined FcRn-TMDD model consists of a plasma space and an endosomal space, with target binding occurring in the former and antibody salvage in the latter. Two parameter regimes are studied in particular, that correspond to cases wherein both the specific and the non-specific clearance mechanisms provide comparable contributions to the total antibody clearance over the same time scale. The analysis offers insight into the processes dominating antibody pharmacokinetics during each characteristic phase of the problem. In addition to the accurate analytical description of the kinetics, relevant pharmacometric expressions are also derived, such as the approximate time and concentration when the target receptors are no longer 'fully' saturated, AUC and the terminal slope. The resulting insight on the dominant processes and model parameters in the specific characteristic phases may be utilised to guide parameter estimation in future modelling efforts. Additionally, the presented theory can be used to assess the validity of various quasi-equilibrium, quasi-steady and Michaelis-Menten type assumptions in each phase. In short, the presented theory can provide guidance for physiology-based pharmacokinetic as well as standard pharmacokinetic modelling efforts.

PK-QTc analyses are an integral part of drug development programs. These analyses are often based on phase I study data, and the question may be asked whether the design of these phase I studies has an impact on the precision of the corresponding PK-QT analysis. More precisely, we are interested whether one can increase the power of such analyses when using interleaved ascending dose designs rather than parallel group ascending dose designs. Based on a simulation study, previous authors have concluded that this is the case. Their conclusions, however, are based on assumptions regarding the magnitude of the random effect variances, and on a very specific set-up of their simulation study. In this paper we provide a study re-analysis of historical QTc data. The resulting estimates of these random effect variances are much smaller than those used by the previous authors. We also propose a simulation set-up that adequately mimics the data generation process and the correlation between the primary endpoint change from baseline and the covariate baseline. We present a simulation study using the revised simulation set-up and random effect variances as observed in our study re-analysis. We did not find major differences in power between the different designs when the number of observations is the same. We also provide a justification based on causal analysis why we think our simulation set-up is more adequate for situations when change from baseline is the primary endpoint, specifically when baseline is used as a covariate.

Prolongation of the QT interval in the ECG is a critical finding that signifies an extended duration from the onset of ventricular depolarization to the end of ventricular repolarization. It can predispose patients to life-threatening arrhythmias, such as Torsades de Pointes (TdP). Long QT syndromes (LQTS) are defined by mutations in ion channel genes, particularly those encoding cardiac potassium and sodium channels and are characterized by a significant risk for sudden cardiac death if untreated. However, besides these clearly defined entities various medications have been implicated in causing QT interval prolongation. There is increasing evidence for a genetically determined risk for drug-induced QT prolongation. In addition, due to numerous clinical factors influencing the QT interval, QT prolongation increases the risk of TdP particularly in multi-morbid patients necessitating vigilant monitoring in at-risk populations. This review gives an overview of mechanisms and conditions which induce QT prolongation, the clinical assessment of QT interval duration, thereby highlighting quantitative variations in measurement techniques and heart-rate correction, as well as in demographic interpretation of normal values. The risk of cardiac arrhythmia is discussed, in both patients with congenital LQTS and acquired QT prolongation, along with influencing pharmacokinetic/pharmacodynamic, non-pharmacologic and genetic risk factors for TdP. Finally, clinical implications for individual patient management, including risk-adapted drug-prescription and use of ECG monitoring to mitigate the risks associated with QT prolongation, are summarized. Understanding the interplay between pharmacokinetics, pharmacodynamics, genetic predisposition and co-morbidities is essential for optimizing treatment in the context of prolonged QT intervals, preventing adverse cardiovascular events, and improving cardiac safety. Comprehensive drug labelling regarding exposure-QT relationships and available pharmacovigilance data are important sources of information enhancing patient safety.

Nonlinear mixed-effects models rely on adequate initial parameter estimates for efficient parameter optimization. Poor initial estimates can result in failed model convergence or termination with incorrect parameter estimates. Non-compartmental analysis (NCA) and other manual methods have typically been used to derive initial estimates for pharmacokinetic (PK) parameters. However, NCA struggles with sparse data and recent advances in automated modeling increasingly emphasize the need for initial estimates that require no user input. This study aimed to develop an integrated pipeline for the computation of initial estimates applicable to various data types and model structures. The designed pipeline incorporated a custom-designed algorithm that leveraged data-driven methods to generate initial estimates for both structural and statistical parameters in population pharmacokinetic (PopPK) base models. The pipeline's performance was evaluated across twenty-one simulated datasets and thirteen real-life datasets. The results suggested that this pipeline performed well in all test cases. Initial estimates recommended by the pipeline resulted in final parameter estimates closely aligned with pre-set true values in simulated datasets or with literature references in the case of real-life data. This study provides an efficient and reliable tool for delivering PK initial estimates for population pharmacokinetic modeling in both rich and sparse data scenarios. An open-source R package has been created.

Despite tremendous clinical responses and patient benefit in hematological malignancies, chimeric Antigen Receptor (CAR) T cells have demonstrated limited success in solid tumors. Herein, we have scaled and augmented our previously described murine PBPK-PD model (Singh et al. mAbs, 2020) to characterize cellular kinetics (CK) and anti-tumor activity in patients with solid tumor malignancies. The model was able to integrate (1) differential kinetics of effector- and memory-phenotypes in peripheral blood (PB), solid tumors and other pertinent tissues (n = 8), (2) host-immune system dynamics with or without prior lymphodepletion chemotherapy (LDC) and its impact of CAR-T cell kinetics and (3) antigenic heterogeneity in patients. Model was developed based on digitized individual level CK, categorical antitumor activity and percentage tumor antigen expression dataset from following phase-1 dose-escalation studies: (A) anti-mesothelin CAR-T in multiple cancer indications (n = 15, cohorts w/ and w/o LDC), (B) gavocabtagene autoleucel (n = 7, w/ and w/o LDC) in multiple indications, (C) anti-glypican 3 CAR-T in advanced hepatocellular carcinoma (n = 13, dose-range 0.7-5.18 billion) and (D) anti-PSMA/TGFβ CAR-T in prostate cancer (n = 10, w/ and w/o LDC). The developed clinical PBPK-PD model was able to simultaneously characterize the CK and categorical anti-tumor longitudinal dataset(s) for each case study while accounting for antigen-expressing tumor burden in each patient. Moreover, model accounted for host-T cell population dynamics post LDC, which competed with CAR-T cell towards overall expansion and persistence post-treatment. Using model simulation, CAR-T cell expansion was found to be dependent on initial tumor burden and antigen positive tumor fraction. The developed PBPK-PD model could be leveraged as an effective tool in future to provide mechanistic understanding on CK-PD behavior of cell therapies targeting solid tumors.

Solving models comprised of nonlinear differential equations (DEs) in NONMEM using ADVAN6 or ADVAN13 typically requires substantially longer run times than models comprised of linear DEs, which in some cases allow for analytical solutions. Often the need to use nonlinear DE solvers results from pharmacokinetic (PK) variations over the dosing interval introducing the nonlinearity via a nonlinear transfer function, as is the case for indirect-response models and enzyme induction models. As long run times hinder model development, it is desirable to derive suitable approximations to speed up model solutions. The zero-order hold, a concept used in the field of advanced process control to optimize control decisions, provides an attractive approximation for these situations that often results in a sequential system of simpler DEs that in some cases can be solved analytically. Two examples, an indirect-response model and an enzyme induction model, demonstrate that the zero-order hold approximation provides a substantial reduction in computational time (up to ~ 140-fold) without unduly biasing the parameter estimates. These examples demonstrate that the zero-order hold approximation offers an attractive method for efficiently solving models where time-varying PK leads to a nonlinear system of DEs.

When a tumor is treated with an antibody-drug conjugate (ADC) complex biochemistry occurs in a domain-the tumor-whose size and structure are changing. Some parts of the tumor may be growing because tumor cells proliferate. Other parts may be stagnant, or nearly so, because the cells there have been damaged by the cytotoxin. Still others may be shrinking because the cells there have been killed by the cytotoxin and are being cleared. Chemical concentrations within the tumor, which influence kinetics and transport, change as the tumor grows or shrinks. Cell surface antigen, to which ADCs are designed to bind, is lost when cells are cleared and is freshly introduced when cells proliferate. For these reasons, and because shrinking the tumor by killing its cells is the purpose of ADC treatment, it is important in a quantitative systems pharmacology (QSP) approach to the problem to model the evolution of tumor size and structure over the course of ADC treatment. In this paper we present a partial differential equation (PDE) model of ADC transport and kinetics in a growing and shrinking Krogh cylinder tumor. We present results of several studies we performed with the model, including an antigen concentration study that shows tumor growth inhibition to be non-monotone as a function of antigen concentration, and a study of the effects of co-administration of mAb and ADC that shows that the greater the delay between mAb and ADC administration the less the effect of co-administration, and which suggests the mechanism for this effect.