J Pharmacokinet Pharmacodyn [JOURNAL]
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200 papers
Model informed assessment of QT prolongation during drug development: a five-year retrospective analysis of EMA scientific advices.
Djokoto HP, Dogné JM, Musuamba FT
J Pharmacokinet Pharmacodyn · 2026 Mar · PMID 41772263 · Publisher ↗
Regulatory evaluation of QT interval prolongation remains central to cardiac safety assessment in drug development. Since the 2015 revision of the ICH E14 Q&A, concentration-QT (C-QT) modelling has been formally recogniz...
Regulatory evaluation of QT interval prolongation remains central to cardiac safety assessment in drug development. Since the 2015 revision of the ICH E14 Q&A, concentration-QT (C-QT) modelling has been formally recognized as an acceptable alternative to dedicated Thorough QT (TQT) studies, offering ethical and practical advantages. This study aimed to characterize how the European Medicines Agency (EMA) has assessed C-QT modelling approaches over the past five years, with particular focus on regulatory acceptance of TQT waiver requests and the recurring drivers of rejection. A retrospective review was performed of EMA Scientific Advice (SA) documents issued between January 2020 and January 2025. Using the internal text-mining platform Scientific Explorer, 524 SA cases (4,196 applicant questions) were screened for "QT" or "QTc." A custom Python tool extracted relevant discussions, which were subsequently categorized by expert review. Regulatory feedbacks were classified as supportive, conditionally supportive, or unsupportive. Among 110 QT-related requests, 81% sought TQT waivers, most justified by C-QT modelling. Of these, 70% were supported, 9% conditionally endorsed, and 21% rejected. Common rejection drivers included insufficient exposure margins (n = 8), study design limitations (n = 5), data gaps (n = 7), unclear methodological reporting (n = 5), QTc interpretation concerns (n = 5), and additional methodological weaknesses (n = 4). In nine supportive cases, safety margins were endorsed in principle but lacked detailed documentation. C-QT modelling is widely accepted by EMA when adequately supported. However, gaps in exposure justification and reporting continue to challenge regulatory confidence, emphasizing the need for standardized practices in QT risk assessment.
Uncertainty undermines the validity of antimicrobial pharmacodynamics.
Woodward AP
J Pharmacokinet Pharmacodyn · 2026 Mar · PMID 41772181 · Full text
Antimicrobial therapy is informed by quantitative models of drug disposition and action. These models utilize experimental and observational evidence, subject to uncertainties, to support drug selection and dosage regime...
Antimicrobial therapy is informed by quantitative models of drug disposition and action. These models utilize experimental and observational evidence, subject to uncertainties, to support drug selection and dosage regimen optimization, and interpret antimicrobial resistance data. The framework includes multiple components, which characterize mechanisms contributing to therapeutic outcome. The components must be combined in a logical sequence to generate predictions, so propagation of uncertainty is a critical consideration. Quantitative evaluation of this uncertainty has received apparently little attention. This essay argues for the importance of uncertainty quantification in antimicrobial pharmacology. The impact of parameter uncertainties and measurement errors on the validity of pharmacokinetic-pharmacodynamic modelling of antimicrobials is described. Major components of the modelling workflow are assessed, and uncertainties characterized. The influence of major design and statistical analysis decisions at each step is emphasized. Finally, using detailed simulations, the impact of these sources of uncertainty on outcomes including clinical breakpoints and dose individualization is illustrated. Measurement of antimicrobial potency as the minimum inhibitory concentration contributes approximately twofold error, which is important for individual dose determination. Interpretation of PK/PD parameters is generally conducted dichotomously as thresholds, which are empirically determined, and subject to error. Parameter uncertainties in the exposure-response relationship are potentially substantial, and contribute apparently major uncertainty to predictions at both population and individual levels. The importance of uncertainty in pharmacokinetics appears context-sensitive. Applications including dose optimization or susceptibility breakpoints appear overly confident, and point estimation from these models may be an unreliable basis for decision making. These observations highlight the importance of uncertainty quantification for rigorous antimicrobial pharmacology.
A physics-informed neural network approach for estimating population-level pharmacokinetic parameters from aggregated concentration data.
Tsiros P, Minadakis V, Sarimveis H
J Pharmacokinet Pharmacodyn · 2026 Feb · PMID 41699348 · Full text
The pharmacokinetic literature is rich in aggregated concentration data that contain valuable information, yet tools to extract this information remain limited. This work introduces distributional physics-informed neural...
The pharmacokinetic literature is rich in aggregated concentration data that contain valuable information, yet tools to extract this information remain limited. This work introduces distributional physics-informed neural networks (D-PINNs), a novel algorithm designed to enable statistical modelling within the PINN framework, allowing recovery of pharmacokinetic parameter distributions at the population level from published concentration means and variances. Unlike traditional PINNs, which often focus on point estimates, D-PINNs incorporate distributional assumptions directly into the optimisation process. The framework utilises neural networks for predicting the mean and variance of the concentration over time. These predictions are then incorporated into a sampling-based procedure within the residual network, which uses the governing ordinary differential equation (ODE) system to compute the physics-informed loss term. The methodology accounts for both interindividual variability through the parameter distribution and measurement noise through a residual error model. The capability of D-PINNs to infer population-level parameter distributions from concentration summary statistics was demonstrated through a simple proof-of-concept using simulated data from a one-compartment pharmacokinetic model of intravenous drug administration. The model achieved high accuracy in estimating both the parameter distribution and the residual error. Hyperparameter tuning highlighted important aspects of model development. The modelling framework was then applied to real-world data to demonstrate its ability to recover information on the distribution of kinetic parameters in the studied population. Specifically, a minimal physiologically-based pharmacokinetic (mPBPK) model for monoclonal antibodies (mAbs) was fitted to aggregated plasma concentration data reported in the literature using D-PINNs. The same aggregated data were also analysed using a Markov chain Monte Carlo (MCMC) analogue to benchmark the proposed methodology.
A mechanistic pharmacokinetic-pharmacodynamic model for degrader-antibody conjugates.
Balthasar MP, Bartlett DW
J Pharmacokinet Pharmacodyn · 2026 Feb · PMID 41629705 · Publisher ↗
Degrader-antibody conjugates (DACs) represent an emerging class of innovative therapeutics that combine the unique delivery capabilities of antibody-drug conjugates (ADCs) with the catalytic targeted protein degradation...
Degrader-antibody conjugates (DACs) represent an emerging class of innovative therapeutics that combine the unique delivery capabilities of antibody-drug conjugates (ADCs) with the catalytic targeted protein degradation capabilities of proteolysis-targeting chimeras (PROTACs). To support the rational design and optimization of this new therapeutic modality, a pharmacokinetic-pharmacodynamic (PK-PD) model framework for DACs was developed to integrate the mechanisms of targeted protein degradation and antibody-mediated drug delivery. The model was calibrated with previously published in vitro and in vivo data for DACs and their PROTAC payloads. The PK module for antibody-mediated PROTAC delivery was linked to a PD module capturing ternary complex formation and protein degradation by the released PROTAC. Downstream pharmacological activity, such as inhibition of cell proliferation for oncology applications, was driven by the changes in target protein levels. The model framework captured the PK and PD of unconjugated and antibody-conjugated PROTACs, linking intracellular PROTAC concentration to protein degradation, in vitro cell proliferation, and in vivo tumor growth inhibition. Model simulations enabled exploration of parameters influencing DAC efficacy, including expression level and internalization efficiency of the surface antigen for antibody targeting, the composition and stability of the antibody-PROTAC linker, and PROTAC payload potency and permeability. The model framework establishes design constraints for antibody, linker, and payload selection and optimization, providing practical insights that can improve the probability of success for novel DAC therapeutics.
Simulation-based assessment of the P-glycoprotein expression-activity relationship shows a drug and system dependency.
van Valkengoed DW, Rottschäfer V, de Lange ECM
J Pharmacokinet Pharmacodyn · 2026 Feb · PMID 41629699 · Full text
In vitro-in vivo extrapolation (IVIVE) of P-glycoprotein (P-gp) transporter activity bears two important assumptions: (1) P-gp expression (i.e., concentration) is linearly related to P-gp activity; and (2) this relation...
In vitro-in vivo extrapolation (IVIVE) of P-glycoprotein (P-gp) transporter activity bears two important assumptions: (1) P-gp expression (i.e., concentration) is linearly related to P-gp activity; and (2) this relation is drug independent. However, conflicting experimental results have been obtained about this relationship. This study therefore aimed to theoretically explore the P-gp expression-activity relationship (EAR). A P-gp membrane kinetic binding model was used to explore the P-gp EAR, and how this is impacted by different drugs as well as drug dose and initial P-gp expression. The model included passive permeability and drug interaction with P-gp described through the association (kon), dissociation (koff), and efflux rate (ke) constants. Simulations were first performed for 7 P-gp substrates, assuming an initial P-gp expression of 1000 µM and drug concentrations of 1 µM. Subsequently, the P-gp concentration was varied (2-300%) to derive the EAR. Next, to determine the impact of drug-specific and system-dependent properties on the P-gp EAR, virtual drugs varying in koff and ke were simulated, simultaneously considering different initial P-gp expressions and drug concentrations. Our simulations show that the P-gp EAR is not always linear, with the EAR showing both linear and non-linear behaviour depending on the drug. The koff/ke ratio of a drug was found to be an important determinant of the P-gp EAR, which shifted towards non-linearity for lower koff/ke ratios. Additionally, the P-gp EAR was more likely to be non-linear for higher ratios of the initial P-gp expression to drug concentration. In conclusion, this study shows that P-gp expression is not always proportional to P-gp activity, thereby explaining experimental contradictions about the EAR. This implies that using P-gp expression as single biomarker for P-gp activity needs to be reconsidered.
Correction to: Catalyzing change in MID3 through globalization, education, and innovation.
Chung DW, Ait-Oudhia S
J Pharmacokinet Pharmacodyn · 2026 Jan · PMID 41543588 · Publisher ↗
Abstract loading — click title to view on PubMed.
Concentration response analyses for QT data with several active compounds.
Heimann G, Lestini G, Zisowsky J
J Pharmacokinet Pharmacodyn · 2026 Jan · PMID 41526765 · Publisher ↗
PK-QTc analyses are routinely done as part of most drug development programs. Usually, the PK concentration of a single compound is related to the QTc effect. However, in many instances there are several active compounds...
PK-QTc analyses are routinely done as part of most drug development programs. Usually, the PK concentration of a single compound is related to the QTc effect. However, in many instances there are several active compounds, for example a parent drug and its metabolite, or combination drugs. Previous authors have shown that doing separate PK-QTc analyses for each of the potentially active compounds may lead to biased results, and recommended to do joint modeling of the impact of both compounds on the corrected QT interval. In this paper we go one step further and propose a formal hypothesis test to exclude a [Formula: see text]msec effect based on a joint modeling approach when there are potentially two active compounds. In analogy to the situation with just one active compound, where the upper limit of a [Formula: see text]% confidence interval for [Formula: see text] (with [Formula: see text] being the slope of a linear exposure-response relationship and [Formula: see text] being the expected maximum concentration of some supra-therapeutic dose) needs to be below [Formula: see text]msec, we use the upper confidence intervals for [Formula: see text], [Formula: see text], and [Formula: see text] and exclude a [Formula: see text]msec effect if all three upper confidence limits are below the [Formula: see text]msec threshold. We propose a bootstrap approach for decision making, and show via simulations that this approach controls the type I error of [Formula: see text]%. We focus on the situation where exposure-response is linear in both compounds, but also indicate how this can be extended to non-linear situations.
Risks encountered when not adjusting for diurnal variation and food effect in QTcF analysis based on phase I data.
Bardol M, Henrich A, Sarr C … +2 more , Mezzalana E, Langenhorst J
J Pharmacokinet Pharmacodyn · 2026 Jan · PMID 41493500 · Full text
Phase I single and multiple ascending dose studies are more and more often used to evaluate QT liability of new drugs. However, these studies are not primarily tailored to concentration-QT analysis and to control or docu...
Phase I single and multiple ascending dose studies are more and more often used to evaluate QT liability of new drugs. However, these studies are not primarily tailored to concentration-QT analysis and to control or document influential factors such as meal intake. In addition, sampling times may vary over the day for operational reasons. This simulation analysis evaluates the reliability of the standard pre-specified linear model (PLM) proposed by a publication of Garnett et al. and an adjusted PLM accounting for food effect and clock time. The QTcF-time profile of a drug with a mild QT-liability (upper bound of the 90% confidence interval close to the 10 ms threshold) resulting from a well-controlled study was simulated 1000 times and evaluated with the unadjusted PLM (Scenario A, negative rate: 20.8%). Compared to suboptimal study designs with uncontrolled and unbalanced (i.e., differences between active treatment and placebo) differences in meal intake and dosing/sampling times, the unadjusted PLM led to an inflated negative rate (≤ 50%), while the adjusted PLM was able to correct for the imbalances resulting in similar negative rates as the reference scenario or lower, i.e., being more conservative. In conclusion, good documentation in Phase I trials and adjusting for known influential factors can help to analyze QT effects reliably and waive with relevance QT/QTc studies.
Correction to: An automated pipeline to generate initial estimates for population Pharmacokinetic base models.
Huang Z, Fidler M, Lan M … +3 more , Cheng IL, Kloprogge F, Standing JF
J Pharmacokinet Pharmacodyn · 2026 Jan · PMID 41484766 · Full text
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Catalyzing change in MID3 through globalization, education, and innovation.
Chung DW, Ait-Oudhia S
J Pharmacokinet Pharmacodyn · 2025 Dec · PMID 41402539 · Publisher ↗
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. Realiz...
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.
Aggregate data modelling: A fast implementation for fitting pharmacometrics models to summary-level data in R.
van de Beek H, Välitalo PAJ, van Hasselt JGC … +1 more , Zwep LB
J Pharmacokinet Pharmacodyn · 2025 Dec · PMID 41366592 · Publisher ↗
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 jointl...
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.
Identification and characterization of virtual sub-populations through phenotype-guided filtering. The challenging case of nonidentifiable models in the context of therapeutic evaluation.
Zugaj D, Nekka F
J Pharmacokinet Pharmacodyn · 2025 Dec · PMID 41361061 · Publisher ↗
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...
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.
On the coupling between a basic FcRn mechanism and target-mediated disposition of antibodies.
Kátai CB, Berns MMM, Elassaiss-Schaap J
J Pharmacokinet Pharmacodyn · 2025 Nov · PMID 41307781 · Publisher ↗
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...
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.
A note on phase I interleaved versus parallel group ascending dose designs for concentration-QTc analyses.
Heimann G, Dumortier T, Meiser K
J Pharmacokinet Pharmacodyn · 2025 Nov · PMID 41240243 · Publisher ↗
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 prec...
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.
QT interval prolongation: clinical assessment, risk factors and quantitative pharmacological considerations.
Gotta V, Donner B
J Pharmacokinet Pharmacodyn · 2025 Nov · PMID 41204044 · Full text
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 li...
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.
An automated pipeline to generate initial estimates for population Pharmacokinetic base models.
Huang Z, Fidler M, Lan M … +3 more , Cheng IL, Kloprogge F, Standing JF
J Pharmacokinet Pharmacodyn · 2025 Nov · PMID 41199105 · Full text
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 esti...
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.
Characterization of CAR-T cellular kinetics and efficacy in solid tumor patients with and without prior lymphodepletion chemotherapy using a PBPK-PD model.
Parmar KR, Dey A, Wang AF … +2 more , Mugundu GM, Singh AP
J Pharmacokinet Pharmacodyn · 2025 Oct · PMID 41145896 · Publisher ↗
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...
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.
Pharmacometric modeling with the zero-order hold.
Haseltine EL, Rodriguez-Romero V
J Pharmacokinet Pharmacodyn · 2025 Oct · PMID 41125956 · Publisher ↗
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 an...
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.
A QSP PDE model of ADC transport and kinetics in a growing or shrinking tumor.
Ross DS, Cabal A
J Pharmacokinet Pharmacodyn · 2025 Oct · PMID 41110006 · Publisher ↗
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 proliferat...
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.
Scoping review of the role of pharmacometrics in model-informed drug development.
Bhat AG, Shin E, Roy A … +1 more , Ramanathan M
J Pharmacokinet Pharmacodyn · 2025 Oct · PMID 41091236 · Full text
Model-informed drug development (MIDD) is a framework that utilizes quantitative modeling and simulation methods to integrate nonclinical and clinical data, as well as prior knowledge, to inform drug development and regu...
Model-informed drug development (MIDD) is a framework that utilizes quantitative modeling and simulation methods to integrate nonclinical and clinical data, as well as prior knowledge, to inform drug development and regulatory decision-making. This scoping review examines the current MIDD landscape with a focus on pharmacometrics. It will clarify essential MIDD concepts and provide a taxonomy of terms. Additionally, it will evaluate the implications of the International Council for Harmonisation (ICH) M15 MIDD draft guidelines and summarize case studies that illustrate the use of MIDD in various drug development and lifecycle management scenarios. The diverse case studies span various therapeutic areas, small molecules, monoclonal antibodies, and long-acting injectable dosage forms. The ICH M15 MIDD guidelines aim to align the expectations of regulators and sponsors, support consistent regulatory decisions, and minimize errors in the acceptance of modeling and simulation. MIDD is a transformative and rigorous process that fosters collaboration between industry and regulatory agencies during drug development.MIDD has enabled accelerated approvals of drugs for pediatric conditions and rare diseases, where it can be difficult to recruit enough patients for efficacy studies. MIDD has contributed to the clinical pharmacology strategies that have successfully allowed dose extrapolation to related disease indications, dosage forms, and clinical populations without additional clinical trials.
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