CPT: Pharmacometrics & Systems Pharmacology最新文献

Cachexia is a metabolic condition that accelerates the clearance of monoclonal antibodies in cancer patients and is a known mechanism causing time-dependent clearance. Successful anticancer treatment often ameliorates symptoms of cachexia, reducing the drug clearance over time especially in patients who respond. Serum albumin level is a common biomarker of cachexia that is frequently associated with antibody drug clearance. Physiologically based pharmacokinetic (PBPK) models of antibody drugs have incorporated albumin metabolism but have not been applied to describe time-varying clearance due to improvement in cachexia. The objective of this analysis was to evaluate albumin levels as a biomarker that is predictive of changes in antibody clearance due to cachexia. A PBPK model that jointly describes metabolism of albumin and biologic drugs was fitted to longitudinal albumin data from cancer patients treated with durvalumab and was used to predict changes in durvalumab clearance over time. PBPK model predictions were compared to empirical population pharmacokinetic (PK) models of durvalumab and other checkpoint inhibitors fitted directly to clinical PK. The model fitted the observed albumin data in cancer patients closely, and the three fitted parameters showed low uncertainty (RSE < 10%). By accounting for longitudinal albumin data in patients, the PBPK model recapitulated the observed magnitude of the change in clearance of durvalumab without fitting to clinical PK data. The model simulation demonstrated that utilization of albumin levels as a marker of cachexia in PBPK models can be used to mechanistically predict time-dependent clearance of monoclonal antibodies.

Physiologically Based Pharmacokinetic (PBPK) modeling is a powerful tool in drug development that integrates drug-specific information with physiological parameters to predict drug concentrations. However, parameter estimation in PBPK models presents significant challenges due to the large number of parameters involved and limited observed data. This tutorial introduces a novel approach coupling whole-body PBPK (WB-PBPK) models with population estimation methods (popWB-PBPK) to leverage individual data and estimate inter-individual variability on physiologically relevant parameters. The framework employs an optimized Stochastic Approximation Expectation–Maximization (SAEM) algorithm, reducing the estimation runtime through an adaptive parameter grid optimization and linear interpolation techniques. Using theophylline as a case study, we illustrate how this approach can accurately estimate drug-specific parameters (CYP1A2 clearance and lipophilicity) while incorporating covariate effects (smoking status). The optimized algorithm significantly reduces computational time compared to the standard SAEM algorithm. Our implementation in the saemixPBPK R package provides an accessible framework for parameter estimation in PBPK models, enabling more robust predictions of pharmacokinetic behavior leveraging individual data. This approach represents an important advancement in mechanistic modeling, allowing simultaneous estimation of population parameters, variability, and uncertainty while maintaining the physiological relevance of PBPK models.

Potassium-competitive acid blockers (PCABs) are emerging alternatives to proton pump inhibitors for the treatment of acid-related diseases. However, due to the complex, nonlinear interaction between drug exposure, food intake, and physiological rhythms, optimizing dosing strategies remains challenging. A multi-leveled population analysis was conducted using published pharmacokinetic and pharmacodynamic data on four representative PCABs: tegoprazan, YH4808, fexuprazan, and vonoprazan. A semi-mechanistic population PK/PD model was developed to account for food effects, circadian pH rhythms, and pH-dependent drug absorption. A multi-level nonlinear mixed-effects modeling framework was implemented to capture both inter-drug and inter-study variability. The model successfully described the time course of plasma concentration and intragastric pH for all four PCABs under various conditions. The model identified differences in pharmacokinetics and pharmacodynamic potency between drugs (with the relative in vitro potency ranked as vonoprazan > fexuprazan > YH4808 > tegoprazan), and simulations demonstrated that both pre- and post-meal administration enhanced pH control in early time period via potentially distinct mechanisms: the pre-meal effect may arise from temporally separated contributions of food- and drug-induced pH elevation, whereas the post-meal effect is likely driven by temporally overlapping, additive actions, particularly under low-dose or non-steady-state conditions. Predicted pH profiles and holding times above pH 4 closely matched reported clinical outcomes. The study demonstrates the application of a mechanistic, multi-level population approach for cross-drug PK/PD evaluation of PCABs. The findings support drug-specific dose optimization and highlight the clinical relevance of food–drug interactions. The modeling approach provides a model platform for pharmacotherapy or model-informed drug development (MIDD).

A physiologically-based pharmacokinetic (PBPK) model of niraparib and its primary metabolite using a relevant virtual cancer population is reported here. A series of in vitro experiments using liver S9, microsomes, and hepatocytes with various inhibitors and recombinant supersomes demonstrated that niraparib is specifically metabolized by carboxylesterase 1 via amide hydrolysis to an acid metabolite (M1). Available virtual cancer populations, along with reference populations, were applied to modeling simulations using fixed trial designs with demographic and clinical chemistry parameters from patients receiving niraparib in clinical studies. Simulations of niraparib and its metabolite M1 were verified across numerous available clinical studies and repeat dose ranges in cancer patients within 2-fold. The PBPK model was used to simulate exposures in moderately hepatic impaired, healthy Chinese and Japanese virtual populations as a surrogate of cancer comorbidity. The PBPK model confirmed minimal DDI liability with niraparib as a precipitant for most in vitro tested drug metabolizing enzymes and transporters. In vitro, niraparib lacks any CYP inhibition, induces CYP1A2 but not CYP3A4, and is not a CYP substrate, unlike some other PARPi's, which inhibit and induce numerous enzymes/transporters and are objects of CYP metabolism. At clinically relevant doses of niraparib ≥ 200 mg, a weak induction risk is predicted with sensitive CYP1A2 substrates, such as caffeine, and both niraparib and olaparib clinically increase serum creatinine in cancer patients, with up to a moderate inhibition risk predicted with MATE-1/-2K substrates, such as metformin, using a PBPK model of niraparib in the absence of a dedicated DDI study.

Bosutinib is an orally available Src/Abl tyrosine kinase inhibitor and has been approved for the treatment of patients with Ph + chronic myelogenous leukemia. Bosutinib is a substrate of P-glycoprotein (P-gp) in vitro and is predominantly metabolized by CYP3A4 in humans with minimal urinary excretion. We present our perspective on using physiologically based pharmacokinetic modeling to understand the atypical changes in oral exposure of bosutinib, a CYP3A and P-gp substrate, in hepatic impairment patients.

Pegylated-interferon-α (Peg-IFNα) is a treatment option for chronic hepatitis B virus (HBV) infection. To quantify treatment response variability, we conducted a model-based meta-analysis (MBMA) of hepatitis B surface antigen (HBsAg) loss, defined as a binary outcome based on HBsAg levels falling below the limit of detection, with Peg-IFNα-based regimens at end-of-treatment (EOT) and 24 weeks post-treatment. A systematic review of HBsAg loss in chronic HBV infection was performed, searching Embase, MEDLINE, and Cochrane (January 2000–July 2022). Studies reporting only per-protocol results were excluded; intent-to-treat (ITT) or modified ITT results were prioritized. Models described the proportion achieving HBsAg loss with respect to treatment regimens, exploring baseline clinical and demographic covariates. For the EOT model, 83 study-strata-arms (11,493 participants) were included; for the 24-week model, 58 study-strata-arms (4267 participants) were included. In both models, Peg-IFNα duration and baseline HBsAg significantly predicted HBsAg loss (p < 0.001); baseline hepatitis B e-antigen (HBeAg) was an additional predictor at EOT (p = 0.007). These covariates reduced between-trial variance by 58.1% (EOT) and 77.6% (24-week), highlighting their role in explaining heterogeneity. This MBMA supports clinical trial design by simulating outcomes with Peg-IFNα across diverse populations, optimizing trial parameters, estimating sample sizes, and informing enrichment strategies. Notably, these findings have been applied to calibrate and validate in silico trials, demonstrating utility in advancing computational approaches for HBV drug development. This approach enhances precision in predicting treatment outcomes and sets a precedent for leveraging MBMA in chronic hepatitis B research, paving the way for more effective strategies.

Long-acting injectable medicinal products (LAIs) prolong drug release and thereby aim to enhance adherence and patient outcomes. European regulatory guidelines require the conduct of single- and multiple-dose trials to exclude differences in drug release between non-steady and steady state conditions. The complexity of these trials may however hamper the development of LAIs. This study aimed to examine whether drug release is different after single- and multiple-dose administration using clinical pharmacokinetic (PK) data of a sample of five regulatory-approved LAIs. Single- and multiple-dose data were extracted from an internal regulatory database. Population pharmacokinetic models with different absorption structures were developed using nonlinear mixed-effect modeling based on the single-dose data of every LAI. The best-fitting models were used to predict the pharmacokinetic profiles after multiple-dose administration. The absorption of LAIs after single-dose administration was best described with (parallel) first-order absorption structures (with and without lag-time). After multiple-dose administration, the mean model accuracy was 93% (minimum to maximum: 70%–122%), and 7 out of 10 observed pharmacokinetic variables (i.e., area under the plasma concentration—time curve, minimum and maximum concentration) met the pre-specified acceptance criteria. In conclusion, multiple-dose PK characteristics can be predicted using models developed from single-dose PK data, which indicates that drug release may not be very different between dosing conditions in this sample of regulatory-approved LAIs. Nevertheless, additional studies on other LAIs are required to test the generalizability of our findings and to increase our understanding of the limitations of the proposed model-based approach vis-à-vis the current evidentiary standard.

Modeling and simulations are indispensable tools to describe pharmacokinetics (PK) and pharmacodynamics to support monoclonal antibody (mAb) development. The linear PK of mAbs is commonly described by a 2-compartment PK model, while the nonlinear PK often observed at low doses is described by target mediated drug disposition (TMDD) models. Since target binding is the primary pharmacology of mAbs and receptor occupancy (RO) could impact PK through TMDD, it is desirable to have a simple mechanistic model to simultaneously describe both PK, including TMDD, and RO at the site of action (SoA). In this tutorial, we introduce a physiologically inspired PKRO (piPKRO) model for mAbs targeting membrane receptors. The linear PK part (referred to as the piPK model) is modified from the classical 2-compartment PK model to include using the physiological compartment volumes, adding drug clearance in extravascular compartments, describing mAb concentration in tissues reflecting measured partition coefficients, and target expression and drug binding based on the location of target expression. A few macroparameters including P_dist (partition coefficient) and t_dist (distribution half-time) are introduced to provide an intuitive understanding of the mAb distribution. Case studies of applying the model to real world data are provided. Analysis with the piPKRO model suggests that local drug depletion could occur due to significant target mediated drug clearance at the SoA in combination with relatively slow drug distribution. This local drug depletion can lead to much lower RO at the SoA compared to RO in the central compartment and subsequently impact efficacious dose predictions.

Chronic hepatitis B virus (HBV) infection remains a significant global health challenge. While the dynamic interplay between viral replication and host immune responses determines infection outcomes, the mechanisms driving the resolution of acute infection versus the emergence of chronicity remain incompletely understood. To address this challenge, we developed a detailed quantitative systems pharmacology (QSP) model of acute HBV infection capturing several key host immune and viral mechanisms absent in previous models. The model was parameterized using publicly available data and calibrated against clinical time-course datasets from multiple acute HBV case studies. Perturbation and local sensitivity analyses identified key drivers of biomarker dynamics, particularly hepatitis B virus DNA (HBV DNA), hepatitis B surface antigen (HBsAg), and alanine aminotransferase (ALT). These dynamics were most sensitive to parameters governing viral replication (e.g., HBV entry via the sodium taurocholate cotransporting polypeptide [NTCP] receptor, covalently closed circular DNA [cccDNA] formation, and hepatocyte turnover) and adaptive immune responses (e.g., CD8⁺ T cell activity, dendritic cell–mediated priming, and regulatory T cell [Treg]–driven immunosuppression). These influential parameters were used to generate a virtual population that reproduced the observed heterogeneity in biomarker trajectories. Notably, the magnitude and timing of biomarker peaks captured most of the variability, reflecting interindividual differences in individual immune responses and viral dynamics. While the current model nicely captures processes associated with acute HBV infections, it will be extended to different stages of chronic HBV with the objective of informing the rational design of novel therapies and supporting the development of curative HBV strategies.