CPT: Pharmacometrics & Systems Pharmacology最新文献

Infliximab, a monoclonal antibody used for immune-mediated diseases, shows high interpatient pharmacokinetic variability. Prolonged exposure increases the risk of adverse effects and costs, making dose personalization essential to balance safety, efficacy, and cost-effectiveness. Population pharmacokinetic models support individualized dosing, but different models may predict varying drug exposure for the same patient. This study aims to identify compatible models for each patient and assess the impact of model selection on dosing. This retrospective study included adult Crohn's disease patients receiving infliximab. Published pharmacokinetic models were screened. Model-patient compatibility was evaluated using Multivariate Exact Discrepancy through 100,000 Monte Carlo simulations. The Metropolis-Hastings algorithm generated individual parameter distributions. For each model-patient pair, the median and 90% confidence interval of the dose required to achieve a target exposure of 2079 mg*day/L were computed. Sixteen models were tested. No model was compatible with all patients. Dosing was calculated only for compatible pairs. The average median dose was 9.25 mg/kg, with an average imprecision of 6.63 mg/kg. The highest median dose reached 23.21 mg/kg, reflecting inter-model differences, while the greatest imprecision (25.69 mg/kg) stemmed from patient variability. This concentration-based method personalizes dosing via pharmacokinetic profiling. Patients can be classified into three groups: (1) those for whom all models provide similar recommendations, indicating high reliability across models; (2) those incompatible with all models, for whom the posology recommended by the manufacturer should be prioritized; and (3) those for whom some models are compatible but intensified therapeutic drug monitoring is required.

Analyzing exposure-response (E-R) relationships for time-to-event (TTE) endpoints presents challenges due to the inherent time-dependent nature of the data. Some authors address these difficulties by using a fixed timepoint approach, where exposure is assessed at a predetermined time rather than dynamically over time. (e.g., initial exposure or last exposure). The aim of the current work is to compare the use of time-static and time-varying metrics to assess the E-R relationship through simulations. PK exposures were simulated from a one-compartment model and TTE data from a parametric proportional hazard model, involving the weekly average PK concentration as a time-varying covariate. Several scenarios were considered to handle the type of dosing (fixed or adaptive), the accumulation of the drug (low or strong), the type of event (efficacy, safety or independent), and the timing of the event onset (early or late). Wald tests on the exposure effect parameter were performed to assess the significance of the E-R relationship. For each simulation scenario, the type-I error and the power of the Wald tests were reported, revealing that no time-static metric consistently produced reliable results across all conditions. In order to ensure adequate statistical properties, we recommend using time-varying exposure, which shows good performance across all scenarios.

Pediatric extrapolation strategies issued by health authorities have streamlined pediatric drug development and reduced the unnecessary burden of conducting pediatric clinical studies. In line with these strategies, physiologically based pharmacokinetic (PBPK) models have been utilized extensively for initial dosing regimen and sampling timepoint selection for pediatric studies, as well as dose validation throughout pediatric drug development. Here, the status and challenges of PBPK modeling in pediatric drug development have been summarized by the IQ Pediatric PBPK Working Group. Our work reviews current practices for pediatric PBPK modeling across various therapeutic areas. To enable best practice, we propose an optimized workflow for pediatric PBPK modeling recommendations. Two selected key pediatric PBPK case examples are also described, where modeling impacted the drug label extension to pediatric patients. Moreover, we analyze the current gaps and challenges in our understanding of drug absorption, distribution, metabolism, and elimination in pediatric PBPK model development. Since neonates are the least studied and the most medically fragile, the depth of our understanding of their rapidly evolving physiological processes is limited and so there exist significant modeling gaps which we summarize here. Finally, we provide recommendations, including building a public data repository, leveraging real-world data, and implementing microdose studies for addressing pediatric PBPK modeling challenges.

We developed a comprehensive, mechanistic model of human copper metabolism to support biomarker qualification for VTX-801, an adeno-associated vector-based gene therapy which is being developed to restore the mutated ATP7B copper transporter gene in Wilson disease (WD). The model integrates physiological copper kinetics with pathophysiological features of WD by distinguishing between ceruloplasmin-bound and non-ceruloplasmin-bound copper (NCC), and by explicitly incorporating ATP7B-dependent processes: biliary excretion and ceruloplasmin loading of copper. Literature-derived time–activity data from healthy subjects, heterozygous carriers, and WD patients, as well as clinical radiocopper data in plasma and feces from a pilot study in non-WD subjects, were used for model development and validation. VTX-801's dose–response was quantified in WD mouse models using ceruloplasmin oxidase activity measurement and ⁶⁴Cu fecal excretion. This enabled derivation of activity factors (AFs) corresponding to restored ATP7B function, with 15% and 40% selected as minimal and optimal efficacy targets. Simulations linked AFs to clinical biomarkers, demonstrating that the 48/2-h plasma radioactivity ratio can effectively differentiate VTX-801 responders from non-responders, providing a decision criterion to safely withdraw standard treatment in participants of a phase 1/2 trial. To broaden applicability beyond radiotracer studies, we simulated “cold” copper kinetics under steady-state conditions, deriving expected values for plasma copper, NCC, urinary copper excretion, and relative exchangeable copper (REC). These simulations suggest that REC may also serve as a suitable and simpler to implement, non-radioactive biomarker for ATP7B gene therapy. This model provides a robust quantitative framework to assess copper-related biomarkers in WD and their response to treatment in silico.

Trial Registration: EudraCT number: 2019-001157-13

Atopic dermatitis (AD) clinical trials exhibit substantial placebo response variability, confounding efficacy assessments of novel therapies. Traditional meta-analyses have identified potential contributors to this variability but rely on single time-point estimates, which fail to account for dynamic, longitudinal response patterns across trials. To overcome this limitation, we developed a model-based meta-analysis (MBMA) framework that characterizes time-course projections of EASI-75 placebo responses while accounting for key covariates. A systematic literature review identified 40 moderate-to-severe AD trials (18 Phase 2, 22 Phase 3), encompassing 4827 patients, suitable for longitudinal modeling. Modeling results highlighted concomitant therapy as a significant driver of placebo response, with trials permitting topical corticosteroids (TCS) demonstrating a 1.8-fold increase in EASI-75 placebo rates compared to trials without concomitant therapy. Additionally, baseline disease severity of the study population, as reflected by the mean baseline EASI score, was inversely associated with placebo response; each 1-point increase in baseline EASI reduced EASI-75 placebo rates at Weeks 12 and 16 by 0.96-fold. Time-course modeling suggested that placebo responses plateaued by Week 12, with EASI-75 outcomes at Week 12 capturing 94% of the projected response at Week 16. Overall, this MBMA framework provides quantitative guidance to optimize clinical trial design, refine power calculations, and improve the differentiation between therapeutic and placebo effects in AD drug development.

With the recent advances in machine learning (ML) and artificial intelligence (AI), data-driven modeling approaches for pharmacokinetics (PK) and pharmacodynamics (PD) have gained popularity due to their versatility in diverse settings and reduced reliance on prior assumptions. However, most of the ML methods ignore the hidden dynamics behind the data, lacking interpretability. This study investigated the applicability of neural controlled differential equation (NCDE), a novel ML method that is suitable for data-driven modeling of PK and PD profiles, especially in the setting of multiple dosing. We demonstrated that NCDE was capable of combining differential-equation-based dynamics with data-driven characteristics, flexibly incorporating various types of inputs, and embedding discontinuous dynamics. Moreover, a direct correspondence was identified between the learned dynamics of NCDE and the dynamics behind the data, which highlights the intrinsic interpretability of NCDE. Additionally, the influence of important hyperparameters was systematically investigated, and it was found that L1 regularization and the AdaMax optimizer were useful for stabilizing the training process and leading to a generalizable NCDE model. Together, these findings demonstrate the accuracy, generalizability, and interpretability of NCDE, indicating that NCDE is a reliable method for further application. In the future, NCDE may further facilitate PK and PD prediction in general.

A common challenge in conducting phase 1 studies that assess the impact of organ impairment on the pharmacokinetics of a drug is the recruitment of a demographically matched control group. The work presented here evaluated alternative approaches for generating control groups in these studies. Available phase 1 data from the upadacitinib and elagolix clinical programs were leveraged as case studies. A statistical matching approach and a population pharmacokinetic model-based approach were evaluated retrospectively for these programs' hepatic and renal impairment clinical studies. Geometric mean ratios of logarithmically transformed C_max and AUC_inf were used to compare exposure in organ impairment groups to respective matched or virtual control groups. In the statistical matching approach, the genetic matching algorithm using Mahalanobis distance showed that external control groups were adequately demographically balanced across all impairment groups of the study except for age. A 3:1 k-match approach minimized the prediction error between matched and reference in-study results for both case studies, resulting in differences in geometric mean ratios ranging from −19% to 3% and −27% to 40% for upadacitinib and elagolix, respectively, compared to in-study controls. Similarly, the population pharmacokinetic approach used models developed from phase 1 data in healthy participants and found that the results were generally comparable to the in-study results, with differences in geometric mean ratios ranging from −30% to 17% and −24% to 41% for upadacitinib and elagolix, respectively. These analyses demonstrate that both approaches may be viable alternatives to assess the impact of organ impairment on pharmacokinetics.

Statins are frequently prescribed for hyperlipidemia, a common comorbidity in patients with obesity and/or metabolic dysfunction-associated steatohepatitis (MASH). However, limited knowledge exists on how MASH may alter statin disposition within hepatocytes where the statin target, 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, is located. This study used a physiologically based pharmacokinetic (PBPK)/permeability-limited multicompartment liver (PerMCL) framework, incorporating zonal transporter and drug-metabolizing enzyme data. Systemic and hepatocellular concentrations of pravastatin, rosuvastatin, and atorvastatin were simulated in Healthy Volunteers (HV), Obese, Morbidly Obese, and MASH virtual populations with the Simcyp Simulator. A pharmacodynamic model in Simcyp Designer was then used to simulate alterations in rosuvastatin cholesterol-lowering efficacy between these populations. Hepatic transport and metabolism pathways were verified against clinical data. Organic anion transporting polypeptide (OATP)1B model uptake pathways were verified using genotype and drug–drug interaction data. Atorvastatin metabolism pathways were verified using metabolite data. Steady-state plasma and zonal hepatocellular concentration–time profiles for each statin were simulated across virtual populations of 100 individuals aged 40–65 years. Simulations predicted > 70% increases in maximal total plasma concentrations and area under the curve for pravastatin and rosuvastatin in MASH compared to HV, with changes in these parameters for atorvastatin simulated to increase > 250%. In MASH, unbound hepatocellular exposure increased by up to 127% in the periportal region for atorvastatin and decreased by up to 55% in the pericentral region for rosuvastatin. The pharmacodynamic model simulated decreased rosuvastatin cholesterol-lowering efficacy in MASH compared with Obese, which could be compensated for with a 50% increase in dose according to exploratory simulations.