CPT: Pharmacometrics & Systems Pharmacology最新文献

Bispecific antibodies can be broadly divided into two categories: those that are pharmacologically active as either dimers bound to a single target or as trimers bound to both targets, and those that are only active as trimers. Dose selection of trimer-based bispecifics poses a unique challenge, as toxicity increases with dose, but efficacy does not. Instead, trimer-driven bispecifics have a bell-shaped efficacy curve, for which both under- and over-dosing can cause a decrease in efficacy. To address the challenge of dose selection for trimer-based bispecifics, we develop a semi-mechanistic pharmacokinetic/pharmacodynamic model of one such bispecific, teclistamab. By introducing variability in key patient-specific parameters, we find that the currently selected phase II recommended dose of 1.5 mg/kg administered subcutaneously weekly falls within the calculated optimal range for maximizing concentration of the pharmacologically active trimer for a broad population. We next explore different strategies for patient stratification based on pre-treatment levels of measurable biomarkers. We discover that significantly more variability across subpopulations is predicted when the drug is administered every 2 weeks as compared to weekly administration, and that higher doses generally result in more interpatient variability. Further, the pharmacologically active trimers are predicted to be maximized at different doses for different subpopulations. These findings underscore the potential for model-supported patient stratification based on measurable biomarkers, offering a middle ground between population-level approaches and fully personalized medicine.

Pembrolizumab is an immune checkpoint inhibitor that has been approved for more than 20 different indications and has shown great survival benefits in various types of cancer. However, the reported benefits of pembrolizumab in patients' quality of life (QoL) have been inconsistent across different studies and different types of cancer. As oncology drug development increasingly emphasizes patient-centered care, patient-reported outcomes (PROs), particularly patient-reported QoL, are recognized as important clinical endpoints. To characterize the effects of pembrolizumab on patient-reported QoL, we conducted a model-based meta-analysis (MBMA) of published clinical trials evaluating pembrolizumab across different types of cancer. The longitudinal EORTC QLQ-C30 GHS/QOL data were extracted in our analysis as QoL scores. A population model was developed to characterize the longitudinal QoL trajectories and quantify both treatment toxicity and efficacy. Out of more than 300 screened studies, only 20 reported longitudinal EORTC QLQ-C30 QoL data. Among these, 8 studies reported no between-group differences in QoL outcomes between pembrolizumab and control arms. However, our modeling revealed that pembrolizumab was associated with greater toxicity but improved long-term QoL. Notably, our approach identified treatment effects on QoL that were not detected by traditional statistical analyses in the original publications. In summary, our study demonstrates that MBMA combined with population modeling enables more accurate evaluation of longitudinal PROs data, overcoming the limitations of conventional methods. This approach offers a robust framework for integrating patient-centered outcomes into oncology drug development and supports the broader use of PROs data in regulatory and clinical decision-making.

Binding in cis-configuration to the PD-1 receptor (PD-1) and IL-2$��\mathrm{\beta \gamma }�$ receptor (IL-2R$��\mathrm{\beta \gamma }�$) has been shown to lead to differentiation of CD8 T cells to better effectors, which is anticipated to drive efficacy of the immune-targeted cytokine eciskafusp alfa, or PD1-IL2v. Here we present a geometrically driven mathematical formulation informed by in vitro and early clinical data which enables prediction of doses at which cis-binding is at its highest, and explains observed differences in concentration-time profiles for patients who had recent exposure to other anti-PD-1 molecules compared with those who had not. Furthermore, binding in cis-configuration is expected to follow a “bell-shaped” relationship with drug concentration such that high concentrations may lead to reduced benefit/risk ratio compared with concentrations around the peak of the bell-shape. Model simulations identify patient cohorts for whom the upper limit of the pharmacological dosing range may be defined by either undesirable off-tumor target engagement or a decrease in on-tumor cis-binding.

Conventional exposure–response (E–R) analyses, such as logistic regression and time-to-event analysis using summary exposure metrics, are often conducted in oncology using data from the pivotal trial(s) with a single dose level to support dosing decisions. However, these E–R analyses, affected by multiple confounding factors, may mischaracterize true E–R relationships, potentially limiting their utility in dosing decisions. This study investigates potential mischaracterization in such analyses influenced by the following time-dependent confounding factors: exposure accumulation, dose modification patterns, and event onset time. We used a simulation-based approach to evaluate two E–R scenarios: ER1, where event time generated with a Weibull distribution is not affected by drug exposure, and ER2, where the response is driven by drug exposure via a joint PK-tumor size model. Our analyses indicate that when using time-dependent exposure metrics (e.g., average concentration till event/censoring), exposure accumulation tends to induce an inverse E–R trend, while dose modifications (interruptions/reductions) likely induce a positive E–R trend. Simulations suggest that employing static exposure metrics (e.g., first-cycle or steady-state) minimizes these biases. Additionally, adopting an Emax model aligned with the underground truth in ER2 in the E–R analyses could reduce bias. When significant dose modifications are present, including relevant data from a dose-range study and employing modified methods for time-dependent exposure derivation may help mitigate bias. Overall, using multiple exposure metrics (including static ones) to assess E–R consistency and interpreting the potential causal effects with totality of evidence (including dose–response results) should be implemented to better inform dosing decisions.

A subcutaneous formulation of nivolumab was evaluated in the phase III study CheckMate 67T (NCT04810078). The co-primary pharmacokinetic exposure endpoints, time-averaged serum concentration over the first 28 days (C_avgd28), and steady-state trough concentration (C_minss) were determined through population pharmacokinetic analysis as compared to conventional non-compartmental analysis (NCA). We proposed a model-based approach to determine subcutaneous and intravenous nivolumab exposures in CheckMate 67T, leveraging extensive prior pharmacokinetic data across various tumor types. The robustness of this model-informed drug development (MIDD) approach was assessed via clinical trial simulations. Concentration–time profiles in randomly sampled patients with renal cell carcinoma receiving second-line systemic therapy were simulated for subcutaneous/intravenous nivolumab. Population pharmacokinetic parameters, sampled from the joint parameter uncertainty distribution, were applied in simulations based on the CheckMate 67T design. Two population pharmacokinetic approaches—the PRIOR subroutine ($PRIOR) in NONMEM and a pooled analysis with historical nivolumab pharmacokinetic data—were used to analyze the simulated data. Results were compared to NCA using intensive and conventional sampling schemes. Analyses showed that model-based analysis provided more accurate area under the curve estimates than NCA. Model-predicted exposure measures, including C_avgd28, maximum serum concentration after the first dose, and minimum serum concentration at day 28, were also consistent across both population pharmacokinetic approaches, with minimal differences in geometric means. In conclusion, both the $PRIOR and pooled population pharmacokinetic methods yielded more accurate results compared to conventional NCA. The MIDD approach was validated as a robust and feasible method to support non-inferiority assessment based on clinical trial simulations.

To establish bioequivalence (BE) of a generic test formulation with respect to a reference listed drug, it is necessary to demonstrate a comparable rate and extent to which active ingredients reach the site of action. To decrease unnecessary human testing and simulate scenarios involving specific populations or challenges with recruitment or study design, industry and regulators are increasingly considering in silico virtual bioequivalence (VBE) approaches. This tutorial introduces the VBEToolbox R package: a toolbox within the Open Systems Pharmacology framework to streamline and standardize computational VBE workflows. The package integrates in vitro and in vivo data to train pharmacokinetic models through inference of inter-individual variability from clinical data and establishment of in vitro to in vivo extrapolations. A nonparametric approach is adopted to account for uncertainties from parameter non-identifiability. The trained model is then applied to determine the study size with statistical power needed to demonstrate BE virtually. The use of the VBE tool is illustrated with two case studies. The first evaluates the VBE of petrolatum and ethylene glycol dermal formulations of testosterone by integrating in vitro skin permeation tests, vehicle/skin partitioning data, testosterone solubility data, and in vivo absorption data in a mechanistic in vitro/in vivo dermal absorption model. The second assesses the VBE of two oral bupropion formulations by integrating in vitro dissolution data in a physiologically based pharmacokinetic model. These case studies highlight essential considerations for model development, training, and extrapolation toward application for VBE assessment.

Efavirenz induces and inhibits multiple drug-metabolizing enzymes, contributing to significant drug–drug interactions. This study quantified the impact of multiple doses of efavirenz on CYP3A activity via midazolam metabolism using a population pharmacokinetic approach. Healthy volunteers received 1 mg midazolam orally in two sessions: first with a single 600 mg efavirenz dose and then after chronic efavirenz dosing (600 mg/day for 17 days). Midazolam and 1′-hydroxymidazolam were quantified via LC–MS/MS, and CYP2B6, CYP3A4, and CYP3A5 genotypes were assessed using TaqMan assays. Seventy-two volunteers (n = 72) completed sampling after the initial dose, and 58 completed both occasions. Non-linear mixed effects modeling was performed using the stochastic approximation expectation maximization estimation method in NONMEM. The base pharmacokinetic model employed was a two-compartment model with first-order absorption and first-order elimination, incorporating proportional error terms for midazolam and 1′-hydroxymidazolam. Covariate analysis utilized a full model approach to assess the impact of CYP3A4 and CYP3A5 genotypes, self-reported sex, and multiple doses of efavirenz as covariates affecting the formation clearance of 1-OH midazolam. CYP3A5 expressors exhibited a 1.27-fold increase in midazolam clearance compared to non-expressors, while CYP3A4 intermediate metabolizers showed a 0.94-fold decrease relative to normal metabolizers. Clearance was also 1.30-fold higher in females compared to males. Multiple doses of efavirenz increased midazolam clearance by 1.92-fold (95% CI 1.65–2.28) after accounting for inter-individual variability caused by other covariates. Furthermore, K_a and bioavailability (F) increased with repeated efavirenz exposure. In conclusion, this population pharmacokinetic analysis effectively quantified the specific induction effect of multiple doses of efavirenz on CYP3A compared to a single dose of efavirenz.

Trial Registration: ClinicalTrials.gov identifier: NCT00668395

The EryDex System (EDS) is a drug/device combination, which has been tested in clinical trials for ataxia telangiectasia (AT). EDS encapsulates dexamethasone sodium phosphate (DSP) solution in autologous erythrocytes at the point of care, and encapsulated DSP (eDSP) is infused back into the patient. Low doses of dexamethasone are released from erythrocytes over a 30-day period. This study aimed to (1) characterize the pharmacokinetics (PK) of dexamethasone released from intravenously infused eDSP based on data collected in clinical trials of healthy adults and pediatric AT patients, and to (2) simulate and extrapolate exposure measures of dexamethasone following intravenous infusion of eDSP administered once per month over 6 months in a pediatric population. The population PK model was developed using dense PK data from a phase 1 study in healthy adults and sparse PK data from a phase 3 study in pediatric AT patients. Three dose levels were studied, and the overall PK population included 24 healthy adults and 109 AT patients. The PK of dexamethasone released from eDSP was described using a simplified two-compartment model, adequate for estimating systemic exposure despite not fully capturing RBC release kinetics indicative of a triphasic pattern. The model showed a good fit, and future refinement will include mechanistic release modeling as more in vitro and in vivo data become available. Monte Carlo simulations of eDSP showed a rapid peak at 0.67 h, followed by sustained dexamethasone release; faster in the first 24 h, then slower over 20–30 days. No accumulation occurred with once-monthly dosing.

Drug–drug interaction (DDI) is a common cause of adverse drug events (ADEs). Despite real-world data-based studies have developed knowledge on DDI, the precise relationships between doses of two-drug combinations exposure and the risks of ADEs remain largely unknown. The estimation of the dose-risk relationship (DRR) under commonly used regression models could be subject to model misspecification or overspecification. We developed a dose-aware model (DAM) for revealing DRR. DAM could improve the DRR estimation by identifying the optimal model from a large number of meaningful models of doses of two-drug combinations exposure and risks of ADE. We compared DAM with commonly used models (e.g., exposed-versus-unexposed model, dose-response model, and saturated model), in which DAM had higher performance metrics on model fitting in real-world data analyses and DRR estimation in a simulation study. In conclusion, DAM is a powerful tool for estimating DRR for potential adverse two-drug combinations, which could be used to mitigate DDI-induced harm.

Nipocalimab is a fully human immunoglobulin G (IgG)1 monoclonal antibody (mAb) designed to selectively block the IgG binding site of neonatal fragment crystallizable receptor (FcRn) to inhibit IgG recycling and decrease circulating IgG, including pathogenic IgG autoantibodies (such as antiacetylcholine receptor, anti-muscle-specific kinase, and anti-low-density lipoprotein-related protein 4 antibodies in generalized myasthenia gravis [gMG]). A mechanistic model, integrating serum nipocalimab concentrations, FcRn occupancy, and total serum IgG data from five phase 1 studies in healthy adult participants and one phase 2 (Vivacity-MG) study in adult participants with gMG, was developed. The relationship between total serum IgG reduction and placebo-corrected MG-Activities of Daily Living score change from baseline in participants with gMG was also characterized. Nipocalimab exhibited nonlinear target (FcRn)-mediated disposition, causing rapid, reversible, and concentration-dependent FcRn occupancy and IgG reduction (up to 85%) in healthy participants and participants with gMG. The PK of nipocalimab after a single intravenous (IV) administration is consistent with that after repeated IV administrations, with no accumulation following every 2 weeks (Q2W) dosing. The PK of nipocalimab and its effect on IgG reduction were similar between healthy participants and participants with gMG. Model-based simulations indicated that the IV dose of 15 mg/kg Q2W, starting 2 weeks after a 30 mg/kg IV loading dose, was the lowest Q2W maintenance dose predicted to achieve the target of 70% median of the average change in IgG reduction in participants with gMG and was the recommended dose for the pivotal phase 3 Vivacity-MG3 study in a gMG population.