CPT: Pharmacometrics & Systems Pharmacology最新文献

Inspired from quantum Monte Carlo, by sampling discrete and continuous variables at the same time using the Metropolis–Hastings algorithm, we present a novel, fast, and accurate high performance Monte Carlo Parametric Expectation Maximization (MCPEM) algorithm. We named it Randomized Parametric Expectation Maximization (RPEM). We compared RPEM with NONMEM's Importance Sampling Method (IMP), Monolix's Stochastic Approximation Expectation Maximization (SAEM), and Certara's Quasi-Random Parametric Expectation Maximization (QRPEM) for a realistic two-compartment voriconazole model with ordinary differential equations using simulated data. We show that RPEM is as fast and as accurate as the algorithms IMP, QRPEM, and SAEM for the voriconazole model in reconstructing the population parameters, for the normal and log-normal cases.

The association between memory CD4+ T cells and cancer prognosis is increasingly recognized, but their impact on lung adenocarcinoma (LUAD) prognosis remains unclear. In this study, using the cell-type identification by estimating relative subsets of RNA transcripts algorithm, we analyzed immune cell composition and patient survival in LUAD. Weighted gene coexpression network analysis helped identify memory CD4+ T cell–associated gene modules. Combined with module genes, a five-gene LUAD prognostic risk model (HOXB7, MELTF, ABCC2, GNPNAT1, and LDHA) was constructed by regression analysis. The model was validated using the GSE31210 data set. The validation results demonstrated excellent predictive performance of the risk scoring model. Correlation analysis was conducted between the clinical information and risk scores of LUAD samples, revealing that LUAD patients with disease progression exhibited higher risk scores. Furthermore, univariate and multivariate regression analyses demonstrated the model independent prognostic capability. The constructed nomogram results demonstrated that the predictive performance of the nomogram was superior to the prognostic model and outperformed individual clinical factors. Immune landscape assessment was performed to compare different risk score groups. The results revealed a better prognosis in the low-risk group with higher immune infiltration. The low-risk group also showed potential benefits from immunotherapy. Our study proposes a memory CD4+ T cell–associated gene risk model as a reliable prognostic biomarker for personalized treatment in LUAD patients.

Statins are used to reduce liver cholesterol levels but also carry a dose-related risk of skeletal muscle toxicity. Concentrations of statins in plasma are often used to assess efficacy and safety, but because statins are substrates of membrane transporters that are present in diverse tissues, local differences in intracellular tissue concentrations cannot be ruled out. Thus, plasma concentration may not be an adequate indicator of efficacy and toxicity. To bridge this gap, we used physiologically based pharmacokinetic (PBPK) modeling to predict intracellular concentrations of statins. Quantitative data on transporter clearance were scaled from in vitro to in vivo conditions by integrating targeted proteomics and transporter kinetics data. The developed PBPK models, informed by proteomics, suggested that organic anion–transporting polypeptide 2B1 (OATP2B1) and multidrug resistance–associated protein 1 (MRP1) play a pivotal role in the distribution of statins in muscle. Using these PBPK models, we were able to predict the impact of alterations in transporter function due to genotype or drug–drug interactions on statin systemic concentrations and exposure in liver and muscle. These results underscore the potential of proteomics-guided PBPK modeling to scale transporter clearance from in vitro data to real-world implications. It is important to evaluate the role of drug transporters when predicting tissue exposure associated with on- and off-target effects.

Modeling the relationships between covariates and pharmacometric model parameters is a central feature of pharmacometric analyses. The information obtained from covariate modeling may be used for dose selection, dose individualization, or the planning of clinical studies in different population subgroups. The pharmacometric literature has amassed a diverse, complex, and evolving collection of methodologies and interpretive guidance related to covariate modeling. With the number and complexity of technologies increasing, a need for an overview of the state of the art has emerged. In this article the International Society of Pharmacometrics (ISoP) Standards and Best Practices Committee presents perspectives on best practices for planning, executing, reporting, and interpreting covariate analyses to guide pharmacometrics decision making in academic, industry, and regulatory settings.

A joint modeling framework was developed using data from 75 patients of early amcenestrant phase I–II AMEERA-1-2 dose escalation and expansion cohorts. A semi-mechanistic tumor growth inhibition (TGI) model was developed. It accounts for the dynamics of sensitive and resistant tumor cells, an exposure-driven effect on tumor proliferation of sensitive cells, and a delay in the initiation of treatment effect to describe the time course of target lesion tumor size (TS) data. Individual treatment exposure overtime was introduced in the model using concentrations predicted by a population pharmacokinetic model of amcenestrant. This joint modeling framework integrated complex RECISTv1.1 criteria information, linked TS metrics to progression-free survival (PFS), and was externally evaluated using the randomized phase II trial AMEERA-3. We demonstrated that the instantaneous rate of change in TS (TS slope) was an important predictor of PFS and the developed joint model was able to predict well the PFS of amcenestrant phase II monotherapy trial using only early phase I–II data. This provides a good modeling and simulation tool to inform early development decisions.

Leriglitazone is a unique peroxisome proliferator-activated receptor-gamma (PPARγ) agonist that crosses the blood–brain barrier in humans and clinical trials have shown evidence of efficacy in neurodegenerative diseases. At clinical doses which are well-tolerated, leriglitazone reaches the target central nervous system (CNS) concentrations that are needed for PPARγ engagement and efficacy; PPARγ engagement is also supported by clinical and anti-inflammatory biomarker changes in the Cerebrospinal fluid in the CNS. Plasma pharmacokinetics (PK) of leriglitazone were determined in a phase 1 study in male healthy volunteers comprising a single ascending dose (SAD) and a multiple ascending dose (MAD) at oral doses of 30, 90, and 270 mg and 135 and 270 mg, respectively. Leriglitazone was rapidly absorbed with no food effect on overall exposure and showed a linear PK profile with dose-exposure correlation. A physiologically based pharmacokinetic (PBPK) model was developed for leriglitazone based on phase 1 data (SAD part) and incorporated CYP3A4 (f_mCYP3A4 = 24%) and CYP2C8-mediated (f_mCYP2C8 = 45%) metabolism, as well as biliary clearance (f_eBIL = 19.5%) derived from in vitro data, and was verified by comparing the observed versus predicted concentration-time profiles from the MAD part. The PBPK model was prospectively applied to predict the starting pediatric doses and was preliminarily verified with data from five pediatric patients.

Duchenne muscular dystrophy (DMD) is a rare X-linked recessive disorder characterized by loss-of-function mutations in the gene encoding dystrophin. These mutations lead to progressive functional deterioration including muscle weakness, respiratory insufficiency, and musculoskeletal deformities. Three-dimensional gait analysis (3DGA) has been used as a tool to analyze gait pathology through the quantification of altered joint kinematics, kinetics, and muscle activity patterns. Among 3DGA indices, the Gait Profile Score (GPS), has been used as a sensitive overall measure to detect clinically relevant changes in gait patterns in children with DMD. To enhance our understanding of the clinical translation of 3DGA, we report here the development of a population nonlinear mixed-effect model that jointly describes the disease progression of the 3DGA index, GPS, and the functional endpoint, North Star Ambulatory Assessment (NSAA). The final model consists of a quadratic structure for GPS progression and a linear structure for GPS-NSAA correlation. Our model was able to capture the improvement in function in GPS and NSAA in younger subjects, as well as the decline of function in older subjects. Furthermore, the model predicted NSAA (CFB) at 1 year reasonably well for DMD subjects ≤7 years old at baseline. The model tended to slightly underpredict the decline in NSAA after 1 year for those >7 years old at baseline, but the prediction summary statistics were well maintained within the standard deviation of observed data. Quantitative models such as this may help answer clinically relevant questions to facilitate the development of novel therapies in DMD.

Trastuzumab deruxtecan (T-DXd; DS-8201; ENHERTU®) is a human epithelial growth factor receptor 2 (HER2)-directed antibody drug conjugate (ADC) with demonstrated antitumor activity against a range of tumor types. Aiming to understand the relationship between antigen expression and downstream efficacy outcomes, T-DXd was administered in tumor-bearing mice carrying NCI-N87, Capan-1, JIMT-1, and MDA-MB-468 xenografts, characterized by varying HER2 levels. Plasma pharmacokinetics (PK) of total antibody, T-DXd, and released DXd and tumor concentrations of released DXd were evaluated, in addition to monitoring γΗ2AX and pRAD50 pharmacodynamic (PD) response. A positive relationship was observed between released DXd concentrations in tumor and HER2 expression, with NCI-N87 xenografts characterized by the highest exposures compared to the remaining cell lines. γΗ2AX and pRAD50 demonstrated a sustained increase over several days occurring with a time delay relative to tumoral-released DXd concentrations. In vitro investigations of cell-based DXd disposition facilitated the characterization of DXd kinetics across tumor cells. These outputs were incorporated into a mechanistic mathematical model, utilized to describe PK/PD trends. The model captured plasma PK across dosing arms as well as tumor PK in NCI-N87, Capan-1, and MDA-MB-468 models; tumor concentrations in JIMT-1 xenografts required additional parameter adjustments reflective of complex receptor dynamics. γΗ2AX longitudinal trends were well characterized via a unified PD model implemented across xenografts demonstrating the robustness of measured PD trends. This work supports the application of a mechanistic model as a quantitative tool, reliably projecting tumor payload concentrations upon T-DXd administration, as the first step towards preclinical-to-clinical translation.

Lenvatinib is a receptor tyrosine kinase (RTK) inhibitor targeting vascular endothelial growth factor (VEGF) receptors 1–3, fibroblast growth factor (FGF) receptors 1–4, platelet-derived growth factor receptor-α (PDGFRα), KIT, and RET that have been implicated in pathogenic angiogenesis, tumor growth, and cancer. The primary objective of this work was to evaluate, by establishing quantitative relationships, whether lenvatinib exposure and longitudinal serum biomarker data (VEGF, Ang-2, Tie-2, and FGF-23) are predictors for change in longitudinal tumor size which was assessed based on data from 558 patients with radioiodine-refractory differentiated thyroid cancer (RR-DTC) receiving either lenvatinib or placebo treatment. Lenvatinib PK was best described by a 3-compartment model with simultaneous first- and zero-order absorption and linear elimination from the central compartment with significant covariates (body weight, albumin <30 g/dL, ALP>ULN, RR-DTC, RCC, HCC subjects, and concomitant CYP3A inhibitors). Except for body weight, none of the covariates have any clinically meaningful effect on exposure to lenvatinib. Longitudinal biomarker measurements over time were reasonably well defined by a PK/PD model with common EC₅₀, E_max, and a slope for disease progression for all biomarkers. Longitudinal tumor measurements over time were reasonably well defined by a tumor growth inhibition E_max model, which in addition to lenvatinib exposure, included model-predicted relative changes from baseline over time for Tie-2 and Ang-2 as having significant association with tumor response. The developed PK/PD models pave the way for dose optimization and potential prediction of clinical response.

Baricitinib is approved for the treatment of rheumatoid arthritis (RA) in more than 70 countries, and juvenile idiopathic arthritis (JIA) in the European Union. Population pharmacokinetic (PK) models were developed in a phase 3 trial to characterize PK in pediatric patients with JIA and identify weight-based dosing regimens. The phase 3, randomized, double-blind, placebo-controlled withdrawal, efficacy and safety trial, JUVE-BASIS, enrolled patients (aged 2 to <18 years) with polyarticular course JIA. During a safety/PK period, baricitinib concentration data from age-based dose cohorts were compared to concentrations from adult patients receiving 4-mg QD. PK data were used to develop a population PK model with allometric scaling to determine a weight-based posology in pediatric patients with JIA that matched the adult 4-mg exposure. Baricitinib plasma concentrations from 217 pediatric patients were used to characterize PK. Based on the adult model, pediatric PK was best described using a 2-compartment model with allometric scaling on clearance and volume of distribution and renal function (estimated with glomerular filtration rate [GFR], a known covariate affecting PK of baricitinib) on clearance. The PK modeling suggested the optimal dosing regimen based on weight for pediatric patients as: 2-mg QD for patients 10 to <30 kg and 4-mg QD for patients ≥30 kg. The use of a population PK model of baricitinib treatment in adult patients with RA, with the addition of allometric scaling for weight on clearance and volume terms, was useful to predict exposures and identify weight-based dosing in pediatric patients with JIA.