CPT: Pharmacometrics & Systems Pharmacology最新文献

{"title":"Population Pharmacokinetic Modeling of the Oral Calcitonin Gene-Related Peptide Receptor Antagonist Rimegepant in Adults.","authors":"Craig M Comisar, Jim H Hughes, Jose Francis, Yorinao Chinda, Yamato Sano, Chieko Muto, Christine Neumar, Rajinder Bhardwaj, Richard Bertz, Jing Liu","doi":"10.1002/psp4.70051","DOIUrl":"https://doi.org/10.1002/psp4.70051","url":null,"abstract":"<p><p>Rimegepant is a small-molecule calcitonin gene-related peptide receptor antagonist approved for acute and preventive migraine treatment in adults, administered as an orally disintegrating tablet (ODT). A population pharmacokinetic analysis was performed to describe rimegepant's plasma concentration-time course and to estimate covariate effects on rimegepant exposure. The model was developed/evaluated in 3 stages using data from 11 phase 1 clinical studies, wherein rimegepant was administered orally to healthy adults, elderly people with stable chronic illness(es), adults with renal or hepatic dysfunction, and healthy adults with Japanese or Chinese ethnicity. Plasma concentration-time data were analyzed using nonlinear mixed effects modeling. A 2-compartment model with 4 transit compartments and a first-order absorption best described the rimegepant plasma concentration-time course. Estimated typical values (%relative standard error) were apparent clearance (CL/F) = 24.1 L/h (4.86%), apparent central volume of distribution (V_c/F) = 114.0 L (5.36%), apparent inter-compartmental clearance (Q/F) = 3.94 L/h (6.37%), apparent peripheral volume of distribution (V_p/F) = 46.0 L (5.30%), absorption rate constant (k_a) = 3.86 h^-1 (28.4%), and transit absorption rate constant (ktr) = 8.23 h^-1 (8.24%). Statistically significant covariates included empirical allometric body weight-based scaling exponents (0.75 for CL/F and Q/F and 1 for V_c/F and V_p/F); severe/moderate hepatic impairment and fluconazole/itraconazole co-administration on CL/F; fed status, dose on relative bioavailability; and fed status, itraconazole co-administration, and capsule and ODT formulations on transition absorption rate constant. Only severe hepatic impairment and co-administration of itraconazole resulted in a clinically significant decrease in rimegepant CL/F, supporting the recommendation to avoid rimegepant administration in patients with severe hepatic impairment or with a strong CYP3A4 inhibitor.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144564640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pirana and Integrated PMX Tools, a Workbench for NONMEM, NLME, pyDarwin, and RsNLME.","authors":"Rong Chen, Mark Sale, James Craig, Michael Tomashevskiy, Alex Mazur, Shuhua Hu, Keith Nieforth","doi":"10.1002/psp4.70067","DOIUrl":"https://doi.org/10.1002/psp4.70067","url":null,"abstract":"<p><p>Keizer initially described Pirana as a workbench designed to streamline management of NONMEM modeling, visualization, and analysis. Initial versions of Pirana integrated tools included NONMEM and PSN. As new tools have become available to pharmacometricians, new capabilities have been added to Pirana. These capabilities include: Integration of the NLME engine via the R speaks NLME (RsNLME) package for developing NLME models Integration of a Shiny graphical interface for the construction of NLME models Integration of machine learning pyDarwin python package Integration of a Shiny interface for custom diagnostics including Goodness of Fit (GOF) plots, tables, Visual Predictive Check (VPC) and report shell generation Improved setup with support for parallel execution on a wide range of platforms In this tutorial, we present a full workflow demonstrating how to use Pirana to build, fit, post-process, and perform VPC on models using NONMEM and NLME. In addition, we show how to use the machine learning-driven pyDarwin package with Pirana to automatically search model structures, random effects, and covariate models.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144559462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shap-Cov: An Explainable Machine Learning Based Workflow for Rapid Covariate Identification in Population Modeling.","authors":"Logan Brooks, Rashed Harun, Jin Y Jin, James Lu","doi":"10.1002/psp4.70044","DOIUrl":"https://doi.org/10.1002/psp4.70044","url":null,"abstract":"<p><p>Covariate identification in population pharmacokinetic/pharmacodynamic (popPK/PD) modeling is a key component in model development that is often prone to bias, time-consuming, and even intractable when too many covariates or complicated models are being considered. Early work leveraging machine learning (ML) for covariate screening has shown promising results over traditional methods. In this work, we expand this effort by integrating explainable machine learning facilitated by Shapley Additive Explanations (SHAP) analysis and covariate uncertainty quantification as well as a formal framework for establishing statistical significance of covariate relationships. Finally, we have packaged the proposed methodology into a flexible set of functions (shap-cov) to support popPK/PD modeling covariate identification.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Competing Risks Analysis of the Finnish Diabetes Prevention Study.","authors":"Moustafa M A Ibrahim, Matti Uusitupa, Jaakko Tuomilehto, Jaana Lindström, Maria C Kjellsson, Mats O Karlsson","doi":"10.1002/psp4.70065","DOIUrl":"https://doi.org/10.1002/psp4.70065","url":null,"abstract":"<p><p>Clinical studies often observe one interesting event in the presence of other competing events. When both types of events can occur at any time but are only observed at clinical visits (i.e., interval censored), standard survival models may introduce bias in the estimated incidence of the interesting event over time. This can also lead to inflated relative differences between treatment groups. We developed a multi-state model for competing risks analysis of interval censored data from the Finnish Diabetes Prevention Study. The developed model predicted the participants' clinical outcomes and demonstrated that lifestyle changes significantly decreased the risk of both diabetes and death. The model showed that those who dropped out were at lower risk of developing diabetes, neglecting the assumption of independent censoring. Furthermore, the model identified the most important covariates predicting the future development of diabetes, which should be targeted for therapeutic intervention in likely clinical scenarios. These covariates are baseline BMI, HbA1c, and insulin sensitivity measurements by QUICKI for the onset of developing T2DM, baseline BMI for dropping out, and sex and age as the predictive covariates of death. Trial Registration: ClinicalTrials.gov identifier: NCT00518167.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Pharmacometric Workflow for Resolving Model Instability in Model Use-Reuse Settings.","authors":"Stephen B Duffull, Daniel F B Wright, Xiao Zhu, Xin Liu, Ahmed Abulfathi, Hailemichael Hishe","doi":"10.1002/psp4.70049","DOIUrl":"https://doi.org/10.1002/psp4.70049","url":null,"abstract":"<p><p>The development of fit-for-purpose pharmacokinetic-pharmacodynamic (PKPD) models based on clinical and pre-clinical data is a critically important process in model informed drug development. This process is often hampered by modeling stability issues that are often multifactorial in nature and difficult to overcome, leading to protracted model building and arbitrary simplification of the model. This tutorial provides a heuristic workflow to help identify and resolve issues relating to model instability. The approach is centered on analyses undertaken using NONMEM, but the concepts can be generalized to other software used for population analysis of pharmacokinetics (PK) or PKPD data.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144505024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of the Drug-Drug Interaction Potential of the GlyT1 Inhibitor Iclepertin (BI 425809): A Physiologically Based Pharmacokinetic (PBPK) Modeling Approach.","authors":"Elin M Matsson, Michael Desch, Valerie Nock, Nina Hanke","doi":"10.1002/psp4.70060","DOIUrl":"https://doi.org/10.1002/psp4.70060","url":null,"abstract":"<p><p>Despite predicting poor functional outcomes and being a significant patient burden, there are no approved pharmacotherapies to treat symptoms of cognitive impairment associated with schizophrenia (CIAS). Iclepertin (BI 425809) is a potent and selective inhibitor of glycine transporter-1 (GlyT1) that was in Phase III development for the treatment of CIAS. Iclepertin is metabolized by the cytochrome P450 (CYP) 3A4 enzyme and also induces CYP3A4 at supratherapeutic concentrations, so drug-drug interactions (DDIs) with CYP3A4 perpetrators and substrates may be expected. A physiologically based pharmacokinetic (PBPK) model was built and qualified based on physiochemical, in vitro, and Phase I clinical data of iclepertin that included different administration routes, formulations, dose levels, single- and multiple-dose administrations and food statuses. The iclepertin PBPK model was further qualified using clinical data of DDIs with a strong CYP3A4 inducer (rifampicin) and a strong CYP3A4 inhibitor (itraconazole). The qualified model was then applied to simulate DDIs of iclepertin 10 mg daily (the intended therapeutic dose) as a victim or perpetrator drug of CYP3A4. Based on the thorough qualification with clinical DDI data, the model was deemed qualified to predict new, untested clinical scenarios such as alternative drug doses, coadministration of different CYP3A4 substrates, coadministration of weak-moderate inducers and inhibitors of CYP3A4, and in the setting of polymedication in vivo. The model allows detailed analyses of DDI behaviors to inform appropriate prescribing of concomitant medications in patients treated with iclepertin.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144483442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporating Multiple Imputation Into Tanezumab Dose-Response Modeling of WOMAC Pain CFB Across Six Randomized Placebo-Controlled Phase 3 Trials.","authors":"Martin Boucher, Scott Marshall, Puneet Gaitonde","doi":"10.1002/psp4.70070","DOIUrl":"https://doi.org/10.1002/psp4.70070","url":null,"abstract":"<p><p>Multiple imputation is increasingly being used to deal with missing data in clinical trials. It is a simulation approach leading to multiple analyses of a pre-defined number of datasets. For a pre-defined statistical model, this is relatively straightforward, but for model development as typically conducted in Pharmacometrics, it is unclear how such an approach could be implemented. This case study describes how this was addressed at the time of a regulatory submission and considers alternative approaches which could be adopted in the future.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144495060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Combined Model-Based Meta-Analysis of Aggregated and Individual FEV1 Data From Randomized COPD Trials.","authors":"Liang Yang, Carolina Llanos-Paez, Shuying Yang, Claire Ambery, Alienor Berges, Maria C Kjellsson, Mats O Karlsson","doi":"10.1002/psp4.70059","DOIUrl":"https://doi.org/10.1002/psp4.70059","url":null,"abstract":"<p><p>Model-based meta-analysis allows integration of aggregated-level data (AD) from different clinical trials in one model to assess population efficacy/safety. However, AD is limited in individual-level information, while individual-patient-level data (IPD) are hard to obtain. Combined modeling may take advantage of both sources. Chronic obstructive pulmonary disease (COPD) is a leading cause of poor health and death. This study established a combined ADIPD model of COPD clinical trials with forced expiratory volume in 1 s (FEV1) as an endpoint and explored methods for estimating interstudy variability (ISV), interindividual variability (IIV), and aggregation bias. Stochastic simulation and estimations (SSE) showed the best method in NONMEM to estimate ISV/IIV: using $LEVEL with equal weight of studies; for the AD part, ISVs from the AD model were fixed, estimating IIV with separate ETAs for each arm; the IPD part shared the fixed ISV and estimated IIV. An approximated normal distribution was derived for lognormal IIV to avoid aggregation bias. Covariate correlations were different at aggregated and individual levels, but did not introduce aggregation bias according to SSE. A separate AD model (published) and IPD model were built, then combined to form the ADIPD model. The ADIPD model included FEV1 baseline, disease progression, placebo effect, and Emax/constant dose-responses for 23 compounds. Identified covariate relationships: higher age, female, higher disease severity, non-current smoker related to lower baseline; higher baseline related to faster disease progression and higher drug effects. Covariate coefficients were estimated more precisely in the ADIPD model than the AD model. ADIPD modeling allows more informed clinical trial simulations for study design. Trial Registration: ClinicalTrials.gov identifier: NCT01053988 and NCT01054885.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144324621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semi-Mechanistic Population Pharmacokinetic/Pharmacodynamic (PK/PD) Modeling of Dupilumab on Pre-Bronchodilator Forced Expiratory Volume in 1 Second (FEV₁) in Uncontrolled Moderate-To-Severe Asthma.","authors":"Li Zhang, John D Davis, Vanaja Kanamaluru, Christine Xu","doi":"10.1002/psp4.70057","DOIUrl":"https://doi.org/10.1002/psp4.70057","url":null,"abstract":"<p><p>In this study, we investigated the pharmacokinetic/pharmacodynamic (PK/PD) relationship of dupilumab as an add-on therapy in the intent-to-treat (ITT) uncontrolled moderate-to-severe asthma population and identified the factors significantly contributing to variability in forced expiratory volume in 1 second; (FEV₁). A semi-mechanistic population PK/PD model was developed using data from two placebo-controlled pivotal studies in 2654 adult and adolescent patients (n = 794 treated with placebo; n = 1860 treated with dupilumab 200 mg [400-mg loading dose] or 300 mg [600-mg loading dose] administered subcutaneously every 2 [Q2W] or 4 weeks [Q4W]). Treatment effect was described using a dupilumab concentration-dependent direct-response E_max model, and placebo effect was described using an empirical time-dependent function. Demographic variables, baseline disease characteristics, type-2 inflammation biomarkers, and immunogenicity were tested as covariates using the stepwise forward selection and backward elimination method. Baseline type-2 inflammation biomarkers (fractional exhaled nitric oxide [FeNO] level and blood eosinophil [EOS] count) were found to be significant covariates for FEV₁, with greater efficacy in patients with elevated biomarker levels. None of the other tested covariates, including age (12-87 years), had a significant impact on FEV₁. The PK/PD model predicted near-maximum FEV₁ response (0.1 L) over a dose of dupilumab. 200-300 mg Q2W in patients with moderate-to-severe asthma.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144324622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Verification of a Physiologically Based Pharmacokinetic Model of Furmonertinib and Its Main Metabolite for Drug–Drug Interaction Predictions","authors":"Yali Wu,&nbsp;Helena Leonie Hanae Loer,&nbsp;Yifan Zhang,&nbsp;Dafang Zhong,&nbsp;Yong Jiang,&nbsp;Jie Hu,&nbsp;Uwe Fuhr,&nbsp;Thorsten Lehr,&nbsp;Xingxing Diao","doi":"10.1002/psp4.70052","DOIUrl":"10.1002/psp4.70052","url":null,"abstract":"<p>Furmonertinib demonstrated potent efficacy as a newly developed tyrosine kinase inhibitor for the treatment of patients with epidermal growth factor receptor (EGFR) mutation-positive non-small cell lung cancer. In vitro research showed that furmonertinib is metabolized to its active metabolite AST5902 via the cytochrome P450 (CYP) enzyme CYP3A4. Furmonertinib is a strong CYP3A4 inducer, while the metabolite is a weaker CYP3A4 inducer. In clinical studies, nonlinear pharmacokinetics were observed during chronic dosing. The apparent clearance showed time- and dose-dependent increases. In this evaluation, a combination of in vitro data using radiolabeled compounds, clinical pharmacokinetic data, and drug–drug interaction (DDI) data of furmonertinib in oncology patients and/or in healthy subjects was used to develop a physiologically based pharmacokinetic (PBPK) model. The model was built in PK-Sim Version 11 using a total of 44 concentration-time profiles of furmonertinib and its metabolite AST5902. Suitability of the predictive model performance was demonstrated by both goodness-of-fit plots and statistical evaluation. The model predicted the observed monotherapy concentration profiles of furmonertinib well, with 32/32 predicted AUC_last (area under the curve until the last concentration measurement) values and 32/32 maximum plasma concentration (<i>C</i>_max) ratios being within twofold of the respective observed values. In addition, 8/8 predicted DDI AUC_last and <i>C</i>_max ratios with furmonertinib as a victim of CYP3A4 inhibition or induction were within twofold of their respective observed values. Potential applications of the final model include the prediction of DDIs for chronic administration of CYP3A4 perpetrators along with furmonertinib, considering auto-induction of furmonertinib and its metabolite AST5902.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 7","pages":"1273-1284"},"PeriodicalIF":3.1,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144309625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}