CPT: Pharmacometrics & Systems Pharmacology最新文献_第4页

A Pharmacometrics-Informed Trial Simulation Framework for Optimizing Study Designs for Disease-Modifying Treatments in Rare Neurological Disorders. 一种基于药物计量学的试验模拟框架，用于优化罕见神经系统疾病改善治疗的研究设计。

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-08-05 DOI: 10.1002/psp4.70082

Yevgen Ryeznik, Ralf-Dieter Hilgers, Nicole Heussen, Emmanuelle Comets, France Mentré, Niels Hendrickx, Mats O Karlsson, Andrew C Hooker, Alzahra Hamdan, Xiaomei Chen, Rebecca Schüle, Matthis Synofzik, Oleksandr Sverdlov

{"title":"A Pharmacometrics-Informed Trial Simulation Framework for Optimizing Study Designs for Disease-Modifying Treatments in Rare Neurological Disorders.","authors":"Yevgen Ryeznik, Ralf-Dieter Hilgers, Nicole Heussen, Emmanuelle Comets, France Mentré, Niels Hendrickx, Mats O Karlsson, Andrew C Hooker, Alzahra Hamdan, Xiaomei Chen, Rebecca Schüle, Matthis Synofzik, Oleksandr Sverdlov","doi":"10.1002/psp4.70082","DOIUrl":"https://doi.org/10.1002/psp4.70082","url":null,"abstract":"The development of new treatments for rare neurological diseases (RNDs) may be very challenging due to limited natural history data, lack of relevant biomarkers and clinical endpoints, small and heterogeneous patient populations, and other complexities. A systematic approach is needed for comparing various design and analysis strategies to identify \"optimal\" approaches for a clinical trial in a chosen RND with the given resource constraints. For this purpose, we propose a pharmacometrics-informed clinical scenario evaluation framework (CSE-PMx), which includes some important research hallmarks relevant to RND clinical trials: a disease progression model for simulating individual longitudinal outcomes, the choice of a suitable randomization method for trial design, and an option to perform subsequent statistical analysis with randomization tests. We illustrate the utility of CSE-PMx for an exemplary randomized trial to compare the disease-modifying effect of an experimental treatment versus control in patients with Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS). In the considered example, our simulation evidence suggests that a nonlinear mixed-effects model (NLMEM) with a population-based likelihood ratio test analysis is valid, robust, and more powerful than some conventional methods such as two-sample t-test, analysis of covariance (ANCOVA), or a mixed model with repeated measurements (MMRM). Our proposed framework is very flexible and generalizable to clinical research in other rare disease indications.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144788477","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}

引用次数: 0

Model-Informed Precision Dosing of Infliximab in Korean Inflammatory Bowel Disease Patients: External Validation of Population Pharmacokinetic Models. 韩国炎症性肠病患者英夫利昔单抗基于模型的精确剂量：群体药代动力学模型的外部验证。

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-08-04 DOI: 10.1002/psp4.70089

Yoonjin Kim, Seung Hwan Baek, In-Jin Jang, Jae-Yong Chung

{"title":"Model-Informed Precision Dosing of Infliximab in Korean Inflammatory Bowel Disease Patients: External Validation of Population Pharmacokinetic Models.","authors":"Yoonjin Kim, Seung Hwan Baek, In-Jin Jang, Jae-Yong Chung","doi":"10.1002/psp4.70089","DOIUrl":"https://doi.org/10.1002/psp4.70089","url":null,"abstract":"Underexposure to infliximab often leads to loss of response in patients with inflammatory bowel disease (IBD). Model-informed precision dosing (MIPD) offers a superior approach to maintaining target infliximab concentrations compared to empirical dosage adjustment. This study aims to externally validate the population pharmacokinetic (PK) models implemented in TDMx, an online MIPD dashboard system, for adult and pediatric Korean IBD patients before clinical use. This retrospective study included 199 IBD patients (142 adults, 57 children) treated with intravenous infliximab at Seoul National University Hospital (Seoul, Republic of Korea) from 2019 to 2023. Three adult and seven pediatric models were evaluated based on accuracy, precision, goodness of fit plots, prediction-corrected visual predictive checks, and normalized prediction distribution errors. For adults, the Passot model showed the best fit (mean percentage error (MPE) 26.4%, mean absolute error (MAE) 1.1 mg/L, relative root-mean square error (rRMSE) 159.0%), whereas all pediatric models were unsuitable for clinical use (MPE 30.4%-143.4%, MAE 1.4-2.6 mg/L, rRMSE 96.3%-564.0%). Predictive performance was compared between datasets with or without accurate information on antibodies-toward-infliximab (ATI), as well as with and without previous concentrations. Assuming all patients were ATI positive improved predictive performance, likely due to the inherent positive bias of the population PK models. Incorporating previous concentrations improved predictions for adult models, achieving acceptable accuracy and precision (Passot model: MPE 17.5%, MAE 1.8 mg/L, rRMSE 80.3% with one concentration). However, pediatric models remained clinically unacceptable, highlighting the need to develop models specifically tailored for this population.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144774862","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}

引用次数: 0

Transforming Pediatric Rare Disease Drug Development: Enhancing Clinical Trials and Regulatory Evidence With Virtual Patients. 转变儿科罕见病药物开发：加强虚拟患者的临床试验和监管证据。

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-08-04 DOI: 10.1002/psp4.70096

Fianne Sips, Marco Virgolin, Giuseppe Pasculli, Federico Reali, Alessio Paris, Annette Janus, Yann Godfrin, Daniel Röshammar, Luca Marchetti, Jane Knöchel

引用次数: 0

Assessing Cytochrome P450 Drug Interaction Risk for Dordaviprone Using Physiologically Based Pharmacokinetic Modeling. 利用基于生理的药代动力学模型评估Dordaviprone患者的细胞色素P450药物相互作用风险。

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-08-04 DOI: 10.1002/psp4.70093

Swati Jaiswal, Nikunjkumar K Patel, Hannah M Jones, Savannah McFeely, Shamia L Faison, Tim Tippin, Odin Naderer

{"title":"Assessing Cytochrome P450 Drug Interaction Risk for Dordaviprone Using Physiologically Based Pharmacokinetic Modeling.","authors":"Swati Jaiswal, Nikunjkumar K Patel, Hannah M Jones, Savannah McFeely, Shamia L Faison, Tim Tippin, Odin Naderer","doi":"10.1002/psp4.70093","DOIUrl":"https://doi.org/10.1002/psp4.70093","url":null,"abstract":"A physiologically based pharmacokinetic (PBPK) model was developed and verified for dordaviprone, a small molecule with antitumor effects in glioma patients. The model was applied to assess the drug-drug interaction (DDI) potential of dordaviprone as a victim of CYP3A4 inhibitors and inducers, and as a perpetrator of CYP3A4, CYP2C8, CYP2D6 inhibition. A combination of in vitro and clinical data was used to develop a minimal distribution PBPK model with a single adjusting compartment and mechanistic absorption using the Simcyp Population-Based Simulator (V21). Simulated maximum concentration (Cmax) and area under the concentration time curve (AUC) of the 3 clinical studies used to verify the PBPK model were within 1.4-fold of observed exposures. The simulated increase in dordaviprone AUC and Cmax (4.6- and 1.7-fold) following administration of multiple doses of itraconazole was consistent with the observed values (4.4- and 1.9-fold). All PBPK-simulated changes in dordaviprone plasma exposure when administered with CYP3A4 moderate (erythromycin, fluconazole) and weak (cimetidine) inhibitors, and moderate (efavirenz) and strong (rifampicin) inducers were consistent with their CYP3A4 potency classification (AUC ratio = 2.68, 2.48, 1.42, 0.35, and 0.17, respectively). The simulated AUC and Cmax of probe substrates for CYP3A4 (midazolam), CYP2C8 (repaglinide) and CYP2D6 (desipramine) after coadministration with 625 mg dordaviprone were the same as those in the absence of dordaviprone (ratio = 1.0) and remained unchanged after a sensitivity analysis using 10-fold more potent inhibition constants. Due to changes in dordaviprone plasma exposure when co-administered with CYP3A4 inhibitors, dordaviprone dose adjustments may be necessary; CYP3A4 inducers should be avoided.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144774861","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}

引用次数: 0

Pharmacometric Model-Based Sample Size Allocation for a Region of Interest in a Multi-Regional Phase 2 Trial: A Case Study of an Anti-Psoriatic Drug. 基于药物计量学模型的多区域2期试验中感兴趣区域的样本量分配：一种抗银屑病药物的案例研究

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-08-01 DOI: 10.1002/psp4.70090

Xiao Zhang, Yubo Xiao, Junyi Wu, Scott Marshall, Xuan Zhou

{"title":"Pharmacometric Model-Based Sample Size Allocation for a Region of Interest in a Multi-Regional Phase 2 Trial: A Case Study of an Anti-Psoriatic Drug.","authors":"Xiao Zhang, Yubo Xiao, Junyi Wu, Scott Marshall, Xuan Zhou","doi":"10.1002/psp4.70090","DOIUrl":"https://doi.org/10.1002/psp4.70090","url":null,"abstract":"Phase 2 trials have historically focused on characterizing the dose-exposure-response relationship in relatively homogeneous patient populations before proceeding to confirmatory trials. However, with the rise of multi-regional Phase 2 trials, it is important to strike a balance between this goal and the requirement to make sure that the optimal doses are chosen for patients from various geographic areas. This study uses a dose-ranging trial for an anti-psoriatic drug, featuring a typical design with a total sample size of N = 175, to highlight key considerations regarding sample size in multi-regional exploratory studies. The allocation of sample size to a region of interest (Region X) was evaluated using both a conventional statistical approach and a pharmacometric model-based (PMx) approach, predicated on the assumption of a minimum treatment improvement in Region X. Further evaluation was performed to assess the probability of reaching reliable conclusions regarding clinically relevant inter-regional differences in treatment response. The statistical approach, relying solely on end-of-trial observations from a single dose group, exhibited a maximum power of less than 40% in detecting treatment differences across regions when Region X accounts for 50% of the total sample size. In contrast, the PMx approach, employing data from multiple dose groups across trial duration, demonstrated that 26% of the total sample size yielded over 80% power to identify the inter-regional difference. The PMx approach has also been shown to offer a more efficient characterization of the clinical relevance of inter-regional differences, and has potential to improve decision-making in development progression by integrating prior knowledge.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144764744","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}

引用次数: 0

Moving From Point-Based Analysis to Systems-Based Modeling: Knowledge Integration to Address Antimicrobial Resistance. 从基于点的分析到基于系统的建模：解决抗菌素耐药性的知识整合。

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-08-01 DOI: 10.1002/psp4.70092

Bhavatharini Arun, Gauri G Rao

引用次数: 0

The Basel Modeling and Simulation Seminar: 20 Editions of Fostering Local Exchange in Pharmacometrics. 巴塞尔建模和模拟研讨会：促进药物计量学本地交流的20个版本。

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-07-31 DOI: 10.1002/psp4.70091

Tamara van Donge, João A Abrantes, Kai Grosch, Gilbert Koch, Meemansa Sood, Britta Steffens, Andreas Krause

引用次数: 0

PBPK Modeling to Predict Clinical Drug-Drug Interaction and Impact of Hepatic Impairment for an ADC With the Payload Auristatin F-Hydroxypropylamide. PBPK模型预测临床药物-药物相互作用和ADC与有效载荷Auristatin f -羟丙酰胺肝损害的影响。

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-07-31 DOI: 10.1002/psp4.70088

Niyanta Kumar, Vaishali Dixit, Howard Burt, Katherine L Gill, Hannah M Jones, Natalie Keirstead, Dian Su, Dorin Toader, Timothy B Lowinger

{"title":"PBPK Modeling to Predict Clinical Drug-Drug Interaction and Impact of Hepatic Impairment for an ADC With the Payload Auristatin F-Hydroxypropylamide.","authors":"Niyanta Kumar, Vaishali Dixit, Howard Burt, Katherine L Gill, Hannah M Jones, Natalie Keirstead, Dian Su, Dorin Toader, Timothy B Lowinger","doi":"10.1002/psp4.70088","DOIUrl":"https://doi.org/10.1002/psp4.70088","url":null,"abstract":"Upifitamab rilsodotin-an antibody drug conjugate (ADC)-comprises a NaPi2b-targeted antibody conjugated to an auristatin-based payload (auristatin F-hydroxypropylamide [AF-HPA]). AF-HPA is metabolized by cytochrome P450 3A4 (CYP3A4) and, to a lower extent, by CYP3A5 and demonstrates both reversible and time-dependent inhibition of CYP3A4. AF-HPA is also a P-glycoprotein (P-gp) substrate. A PBPK model was developed using a mixed \"bottom-up\" and \"top-down\" modeling approach with a combination of in vitro, nonclinical, and clinical ADME/PK data. The model recapitulated the clinical PK of conjugated and unconjugated AF-HPA. Simulations were used to predict the potential of unconjugated AF-HPA to be a victim or perpetrator of clinical drug-drug interactions (DDI) and predict the impact of hepatic impairment on the exposure to unconjugated AF-HPA. Simulations suggested negligible potential for clinical DDI between unconjugated AF-HPA and CYP3A substrates. Simulations also showed ~30% increase in unconjugated AF-HPA exposure following an IV dose of 36 mg/m2 in the presence of itraconazole, an inhibitor of both CYP3A4 and P-gp. A negligible change in the exposure to unconjugated AF-HPA was predicted in patients with mild hepatic impairment, which aligned with observed clinical data. The model predicted a ~1.5-fold increase in unconjugated AF-HPA AUC and negligible change in the Cmax in patients with moderate and severe hepatic impairment. Finally, this PBPK model may be applied (with modification to the conjugated drug sub-model parameters) to predict DDI and hepatic impairment potential for other ADCs with the same linker and payload.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144759398","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}

引用次数: 0

Machine Learning-Based Model Selection and Averaging Outperform Single-Model Approaches for a Priori Vancomycin Precision Dosing. 基于机器学习的模型选择和平均优于单模型方法的先验万古霉素精确给药。

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-07-30 DOI: 10.1002/psp4.70084

Wisse van Os, Amaury O'Jeanson, Carla Troisi, Chun Liu, Jordan T Brooks, Jasmine H Hughes, Dominic M H Tong, Ron J Keizer

{"title":"Machine Learning-Based Model Selection and Averaging Outperform Single-Model Approaches for a Priori Vancomycin Precision Dosing.","authors":"Wisse van Os, Amaury O'Jeanson, Carla Troisi, Chun Liu, Jordan T Brooks, Jasmine H Hughes, Dominic M H Tong, Ron J Keizer","doi":"10.1002/psp4.70084","DOIUrl":"https://doi.org/10.1002/psp4.70084","url":null,"abstract":"Selecting an appropriate population pharmacokinetic (PK) model for individual patients in model-informed precision dosing (MIPD) can be challenging, particularly in the absence of therapeutic drug monitoring (TDM) samples. We developed a machine learning (ML) model to guide individualized PK model selection for a priori MIPD of vancomycin based on routinely recorded patient characteristics. This retrospective analysis included 343,636 vancomycin TDM records, each from a distinct adult patient across 156 healthcare centers, along with a priori predictions from six PK models. A multi-label classification approach was applied, labeling PK model predictions based on whether they fell within 80%-125% of observed TDM values. Various modeling strategies were evaluated using XGBoost as the base algorithm, with binary relevance selected for the final model. At the prediction stage, PK models were ranked and averaged for each patient based on ML-predicted probabilities that predictions would fall within 80%-125% of the observed concentration. Selecting the highest ranked PK model for each patient and ML-based model averaging outperformed all single PK models, body mass index-based selection, and naive averaging. On a population level, these ML approaches resulted in more accurate predictions, a higher proportion of predictions within 80%-125% of observed vancomycin concentrations, and no systematic bias. Predictive performance declined with lower ML-assigned rankings, and selecting the lowest-ranked PK model for each patient resulted in worse performance than the worst-performing single PK model. By guiding the selection of appropriate models and avoiding less suitable ones, ML approaches for a priori MIPD may improve early dosing decisions.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144741441","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}

引用次数: 0

Optimizing First-Line Therapeutics in Non-Small Cell Lung Cancer: Insights From Joint Modeling and Large-Scale Data Analysis. 优化非小细胞肺癌的一线治疗：来自联合建模和大规模数据分析的见解。

IF 3 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-07-29 DOI: 10.1002/psp4.70079

Benjamin K Schneider, Sebastien Benzekry, Jonathan P Mochel

{"title":"Optimizing First-Line Therapeutics in Non-Small Cell Lung Cancer: Insights From Joint Modeling and Large-Scale Data Analysis.","authors":"Benjamin K Schneider, Sebastien Benzekry, Jonathan P Mochel","doi":"10.1002/psp4.70079","DOIUrl":"https://doi.org/10.1002/psp4.70079","url":null,"abstract":"Non-small cell lung cancer (NSCLC) is often intrinsically resistant to several first- and second-line therapeutics and can rapidly acquire further resistance after a patient begins treatment. Treatment outcomes are, therefore, significantly impacted by the optimization of scheduling. Previous preclinical research has suggested that scheduling bevacizumab sequentially with combination antiproliferatives could improve clinical outcomes. Mathematical modeling is a well-suited tool for investigating this proposed modification. To address this critical need, individual patient tumor data from 11 clinical trials in NSCLC have been collated and used to develop a semi-mechanistic model of NSCLC growth and response to the therapeutics represented in those trials. Precise estimates of clinical parameters fundamental to cancer modeling have been produced, such as the rate of acquired resistance to various pharmaceuticals, the relationship between drug concentration and cancer cell death, as well as the fine dynamics of vascular remodeling in response to bevacizumab. In a reserved portion of the dataset, this model predicted the efficacy of individual treatment time courses with an average difference between final prediction and observation of 59.7% after a single tumor measurement and 11.7% after three successive tumor measurements. A delay of 9.6 h between pemetrexed-cisplatin and bevacizumab administration is predicted to optimize the benefit of sequential administration. At this gap, approximately 93.5% of simulated patients benefited from a gap in administration compared with concomitant administration. Of those simulated patients, the mean improvement in tumor reduction was 20.7%. This suggests that scheduling a modest gap between the administration of bevacizumab and partner antiproliferatives could meaningfully improve patient outcomes in NSCLC.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144741442","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}

引用次数: 0