CPT: Pharmacometrics & Systems Pharmacology最新文献

Historical Use of Markov Model and Posterior Predictive Checks in Pharmacometrics.

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-04-08 DOI: 10.1002/psp4.70031

Pascal Girard, Helen Kastrissios

引用次数: 0

Synthetic Data in Healthcare and Drug Development: Definitions, Regulatory Frameworks, Issues.

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-04-07 DOI: 10.1002/psp4.70021

Giuseppe Pasculli, Marco Virgolin, Puja Myles, Anna Vidovszky, Charles Fisher, Elisabetta Biasin, Miranda Mourby, Francesco Pappalardo, Saverio D'Amico, Mario Torchia, Alexander Chebykin, Vincenzo Carbone, Luca Emili, Daniel Roeshammar

{"title":"Synthetic Data in Healthcare and Drug Development: Definitions, Regulatory Frameworks, Issues.","authors":"Giuseppe Pasculli, Marco Virgolin, Puja Myles, Anna Vidovszky, Charles Fisher, Elisabetta Biasin, Miranda Mourby, Francesco Pappalardo, Saverio D'Amico, Mario Torchia, Alexander Chebykin, Vincenzo Carbone, Luca Emili, Daniel Roeshammar","doi":"10.1002/psp4.70021","DOIUrl":"https://doi.org/10.1002/psp4.70021","url":null,"abstract":"With the recent and evolving regulatory frameworks regarding the usage of Artificial Intelligence (AI) in both drug and medical device development, the differentiation between data derived from observed ('true' or 'real') sources and artificial data obtained using process-driven and/or (data-driven) algorithmic processes is emerging as a critical consideration in clinical research and regulatory discourse. We conducted a critical literature review that revealed evidence of the current ambivalent usage of the term \"synthetic\" (along with derivative terms) to refer to \"true/observed\" data in the context of clinical trials and AI-generated data (or \"artificial\" data). This paper, stemming from a critical evaluation of different perspectives captured from the scientific literature and recent regulatory endeavors, seeks to elucidate this distinction, exploring their respective utilities, regulatory stances, and upcoming needs, as well as the potential for both data types in advancing medical science and therapeutic development.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802649","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

SpatialCNS-PBPK: An R/Shiny Web-Based Application for Physiologically Based Pharmacokinetic Modeling of Spatial Pharmacokinetics in the Human Central Nervous System and Brain Tumors.

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-04-04 DOI: 10.1002/psp4.70026

Charuka D Wickramasinghe, Seongho Kim, Jing Li

{"title":"SpatialCNS-PBPK: An R/Shiny Web-Based Application for Physiologically Based Pharmacokinetic Modeling of Spatial Pharmacokinetics in the Human Central Nervous System and Brain Tumors.","authors":"Charuka D Wickramasinghe, Seongho Kim, Jing Li","doi":"10.1002/psp4.70026","DOIUrl":"https://doi.org/10.1002/psp4.70026","url":null,"abstract":"Quantitative understanding of drug penetration and exposure in the human central nervous system (CNS) and brain tumors is essential for the rational development of new drugs and optimal use of existing drugs for brain cancer. To address this need, we developed and validated a novel 9-compartment permeability-limited CNS (9-CNS) physiologically based pharmacokinetic (PBPK) model, enabling mechanistic and quantitative prediction of spatial pharmacokinetics for systemically administered small-molecule drugs across different regions of the human brain, cerebrospinal fluid, and brain tumors. To make the 9-CNS model accessible to a broad range of users, we developed the SpatialCNS-PBPK app, a user-friendly, web-based R/Shiny platform built with R and Shiny programming. The app provides key functionalities for model simulation, sensitivity analysis, and pharmacokinetic parameter calculation. This tutorial introduces the development and evaluation of the SpatialCNS-PBPK app, highlights its key features and functions, and provides a step-by-step user guide for practical applications. By enhancing our ability to predict the spatial pharmacokinetics of anticancer drugs in the human CNS and brain tumors, the SpatialCNS-PBPK app serves as an invaluable computational tool and data-driven approach for advancing drug development and optimizing treatment strategies for more effective treatment of brain cancer.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143779362","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

Harnessing Open-Source Solutions: Insights From the First Open Systems Pharmacology (OSP) Community Conference.

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-04-03 DOI: 10.1002/psp4.70028

André Dallmann, Denise Feick, Pavel Balazki, Salih Benamara, Rolf Burghaus, Marylore Chenel, Siak-Leng Choi, Henrik Cordes, Mariana Guimarães, Abdullah Hamadeh, Ibrahim Ince, Kathleen M Job, Tobias Kanacher, Andreas Kovar, Lars Kuepfer, Jörg Lippert, Julia Macente, Nina Nauwelaerts, Christoph Niederalt, Sheila Peters, Susana Proença, Masanobu Sato, Stephan Schaller, Jan Frederik Schlender, Annika Schneider, Erik Sjögren, Juri Solodenko, Alexander Staab, Paul Vrenken, Thomas Wendl, Wilhelmus E A de Witte, Donato Teutonico

引用次数: 0

Using the Simcyp R Package for PBPK Simulation Workflows With the Simcyp Simulator.

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-04-03 DOI: 10.1002/psp4.70022

Anthonia M Onasanwo, Naresh Mittapelly, Laura Shireman, Barry Vinden, Kevin McNally, James Craig, Frederic Y Bois

{"title":"Using the Simcyp R Package for PBPK Simulation Workflows With the Simcyp Simulator.","authors":"Anthonia M Onasanwo, Naresh Mittapelly, Laura Shireman, Barry Vinden, Kevin McNally, James Craig, Frederic Y Bois","doi":"10.1002/psp4.70022","DOIUrl":"https://doi.org/10.1002/psp4.70022","url":null,"abstract":"Physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) modeling aims to understand how a drug is absorbed, distributed, metabolized, excreted, and acts in a human or animal body. The Simcyp Simulator is a well-known commercial PBPK/PD simulation software offering many options. It can, for example, use multiple compounds and population specification files to study the behavior of specific drug formulations in particular population groups. Such features can greatly speed up and optimize clinical research and drug development studies. On the other hand, the statistical coding language R offers benefits, such as easy scripting, automation, flexible statistical analyses, and visualization of results. These benefits can be applied to PBPK modeling. We describe here version 23.0.64 of an R software package that can run the Simcyp Simulator from R, changing its inputs and processing its outputs. We detail the implementation of two automated workflows for model development and verification. The first demonstrates the verification of a drug-drug interaction model for Atazanavir, an antiretroviral drug indicated for the treatment of HIV/AIDS. The second is applied to the virtual bioequivalence assessment of paliperidone palmitate long-acting injectable suspensions. We show how simulations that could take days otherwise can be executed, analyzed, and displayed in a matter of hours.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143779363","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

Mechanistic Physiologically Based Pharmacokinetic Modeling of Dry Powder and Nebulized Formulations of Orally Inhaled TMEM16A Potentiator GDC-6988.

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-04-01 DOI: 10.1002/psp4.70027

Rui Zhu, Ian Sorrell, Fang Ma, Miaoran Ning, Yoen-Ju Son, Gaohong She, Tom De Bruyn, Joshua Galanter, Nastya Kassir, Ryan Owen, Masoud Jamei, Iain Gardner, Yuan Chen

{"title":"Mechanistic Physiologically Based Pharmacokinetic Modeling of Dry Powder and Nebulized Formulations of Orally Inhaled TMEM16A Potentiator GDC-6988.","authors":"Rui Zhu, Ian Sorrell, Fang Ma, Miaoran Ning, Yoen-Ju Son, Gaohong She, Tom De Bruyn, Joshua Galanter, Nastya Kassir, Ryan Owen, Masoud Jamei, Iain Gardner, Yuan Chen","doi":"10.1002/psp4.70027","DOIUrl":"https://doi.org/10.1002/psp4.70027","url":null,"abstract":"The orally inhaled route of administration for respiratory indications can maximize drug exposure to the site of action (lung) to increase efficacy while minimizing systemic exposure to achieve an improved safety profile. However, due to the difficulty of taking samples from different regions of the human lung, often only systemic pharmacokinetic (PK) samples are taken and assumed to be reflective of the lung PK of the compound, which may not always be the case. In this study, a mechanistic lung physiologically based pharmacokinetic (PBPK) model was built using a middle-out approach (i.e., combining elements of bottom-up prediction and using clinical data to inform some model parameters) to predict plasma and lung PK of an orally inhaled TMEM16A potentiator GDC-6988 in humans. The lung PBPK model accounted for lung deposition, lung and oral absorption, systemic clearance, and tissue distribution. The model was refined using data from a Phase 1b study with dry powder (DP) formulation and was also verified using data from a Phase 1 study with a nebulized (Neb) formulation. The refined model adequately captures the observed GDC-6988 plasma PK profiles in both the DP and Neb studies and allows prediction of the regional lung fluid and tissue concentrations. The sensitivity analyses showed that the systemic Cmax depended on the ratio of airway to alveolar deposition, but this did not impact the AUC. This novel mechanistic lung PBPK modeling framework could be applied to predict plasma and regional lung exposure and inform the early clinical development of inhaled molecules (e.g., dose selection).","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763266","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

Middle-Out Physiologically Based Pharmacokinetic Modeling to Support Pediatric Dosing Recommendation for Alectinib.

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-03-30 DOI: 10.1002/psp4.70020

Tamara van Donge, Elena Guerini, Amaury O'Jeanson, Neil Parrott, Clare Devlin, Cordula Stillhart, Nassim Djebli

{"title":"Middle-Out Physiologically Based Pharmacokinetic Modeling to Support Pediatric Dosing Recommendation for Alectinib.","authors":"Tamara van Donge, Elena Guerini, Amaury O'Jeanson, Neil Parrott, Clare Devlin, Cordula Stillhart, Nassim Djebli","doi":"10.1002/psp4.70020","DOIUrl":"https://doi.org/10.1002/psp4.70020","url":null,"abstract":"Adult patients with anaplastic lymphoma kinase positive (ALK+) advanced non-small-cell lung cancer (NSCLC) are treated with 600 mg alectinib twice daily (BID) as first-line treatment. ALK positive solid and central nervous system (CNS) tumors are described in the pediatric population, with limited clinical data due to the rarity of the disease and challenges to determine the right dosing. This study aims to inform pediatric dose recommendations for alectinib by performing a middle-out physiologically based pharmacokinetic (PBPK) modeling approach, accounting for differences in absorption and enzyme maturation. The developed adult PBPK model is leveraging insights from two previously developed PBPK models (focusing on absorption and drug-drug interactions) and is complemented with newly generated data. The adult PBPK model is validated with pharmacokinetic data from two clinical studies in the adult population. The ratios between the predicted and observed steady-state AUC after 600 mg BID for 28 days are within the acceptable range in three different adult body weight categories (from 0.81 to 1.02). Initial pediatric dose recommendations are informed by population PK model predictions (assuming no maturation of enzymes) and aim to have similar exposure to the adult population. Intrinsic clearance values for all contributing CYP enzymes are included in the pediatric PBPK model to account for changes in enzyme maturation. The current PBPK model confirmed that the recommended alectinib doses by population PK predictions were accurate for the pediatric age range, with one exception: patients younger than 3.5 years are suggested to receive 100 mg BID, instead of 190 mg BID.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143751474","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

Precision Medicine in Oncology: Imatinib Dosing in the Obese Cancer Population Using Virtual Clinical Trials.

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-03-27 DOI: 10.1002/psp4.70018

Khairulanwar Burhanuddin, Afzal Mohammed, Nurul Afiqah Burhanuddin, Raj K S Badhan

{"title":"Precision Medicine in Oncology: Imatinib Dosing in the Obese Cancer Population Using Virtual Clinical Trials.","authors":"Khairulanwar Burhanuddin, Afzal Mohammed, Nurul Afiqah Burhanuddin, Raj K S Badhan","doi":"10.1002/psp4.70018","DOIUrl":"https://doi.org/10.1002/psp4.70018","url":null,"abstract":"This study investigates the impact of obesity on imatinib pharmacokinetics in cancer patients by utilizing physiologically based pharmacokinetic modeling (PBPK) and virtual clinical trial approaches and evaluates the effectiveness of therapeutic drug monitoring (TDM)-guided dose adjustment to recover the imatinib trough concentration (Cmin) into the target concentration. PBPK models were validated against clinical data from lean, overweight, and obese cancer populations. Simulations revealed significant physiological differences across body-mass-index categories, including higher body surface area, liver weight, and cardiac output in obese individuals, coupled with lower CYP3A4 enzyme activity and hematocrit levels, which translated into pharmacokinetic differences. Obese patients exhibited significantly lower imatinib maximum concentration and area-under-the-curve values. Cmin levels, a key determinant of therapeutic response, were consistently lower in the obese cohort, with a greater proportion of individuals falling below the subtherapeutic threshold (< 750 ng/mL); nevertheless, the differences are not statistically significant. TDM-guided dose adjustments improved Cmin levels across BMI groups. For patients with Cmin between 450 and 750 ng/mL, dose increases of 1.5-2.0 times effectively restored levels to the target range (750-1500 ng/mL). However, individuals with Cmin < 450 ng/mL often failed to achieve therapeutic levels, suggesting limited benefit from further dose escalation and a need for alternative therapies. This study underscores the importance of PBPK modeling and TDM in tailoring imatinib therapy for obese cancer patients by addressing physiological differences and optimizing dosing strategies for better outcomes.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143728837","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

Correction to “Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations”

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-03-26 DOI: 10.1002/psp4.70024

{"title":"Correction to “Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations”","authors":"","doi":"10.1002/psp4.70024","DOIUrl":"10.1002/psp4.70024","url":null,"abstract":"\u0000 Valderrama, D., Teplytska, O., Koltermann, L.M., Trunz, E., Schmulenson, E., Fritsch, A., Jaehde, U. and Fröhlich, H. (2025), Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations. CPT Pharmacometrics Syst Pharmacol. https://doi.org/10.1002/psp4.13313\u0000 In the published version of the above article, we noticed an inaccuracy in Figures 2 and 3 (the goodness-of-fit plots) and the corresponding Tables 3 and S3.Goodness-of-fit plots illustrate how well a model fits the data by plotting individual predictions (IPRED) (or just predictions) against the actual observations. Upon further discussion, we realized that the IPRED shown for the Multimodal Scientific Machine Learning model were derived from maximum-a posteriori (MAP) individual parameter estimation and those shown for the population pharmacokinetic (PopPK) models were pure simulations, that did not make use of the concentrations in the test data.Although we stated this truthfully in our Methods section, we now realize that this comparison can be misleading. We have therefore replaced the goodness-of-fit plots for the PopPK model with more appropriate plots representing IPRED after MAP estimation and added the corresponding metrics to our Tables 3 and S3.The conclusions of our article are not affected by this correction.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 4","pages":"807-811"},"PeriodicalIF":3.1,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708957","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}

引用次数: 0

Pharmacokinetic Model-Informed Precision Dosing of Natalizumab in Multiple Sclerosis.

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-03-20 DOI: 10.1002/psp4.70014

Stefan P H van den Berg, Alyssa A Toorop, Femke Hooijberg, Gertjan Wolbink, Nivea M F Voelkner, Liza M Y Gelissen, Joep Killestein, Zoé L E van Kempen, Thomas P C Dorlo, Theo Rispens

{"title":"Pharmacokinetic Model-Informed Precision Dosing of Natalizumab in Multiple Sclerosis.","authors":"Stefan P H van den Berg, Alyssa A Toorop, Femke Hooijberg, Gertjan Wolbink, Nivea M F Voelkner, Liza M Y Gelissen, Joep Killestein, Zoé L E van Kempen, Thomas P C Dorlo, Theo Rispens","doi":"10.1002/psp4.70014","DOIUrl":"https://doi.org/10.1002/psp4.70014","url":null,"abstract":"Intravenous natalizumab is an effective treatment for relapsing-remitting multiple sclerosis. However, the standard treatment interval of 4 weeks may be excessive for many patients. Personalized interval extension using therapeutic drug monitoring (TDM) can result in adequate drug exposure while reducing hospital visits and healthcare costs. Here, we investigate to which extent TDM-guided personalized dosing can benefit from model-informed precision dosing (MIPD). Individual posterior PK estimates were derived using patient weight and two trough concentrations at the standard dose interval by Bayesian estimation using a newly developed population PK model. MIPD was compared to a previously deployed TDM-guided stratified personalized dosing protocol (SPD) using a decision tree to personalize dosing intervals. Accuracy (mean prediction error) of the predicted dosing intervals was 4.8% versus 24% for model-informed estimates versus decision tree, respectively, when aiming for a 10 μg/mL trough concentration, and 4.8% versus 86% when aiming for 5 μg/mL. Corresponding precision (root mean square error) was 2.3 versus 4.0, and 1.5 versus 5 μg/mL. Finally, we evaluated the feasibility of a MIPD approach to attain a therapeutic trough of 2 μg/mL. Simulating MIPD showed a reduction in the average infusions versus the standard interval by 40%, with an average dose interval of 7 weeks, while maintaining adequate drug exposure. MIPD was concluded to be superior to the conventional TDM-guided personalized dosing approach in terms of enhanced precision in individual dose interval selection, enabling more efficient interval extensions. Simulations supported the clinical deployment of natalizumab MIPD.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143662980","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