CPT: Pharmacometrics & Systems Pharmacology最新文献

{"title":"Development of a Heuristic Machine Analogy Method for Model Simplification With an Application to a Large-Scale Model of Gi/Gs Signaling","authors":"Liang Yang,&nbsp;David Finlay,&nbsp;Michelle Glass,&nbsp;Stephen Duffull","doi":"10.1002/psp4.70029","DOIUrl":"10.1002/psp4.70029","url":null,"abstract":"<p>Model simplification is a process to simplify large-scale mathematical models to enable easy applications such as simulation and parameter estimation. A novel heuristic machine analogy method of model simplification was developed and applied to a motivating example of a model for cAMP signaling switch induced by Gi/Gs pathway competition for the CB₁ receptor (consisting of 31 species and 76 parameters) to enable its use in estimation. The method first acquired an understanding of the mechanism by full model simulation, and then the mechanism was abstracted to a machine analogy. The machine analogy included signal start, signal mode selector, signal size regulator, and final effector, representing functions of different parts of the full model. The simplified minimal model (consisting of 11 species and 13 estimated parameters) was used for parameter estimation for Gi/Gs signaling of six CB₁ agonists. The results of the minimal model suggested that six CB₁ agonists have similar ratios of Gi/Gs activation, indicating Gi/Gs preference was more of a system effect rather than a ligand-specific effect. In conclusion, the novel machine analogy method can be used to heuristically simplify a larger-scale model while maintaining the important mechanisms. In the example here, the full Gi/Gs model of CB₁ was successfully simplified, and the results indicated Gi/Gs preference is a system-dependent effect.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 6","pages":"1098-1107"},"PeriodicalIF":3.0,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143982655","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}

{"title":"Model-Based Meta-Analysis of the Relationship Between Pioglitazone and Histological Outcomes in Metabolic Dysfunction-Associated Steatohepatitis Patients.","authors":"Quyen Thi Tran, Tham Thi Bui, Lien Thi Ngo, Bo Ram Yang, In-Hwan Baek, Van Hung Nguyen, Kyung Ae Lee, Hwi-Yeol Yun, Jung-Woo Chae, Soyoung Lee, Jae Hyun Kim, Woojin Jung","doi":"10.1002/psp4.70034","DOIUrl":"https://doi.org/10.1002/psp4.70034","url":null,"abstract":"<p><p>Given the high prevalence of the population who have metabolic dysfunction-associated steatohepatitis (MASH), interest is growing in MASH-targeted treatments. However, currently, there has been only one regulatory approved drug for MASH (Rezdiffra). Pioglitazone, a commonly used type 2 diabetes mellitus drug, is currently used off-label for the treatment of MASH. Our study aimed to perform a model-based meta-analysis to quantitatively examine the efficacy of pioglitazone in improving histological parameters and liver enzymes in patients with MASH. A comprehensive search was performed in Pubmed and clinicaltrials.gov. We collected histological outcomes (including steatosis, inflammation, ballooning, and fibrosis) and liver enzyme data. Due to sparse data, the gathered histological outcomes were used to generate virtual data. Next, model development for the virtual histological dataset was performed using a logistic model. In addition, Weibull and exponential models were tested to find the best fit for liver enzyme data. Model evaluations were carried out by visual predictive check, bootstrap method, and stacked bar plot. Eight studies with 540 patients were included. A logit model was used to analyze four outcomes. The results showed that using pioglitazone improved all four histological parameters. These effects are dose- and time-dependent under the Emax-time model for steatosis and ballooning, and under the linear relationship for inflammation and fibrosis. For liver enzymes, the Weibull model fitted well for both ALT and AST data. In conclusion, the developed models of pioglitazone may serve as a benchmark to assess the effectiveness of novel MASH-targeted treatments.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143972552","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":"Historical Use of Markov Model and Posterior Predictive Checks in Pharmacometrics","authors":"Pascal Girard,&nbsp;Helen Kastrissios","doi":"10.1002/psp4.70031","DOIUrl":"10.1002/psp4.70031","url":null,"abstract":"&lt;p&gt;We would like to congratulate the authors for their excellent “&lt;i&gt;Tutorial on pharmacometric Markov models&lt;/i&gt;” published in a recent issue of CPT-PSP [&lt;span&gt;1&lt;/span&gt;]. Their publication emphasizes the increasing use of such models in the field of pharmacometrics, is very complete, and presents in one single paper the theoretical framework of discret-time Markov model (DTMM), continuous-time Markov model, and Hidden Markov model.&lt;/p&gt;&lt;p&gt;However, by restricting their Pubmed search to &lt;b&gt;“&lt;/b&gt;Markov pharmacometric” the authors missed two important seminal papers in this field, both authored by Girard, Sheiner, Kastrissios and Blashke, related to the analysis of dosing regimen compliance (or adherence) data and population pharmacokinetic (pop-PK) modeling [&lt;span&gt;2, 3&lt;/span&gt;]. The first paper [&lt;span&gt;2&lt;/span&gt;] addressed via DTMM and pop-PK simulations the question of the loss of information (bias and precision) in pop-PK analysis when using partial information on patients' dose intakes before concentration measurements versus using the full dosing history as provided by an electronic device. The paper concluded that the use of a limited number of dose records (chosen based on an a priori estimate of the half-life of the drug) would be sufficient to get unbiased and precise PK parameter estimates. Interestingly, the sequence of dose intakes was simulated using a DTMM that was calibrated using real data from electronically monitored patients, which to our best knowledge is the first time a Markov model was used in the field of pharmacometrics.&lt;/p&gt;&lt;p&gt;For &lt;i&gt;p&lt;/i&gt;(&lt;i&gt;n&lt;/i&gt;), a Markov model was postulated and logits were derived with proper constraints for &lt;i&gt;n&lt;/i&gt; = 0, 1, or &gt; 1. The full log likelihood was derived, and parameter estimation was performed with the Laplacian method in NONMEM. The covariates, time of the day (morning, mid-day, evening), weekend days, and age were found to be significant. Figure 4 of that paper visualizes observed dosing patterns (corresponding to the number of times the pill bottle was opened by the patients) [&lt;span&gt;3&lt;/span&gt;] (reproduced here as Figure 1). Interestingly, it is quite similar to panels (a) and (b) of Figure 1 of the tutorial paper that shows a visualization to explore the Markovian features of a categorical response, although the latter paper goes one step further by showing a correlation plot of current versus previous response [&lt;span&gt;1&lt;/span&gt;].&lt;/p&gt;&lt;p&gt;The original work [&lt;span&gt;3&lt;/span&gt;] was published in a statistical journal and was written based on a statistical background rather than a pharmacometric one. However, it is an important reminder that our work [&lt;span&gt;2, 3&lt;/span&gt;] was supervised and guided by Prof Lewis B. Sheiner, a giant in the field of pharmacometrics, even before it became a newly coined discipline in 1982 [&lt;span&gt;4&lt;/span&gt;]. Coming back to our original published work [&lt;span&gt;3&lt;/span&gt;], it is also worth noting that this paper was the first time where pharmacometricians used the ‘posterior pr","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 5","pages":"817-818"},"PeriodicalIF":3.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802648","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}

{"title":"Synthetic Data in Healthcare and Drug Development: Definitions, Regulatory Frameworks, Issues","authors":"Giuseppe Pasculli,&nbsp;Marco Virgolin,&nbsp;Puja Myles,&nbsp;Anna Vidovszky,&nbsp;Charles Fisher,&nbsp;Elisabetta Biasin,&nbsp;Miranda Mourby,&nbsp;Francesco Pappalardo,&nbsp;Saverio D'Amico,&nbsp;Mario Torchia,&nbsp;Alexander Chebykin,&nbsp;Vincenzo Carbone,&nbsp;Luca Emili,&nbsp;Daniel Roeshammar","doi":"10.1002/psp4.70021","DOIUrl":"10.1002/psp4.70021","url":null,"abstract":"<p>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.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 5","pages":"840-852"},"PeriodicalIF":3.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70021","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"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,&nbsp;Seongho Kim,&nbsp;Jing Li","doi":"10.1002/psp4.70026","DOIUrl":"10.1002/psp4.70026","url":null,"abstract":"<p>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.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 5","pages":"864-880"},"PeriodicalIF":3.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70026","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing Open-Source Solutions: Insights From the First Open Systems Pharmacology (OSP) Community Conference","authors":"André Dallmann,&nbsp;Denise Feick,&nbsp;Pavel Balazki,&nbsp;Salih Benamara,&nbsp;Rolf Burghaus,&nbsp;Marylore Chenel,&nbsp;Siak-Leng Choi,&nbsp;Henrik Cordes,&nbsp;Mariana Guimarães,&nbsp;Abdullah Hamadeh,&nbsp;Ibrahim Ince,&nbsp;Kathleen M. Job,&nbsp;Tobias Kanacher,&nbsp;Andreas Kovar,&nbsp;Lars Kuepfer,&nbsp;Jörg Lippert,&nbsp;Julia Macente,&nbsp;Nina Nauwelaerts,&nbsp;Christoph Niederalt,&nbsp;Sheila Peters,&nbsp;Susana Proença,&nbsp;Masanobu Sato,&nbsp;Stephan Schaller,&nbsp;Jan Frederik Schlender,&nbsp;Annika Schneider,&nbsp;Erik Sjögren,&nbsp;Juri Solodenko,&nbsp;Alexander Staab,&nbsp;Paul Vrenken,&nbsp;Thomas Wendl,&nbsp;Wilhelmus E. A. de Witte,&nbsp;Donato Teutonico","doi":"10.1002/psp4.70028","DOIUrl":"10.1002/psp4.70028","url":null,"abstract":"<p>In 2017, the free and open-source software Open Systems Pharmacology (OSP) was launched. Since then, OSP has evolved from a small community into a diverse network of stakeholders committed to advancing open-source solutions for model-informed drug development (MIDD). In this context, the first OSP Community Conference was hosted by Novartis in Basel, Switzerland, on October 7–8, 2024, which gathered over 100 attendees from more than 40 institutions. This perspective synthesizes key insights from the conference.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 5","pages":"822-827"},"PeriodicalIF":3.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143771723","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}

{"title":"Using the Simcyp R Package for PBPK Simulation Workflows With the Simcyp Simulator","authors":"Anthonia M. Onasanwo,&nbsp;Naresh Mittapelly,&nbsp;Laura Shireman,&nbsp;Barry Vinden,&nbsp;Kevin McNally,&nbsp;James Craig,&nbsp;Frederic Y. Bois","doi":"10.1002/psp4.70022","DOIUrl":"10.1002/psp4.70022","url":null,"abstract":"<p>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.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 5","pages":"853-863"},"PeriodicalIF":3.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70022","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanistic Physiologically Based Pharmacokinetic Modeling of Dry Powder and Nebulized Formulations of Orally Inhaled TMEM16A Potentiator GDC-6988","authors":"Rui Zhu,&nbsp;Ian Sorrell,&nbsp;Fang Ma,&nbsp;Miaoran Ning,&nbsp;Yoen-Ju Son,&nbsp;Gaohong She,&nbsp;Tom De Bruyn,&nbsp;Joshua Galanter,&nbsp;Nastya Kassir,&nbsp;Ryan Owen,&nbsp;Masoud Jamei,&nbsp;Iain Gardner,&nbsp;Yuan Chen","doi":"10.1002/psp4.70027","DOIUrl":"10.1002/psp4.70027","url":null,"abstract":"<p>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 <i>C</i>_max 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).</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 6","pages":"1087-1097"},"PeriodicalIF":3.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70027","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Middle-Out Physiologically Based Pharmacokinetic Modeling to Support Pediatric Dosing Recommendation for Alectinib","authors":"Tamara van Donge,&nbsp;Elena Guerini,&nbsp;Amaury O'Jeanson,&nbsp;Neil Parrott,&nbsp;Clare Devlin,&nbsp;Cordula Stillhart,&nbsp;Nassim Djebli","doi":"10.1002/psp4.70020","DOIUrl":"10.1002/psp4.70020","url":null,"abstract":"<p>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.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 6","pages":"1077-1086"},"PeriodicalIF":3.0,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70020","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision Medicine in Oncology: Imatinib Dosing in the Obese Cancer Population Using Virtual Clinical Trials","authors":"Khairulanwar Burhanuddin,&nbsp;Afzal Mohammed,&nbsp;Nurul Afiqah Burhanuddin,&nbsp;Raj K. S. Badhan","doi":"10.1002/psp4.70018","DOIUrl":"10.1002/psp4.70018","url":null,"abstract":"<p>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 (C_min) 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. C_min 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 (&lt; 750 ng/mL); nevertheless, the differences are not statistically significant. TDM-guided dose adjustments improved C_min levels across BMI groups. For patients with C_min 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 C_min &lt; 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.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 6","pages":"1050-1064"},"PeriodicalIF":3.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70018","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}