CPT: Pharmacometrics & Systems Pharmacology最新文献

{"title":"Development of an Agent-Based Model to Investigate Durability of Factor IX Activity in Hemophilia B Patients Treated With Etranacogene Dezaparvovec","authors":"Yuezhe Li,&nbsp;Partha Nandy,&nbsp;Eric Jordie,&nbsp;Timothy Knab,&nbsp;Karsten Peppel,&nbsp;Daniel C. Kirouac,&nbsp;A. Katharina Wilkins,&nbsp;Silpa Nuthalapati","doi":"10.1002/psp4.70286","DOIUrl":"https://doi.org/10.1002/psp4.70286","url":null,"abstract":"<p>Many currently approved gene therapies use adeno-associated virus (AAV) to deliver DNA sequences encoding protein(s)-of-interest into cells. The AAV viral genome forms stable, circular DNA structures called episomes after entering the nuclei. Therapeutic proteins are then generated in vivo from transcription and translation of these episomes, and long-term durability thus depends on episome stability. Prior modeling work has utilized differential equation-based models to characterize AAV uptake and subsequent protein production. However, episome loss associated with target cell turnover is poorly described with these models. Here, an agent-based model (ABM) to overcome this shortcoming is developed. The liver was used as the example organ as it is known to be self-renewing and has been a common target for gene therapies. In this model, each hepatocyte is an agent, capable of division and death. When transduced, these agents acquire and carry episomes. During cell division, episomes are passed from mother to daughter cells. All episomes are presumed lost when transduced cells die. The ABM was applied to etranacogene dezaparvovec (formerly AMT-061 or CSL222), a liver-targeting gene therapy for hemophilia B consisting of AAV serotype 5 particles encoding a transgene for the Padua variant (R338L) of coagulation factor IX (FIX). ABM-simulated FIX activity in patients receiving this therapy was consistent with clinical observations over more than 2 years following treatment. This ABM was then used to generate 20-year predictions and explore biological mechanisms underlying long-term durability. The ABM framework and approach should be applicable to investigating durability of other AAV-based gene therapies.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 7","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70286","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282672","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":"Polymyxin B Intravenous Administration Strategy Guided by Minimum Inhibitory Concentration in Critically Ill Patients With Pulmonary Infection: Insights From PBPK Modeling","authors":"Shengnan Zhang,&nbsp;Nan Yang,&nbsp;Ruwei Yang,&nbsp;Yuanfang Qin,&nbsp;Tingting Wu,&nbsp;Cuifang Wu,&nbsp;Yueliang Xie,&nbsp;Jingjing Liu,&nbsp;Chun Liu,&nbsp;Qi Pei","doi":"10.1002/psp4.70289","DOIUrl":"https://doi.org/10.1002/psp4.70289","url":null,"abstract":"<p>Intravenous polymyxin B (PMB) is widely used for treating drug-resistant gram-negative pneumonia, though its efficacy in pulmonary infections is considered limited. This study aims to identify patients with severe pulmonary infections suitable for treatment with intravenous PMB alone and to recommend individualized optimal dosing regimens. Physiologically based pharmacokinetic (PBPK) models, developed using data from mice, healthy subjects, and critically ill patients, were employed to predict PMB concentrations in epithelial lining fluid (ELF) and plasma. All models were validated against published pharmacokinetic data, and Monte Carlo simulations were used to evaluate various dosing regimens. The therapeutic target was defined as an ELF steady-state 24 h area under the concentration-time curve to minimum inhibitory concentration (AUC_ss,24h/MIC) ≥ 50. Simulations showed that intravenous PMB maintenance regimens of 100 mg, 1.25 mg/kg, or 1.50 mg/kg q12h enabled 94.4%, 82.2%, and 93.5% of critically ill patients to achieve the ELF pharmacokinetic/pharmacodynamic (PK/PD) target for pathogens with an MIC of 1 mg/L. A plasma AUC_ss,24h/MIC threshold of 80.6 was identified as an alternative PK/PD target, with retrospective analysis confirming significantly better outcomes for patients exceeding this threshold (<i>p</i> = 0.038), warranting prospective validation. Overall, PBPK modeling supports 100 mg or 1.50 mg/kg q12h as candidate initial intravenous regimens for critically ill patients with pulmonary infections. Dosing adjustments based on MIC and plasma AUC_ss,24h/MIC value are recommended. Adjunctive nebulization or combination therapy is advised for MICs ≥ 2 mg/L, while 50 mg q12h is sufficient for MICs ≤ 0.5 mg/L. Plasma AUC_ss,24h/MIC values exceeding 80.6 may guide individualized dosing when therapeutic drug monitoring is available.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 7","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70289","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282405","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":"The Empirical Bayes Variational Autoencoder—A Neural ODE Approach for Population Modeling in Pharmacology","authors":"Marcus Baaz,&nbsp;Anders Sjöberg,&nbsp;Mats Jirstrand","doi":"10.1002/psp4.70280","DOIUrl":"https://doi.org/10.1002/psp4.70280","url":null,"abstract":"<p>Variational autoencoders (VAEs) combined with neural ordinary differential equations provide a flexible framework for exploring neural latent-variable models in population pharmacokinetics. In this work, we investigate an empirical Bayes VAE formulation that integrates encoder–decoder architectures with covariate-dependent population priors, enabling correlated latent representations and probabilistic inference. We evaluate the proposed framework using controlled simulation studies and a small clinical benchmark dataset. The simulation experiments assess the ability to recover known population structures and covariate effects, while the clinical study evaluates subject-specific prediction and model diagnostics. In simulation studies with correlated individual parameters, the empirical Bayes VAE consistently captured population-level variability, whereas a fixed-prior VAE baseline exhibited systematic biases. In our experiments, extrapolation beyond the training dosing schedules showed more stable predictive behavior when using the proposed input–response normalization, relative to models trained without normalization, within a limited range. Diagnostic analyses indicated clear relationships between inferred latent variables and true parameters, and estimated observation noise was consistent with simulated values. In the clinical case study, cross-validation experiments suggested predictive performance comparable to previously reported neural ODE–based approaches. Overall, the results illustrate the feasibility of combining empirical Bayes inference with neural ODE–based decoders for population modeling. The proposed framework should be viewed as a methodological proof-of-concept, highlighting both the potential and the current limitations of variational neural approaches in pharmacometric applications.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 7","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70280","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148268906","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":"PMX-CovEval: A Framework Including a Simulated Pharmacokinetic Database for Covariate Model Building Methods Benchmarking","authors":"Mélanie Karlsen,&nbsp;Jérôme Azé,&nbsp;Sandra Bringay,&nbsp;Pascal Poncelet,&nbsp;David Fabre,&nbsp;David Marchionni,&nbsp;Elisa A. M. Calvier","doi":"10.1002/psp4.70278","DOIUrl":"https://doi.org/10.1002/psp4.70278","url":null,"abstract":"<p>The development of new methods for covariate model building (CMB) in population pharmacokinetics (popPK) highlights the need for a standardized evaluation framework for method benchmarking. This data paper introduces PMX-CovEval, a framework including a collection of datasets based on 127 distinct scenarios. These scenarios aim to reflect the diversity of models, available clinical studies, and covariates encountered in real-world applications, while remaining limited enough in number to encourage their practical use. To support the evaluation of standard CMB techniques available on PsN and Monolix, model files for NONMEM and Monolix are provided alongside the datasets. Additionally, empirical Bayes estimates (EBEs) from the true base models are included to facilitate the testing of EBE-based regression approaches. By offering pharmacokinetic (PK) datasets, model files, and EBEs in a unified resource, PMX-CovEval provides a standardized, reproducible framework for evaluation and systematic comparison of CMB strategies in popPK. Initial benchmarking results are also provided.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 7","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70278","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148268905","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":"PK and PK/PD Modeling of Bcl2 Inhibitor S65487 in Patients With AML and Investigation of Nonlinearity With Microdosing","authors":"Chloé Burlot,&nbsp;François Riglet,&nbsp;Mathilde Romagnoli,&nbsp;Ariane Leconte,&nbsp;Audrey Delmas,&nbsp;Antonin Schmitt,&nbsp;Sylvain Fouliard","doi":"10.1002/psp4.70288","DOIUrl":"10.1002/psp4.70288","url":null,"abstract":"<p>This work aims to assess the S65487-induced disruption of the B-cell lymphoma-2 (Bcl2)/Bcl2-like protein 11 (Bim) complex at therapeutic doses via a pharmacokinetic/pharmacodynamic model and to reconcile pharmacokinetic data from microdoses and therapeutic doses in a single population pharmacokinetic model. The first model consists of a linear 3-compartment pharmacokinetic model linked to an indirect response model with concentration-dependent inhibition of Bcl2/Bim complex formation. Simulations predicted a 97.6% maximum reduction in complex formation with 1200 mg of S65487, following a sustained administration schedule and using characteristics from phase I/II patients. Before clinical trials were conducted in cancer patients, a microdose study was conducted in healthy volunteers. Although S65487 exhibited slightly nonlinear pharmacokinetics at therapeutic doses, pharmacokinetic parameters estimated after microdose administration differed substantially. Thus, several hypotheses were explored to reconcile the microdose and therapeutic data within a unified PK model. Nonlinearity in drug disposition emerged as the most biologically plausible explanation. A Michaelis–Menten approximation of the target-mediated drug disposition model was applied to elimination and distribution processes to estimate parameters through saturable pathways. Performance of this second model, the microdose optimized pharmacokinetic model, was evaluated by comparing population predicted and observed area under the curve. This optimized model demonstrated significant improvement, reducing the difference in the description of exposure for the microdose study from 5- to 1-fold, without compromising the description at therapeutic doses. This modified model has a wider range of applicability across doses and could support the extrapolation of microdose study results to therapeutic doses in future research.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 7","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70288","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148263666","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":"Cellular Heterogeneity in Drug Uptake Amplifies Pharmacodynamic Variability: A Stochastic PK-PD Analysis","authors":"Nhung Hong-Thi Duong,&nbsp;Tuan Ngoc Do,&nbsp;Tien Tran-Nam Nguyen,&nbsp;Khanh Quoc Phan,&nbsp;Lap Thi Nguyen","doi":"10.1002/psp4.70282","DOIUrl":"10.1002/psp4.70282","url":null,"abstract":"<p>Traditional pharmacokinetic-pharmacodynamic models assume cellular homogeneity, yet clinical observations reveal substantial response variability even among patients with similar plasma exposure. We hypothesized that cellular heterogeneity in drug transporter expression, coupled with nonlinear dose–response relationships, can amplify microscopic cellular variability into population-level outcome variability. Using cobimetinib as an exemplar, we developed a proof-of-principle multiscale stochastic framework that couples deterministic systemic pharmacokinetics with cellular-level stochastic differential equations. In this framework, transporter expression was modeled as log-normally distributed across cells, generating heterogeneity in intracellular drug concentrations despite identical plasma exposure. Simulations showed that cellular heterogeneity can broaden the distribution of extinction times and produce population-level outcomes that differ from those predicted by homogeneous or mean-field formulations. Under the intermittent 21/7 regimen, extinction times were cycle-structured and, in the extended simulations, were better described by a three-component mixture than by a unimodal model, indicating schedule-associated survival cohorts rather than a universal multimodal law. Across the simulations, treatment failure probability increased with population size while the amplification factor remained approximately constant, consistent with an intensive single-cell property. Sensitivity analyses indicated that the coefficient of variation (CV) of transporter expression was a key determinant of outcome variability across the explored parameter space. These findings support the hypothesis that non-genetic heterogeneity in drug uptake can contribute to variability in treatment response and apparent resistance. More broadly, this proof-of-principle framework highlights the value of stochastic cell-level modeling for studying therapeutic response distributions when cellular heterogeneity and nonlinear pharmacodynamics are expected to play important roles.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 6","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70282","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148249611","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":"AI-Powered Lesion-Level Tumor Growth Inhibition Modeling Improves Model Stability and Prognostic Association With PFS","authors":"Alan Liu,&nbsp;Chong Duan,&nbsp;Kevin Maresca,&nbsp;Yazdi K. Pithavala,&nbsp;Jen-Tang Lu,&nbsp;Cathy Cen Guo,&nbsp;Jennifer E. Hibma","doi":"10.1002/psp4.70279","DOIUrl":"https://doi.org/10.1002/psp4.70279","url":null,"abstract":"<p>Response Evaluation Criteria in Solid Tumors (RECIST 1.1) is currently the standard for tumor response measurement, but it is time-consuming, labor-intensive, and subject to reader variability. Tumor growth inhibition (TGI) models built on RECIST datasets often consider the time-course of all lesions as one aggregated tumor, as represented by the sum of longest diameters. An AI-based tool was utilized to obtain comprehensive data of both aggregated tumor and individual lesion growth dynamics in terms of their longest diameters and 3D volumes. Standard and AI-derived measurements were used to develop TGI models to evaluate the time-course of aggregate and lesion-level tumor shrinkage for patients receiving lorlatinib in the phase III CROWN trial. Additionally, a parametric time-to-event model was developed to describe the PFS probability. Lesion-level TGI models were connected to the PFS model by investigating the influence of various TGI-derived tumor metrics and parameter estimates on the association with PFS. TGI models developed using the more comprehensive AI-generated lesion measurements demonstrated better precision and were more robust than traditional RECIST-based models when tested under different scenarios using stochastic simulation and estimation (SSE). The mean value of tumor decay rate (K_D) and the mean lesion size at week 8 were found to be significantly associated with PFS from the lesion-level models. In contrast, aggregated TGI models did not identify any significant prognostic factors of PFS. This modeling approach, utilizing AI-powered auto-measurement and incorporating individual lesion dynamics, provides a more complete understanding of tumor growth and can enable early decision-making for lesion-targeting therapies.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70279","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148238184","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":"Physiologically Based Pharmacokinetic Model to Predict Drug–Drug Interactions With the Antibody–Drug Conjugate Trastuzumab Deruxtecan","authors":"Akiko Watanabe,&nbsp;Katherine L. Gill,&nbsp;Kuan-Fu Chen,&nbsp;Hannah M. Jones,&nbsp;Mailys De Sousa Mendes,&nbsp;Iain Gardner,&nbsp;Masakatsu Kotsuma","doi":"10.1002/psp4.70284","DOIUrl":"10.1002/psp4.70284","url":null,"abstract":"<p>The topoisomerase I inhibitor payload (DXd) released from the human epidermal growth factor receptor 2-targeted antibody–drug conjugate (ADC) trastuzumab deruxtecan (T-DXd) is eliminated by hepatic uptake via OATP1B1/3, metabolism by CYP3A, biliary excretion via P-glycoprotein and breast cancer resistance protein, and urinary excretion. In a clinical drug–drug interaction (DDI) study between T-DXd and ritonavir (a strong cytochrome P450 [CYP] 3A/organic anion transporting polypeptide 1B inhibitor) or itraconazole (a strong CYP3A inhibitor), a 1.2-fold increase in DXd exposure was observed. In this study, a physiologically based pharmacokinetic (PBPK) model of T-DXd was constructed to quantitatively describe the pharmacokinetics (PK) of DXd and the DDI of DXd released from T-DXd. The minimal ADC PBPK model was developed to describe both intact ADC and payload PK using in vitro data of DXd and clinical PK data after T-DXd administration. The developed model reasonably described the PK of T-DXd and DXd after intravenous administration of T-DXd. The main elimination pathway of DXd was estimated to be biliary excretion, followed by urinary excretion, with metabolism contributing to a lesser extent. DDIs with ritonavir and itraconazole simulated by the PBPK model reproduced the clinical DDI study results, indicating that the impact of concomitant medications was not clinically meaningful. These findings suggest that this model may potentially be used for DDI evaluation of future DXd-containing ADCs, aiding in the prediction of DDIs under different scenarios and in specific populations.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70284","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148210413","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":"Evaluation of the Current ATTR-CM Treatment Landscape via a Mathematical Model of TTR Dissociation and Amyloid Formation","authors":"Stevan Methven Jeknić,&nbsp;Travis A. Ahn-Horst,&nbsp;Alan X. Ji,&nbsp;Jin Hyun Ju,&nbsp;Morgan Paull,&nbsp;Sun-Gou Ji,&nbsp;Uma Sinha","doi":"10.1002/psp4.70277","DOIUrl":"10.1002/psp4.70277","url":null,"abstract":"<p>Transthyretin amyloid cardiomyopathy (ATTR-CM) is a progressive, often fatal disease arising from the dissociation of circulating transthyretin (TTR) tetramers into monomers that misfold and form amyloid fibrils that deposit in the myocardium and other tissues. Approved treatment paradigms involve tetramer stabilization with small molecules or TTR knockdown via RNA interference. Despite the clinical success of these drugs across several metrics (including all-cause mortality and cardiovascular hospitalization), gaps remain in understanding how measurable biological changes, such as differences in serum TTR concentration, translate into reductions in amyloid deposition. To address this, we built a mechanistic mathematical model of the TTR system that integrates heterogeneous data sources, including in vitro assays and clinical data, and recapitulates key features of ATTR-CM disease biology. The model predicts “monomer efflux,” the rate at which unfolded monomers could potentially form amyloid, as a proxy for the pathogenic mechanism, which is difficult to measure. Model-predicted monomer efflux linearly correlated with reported rates of change in clinical measures, supporting its validity as a comparative metric. The model showed that monomer efflux is nearly twice as high in hereditary ATTR-CM (ATTRv) as in wild-type ATTR-CM (ATTRwt). Comparison of treatment modalities showed that acoramidis yields the greatest reduction in monomer efflux (96% in ATTRwt, 95% in ATTRv) due to rapid, near-complete stabilization of TTR. This framework enables quantitative interrogation of the disease system and principled comparisons across modalities that may help inform treatment selection.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70277","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148197653","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":"Modeling Peak Expiratory Flow in Patients With Asthma and Quantifying Treatment Effects Using a Mixed-Effects Hidden Markov Model","authors":"Ludvig Jakobsson,&nbsp;Marcus Baaz,&nbsp;Jacob Leander,&nbsp;Philip Gerlee,&nbsp;Mats Jirstrand","doi":"10.1002/psp4.70281","DOIUrl":"https://doi.org/10.1002/psp4.70281","url":null,"abstract":"<p>Clinical trials in asthma and chronic obstructive pulmonary disease often use exacerbation risk as the primary endpoint. However, exacerbations occur with low frequency, leading to long and costly clinical trials. Home-measured spirometry, which is becoming more common, provides an alternative and has previously been used to shorten the necessary trial duration. In this work, we develop a <i>mixed-effects hidden Markov model</i> (MEHMM) for analyzing home-measured <i>peak expiratory flow</i> (PEF), combining an observation model with a latent two-state disease process representing sustained periods of high and low PEF, respectively. An inference framework is implemented to estimate fixed and random effects together with measures of uncertainty. Data from a phase 2b dose-finding study of velsecorat in asthma are used to investigate dose–response relationships, complemented by an extensive simulation study. The results demonstrate reliable estimation of parameters and identify statistically significant treatment effects on multiple model components. These findings support the use of latent disease-state models for extracting meaningful information from home-measured spirometry.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70281","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148174456","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}