CPT: Pharmacometrics & Systems Pharmacology最新文献_第6页

Clinical modeling of motor function to predict treatment efficacy and enable in silico treatment comparisons in infantile-onset Pompe disease. 运动功能的临床建模以预测治疗效果，并使婴儿起病庞贝病的计算机治疗比较成为可能。

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-12-13 DOI: 10.1002/psp4.13287

Fatiha Rachedi, Rana Jreich, Susan Sparks, Atef Zaher, Kristina An Haack, Alicia Granados, Zhaoling Meng

{"title":"Clinical modeling of motor function to predict treatment efficacy and enable in silico treatment comparisons in infantile-onset Pompe disease.","authors":"Fatiha Rachedi, Rana Jreich, Susan Sparks, Atef Zaher, Kristina An Haack, Alicia Granados, Zhaoling Meng","doi":"10.1002/psp4.13287","DOIUrl":"https://doi.org/10.1002/psp4.13287","url":null,"abstract":"Infantile-onset Pompe disease (IOPD) is a rare, deadly, quickly-progressing degenerative disease. Even with life-sustaining treatment (e.g., alglucosidase alfa [ALGLU]), many patients experience continued motor impairment. The Mini-COMET trial evaluated avalglucosidase alfa (AVAL) versus ALGLU on motor and other outcomes in IOPD. However, treatment groups were imbalanced at baseline and the trial was not powered to directly compare treatments. To supplement this limited data, we developed a modeling and simulation approach to compare AVAL versus ALGLU head-to-head in in silico (i.e., computer-simulated) trials. We first developed a longitudinal clinical model to establish the relationship between changes in motor function and changes in urinary hexose tetrasaccharide (uHex4), an established biomarker in IOPD. This model was based on pooled data from Mini-COMET (n = 21) and COMET trials (n = 100 patients with late-onset Pompe disease, LOPD). We then conducted in silico trials mimicking Mini-COMET. Simulated trials were informed by motor data generated from the clinical model and uHex4 profiles simulated in a quantitative systems pharmacology model. The virtual IOPD population was based on observed Mini-COMET baseline characteristics but engineered to have well-balanced baseline characteristics across treatment cohorts. In silico trials showed that patients with IOPD would have the greatest improvements in motor function with AVAL 40 mg/kg every other week (Q2W), suboptimal improvement with ALGLU 40 mg/kg Q2W, and no improvement with ALGLU 20 mg/kg Q2W. This study provides information on the relative efficacy of IOPD treatments and mitigates the confounding effects of imbalanced treatment cohorts. Our approach could also be applied in other rare diseases.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142817323","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

A tutorial on pharmacometric Markov models 关于药物计量学马尔可夫模型的教程。

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-12-13 DOI: 10.1002/psp4.13278

Qing Xi Ooi, Elodie Plan, Martin Bergstrand

{"title":"A tutorial on pharmacometric Markov models","authors":"Qing Xi Ooi, Elodie Plan, Martin Bergstrand","doi":"10.1002/psp4.13278","DOIUrl":"10.1002/psp4.13278","url":null,"abstract":"The Markov chain is a stochastic process in which the future value of a variable is conditionally independent of the past, given its present value. Data with Markovian features are characterized by: frequent observations relative to the expected changes in values, many consecutive same-category or similar-value observations at the individual level, and a positive correlation observed between the current and previous values for that variable. In drug development and clinical settings, the data available commonly present Markovian features and are increasingly often modeled using Markov elements or dedicated Markov models. This tutorial presents the main characteristics, evaluations, and applications of various Markov modeling approaches including the discrete-time Markov models (DTMM), continuous-time Markov models (CTMM), hidden Markov models, and item-response theory model with Markov sub-models. The tutorial has a specific emphasis on the use of DTMM and CTMM for modeling ordered-categorical data with Markovian features. Although the main body of this tutorial is written in a software-neutral manner, annotated NONMEM code for all key Markov models is included in the Supplementary Information.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 2","pages":"197-216"},"PeriodicalIF":3.1,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13278","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142817390","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

Advancing pharmacometrics in Africa—Transition from capacity development toward job creation 在非洲推进药物计量学——从能力发展到创造就业的转变。

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-12-09 DOI: 10.1002/psp4.13291

Goonaseelan (Colin) Pillai, Samer Mouksassi, Innocent G. Asiimwe, Craig R. Rayner, Steven Kern, Phumla Sinxadi, Paolo Denti, Eric Decloedt, Catriona Waitt, Bernhards R. Ogutu, Rik de Greef

{"title":"Advancing pharmacometrics in Africa—Transition from capacity development toward job creation","authors":"Goonaseelan (Colin) Pillai, Samer Mouksassi, Innocent G. Asiimwe, Craig R. Rayner, Steven Kern, Phumla Sinxadi, Paolo Denti, Eric Decloedt, Catriona Waitt, Bernhards R. Ogutu, Rik de Greef","doi":"10.1002/psp4.13291","DOIUrl":"10.1002/psp4.13291","url":null,"abstract":"Trained pharmacometricians remain scarce in Africa due to limited training opportunities, lack of a pharmaceutical product development ecosystem, and emigration to high-income countries. The Applied Pharmacometrics Training (APT) fellowship program was established to address these gaps and specifically foster job creation for talent retention. We review the APT program's progress over 3 years and encourage collaboration to enhance local clinical data analysis in Africa. Initiated in 2021 by Pharmacometrics Africa, a non-profit educational entity, with support from partners including the Bill & Melinda Gates Foundation and Certara, the APT program targets African doctoral-level scientists and clinicians. This 6-month program is jointly managed by partners, with Pharmacometrics Africa handling logistics and sponsor liaison. Job creation initiatives include inviting fellows to join consulting teams or local research centers. Over the 3 year reporting period, 177 applications were received, with 27 individuals (41% female, median age 35 years) from nine African countries selected into and completing the full program. The fellows worked on 13 data analysis projects, with six so far being presented at international conferences and/or submitted for publication in peer-reviewed journals. Nine fellows have joined consulting teams or research centers working from offices in Africa. Currently, in the 3rd year, the APT program has demonstrated success in skills development, job creation, and fostering a critical mass of African pharmacometricians. Collaboration is essential for the sustainable advancement of model-informed drug development in Africa.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 3","pages":"407-419"},"PeriodicalIF":3.1,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13291","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794539","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

Implementing a Bayesian approach using Stan with Torsten: Population pharmacokinetics analysis of somatrogon 与 Torsten 一起使用 Stan 实现贝叶斯方法：索马曲贡的群体药代动力学分析。

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-12-09 DOI: 10.1002/psp4.13279

Yuchen Wang, Xinyi Pei, Tao Niu, Joan Korth-Bradley, Luke Fostvedt

{"title":"Implementing a Bayesian approach using Stan with Torsten: Population pharmacokinetics analysis of somatrogon","authors":"Yuchen Wang, Xinyi Pei, Tao Niu, Joan Korth-Bradley, Luke Fostvedt","doi":"10.1002/psp4.13279","DOIUrl":"10.1002/psp4.13279","url":null,"abstract":"Fully Bayesian approaches are not commonly implemented for population pharmacokinetic (PK) modeling. In this paper, we evaluate the use of Stan with R and Torsten for population PK modeling of somatrogon, a recombinant long-acting growth hormone approved for the treatment of growth hormone deficiency. As a software for Bayesian inference, Stan provides an easy way to conduct MCMC sampling for a wide range of models with efficient sampling algorithms, and there are several diagnostic tools to evaluate the MCMC convergence and other potential issues. Three different sets of priors were evaluated for estimation and prediction: a weakly informative uniform set, a moderately informative set, and a very informative set of priors. All three prior sets showed good performance and all chains mixed well. There were some minor differences in the final parameter posterior distributions while implementing different prior sets, but the posterior predictions covered the observations nicely, not only for the individuals included in posterior sampling but also for new individuals. The impact of a centered versus non-centered parameterization were evaluated, with the non-centered approach improving the estimation time, but it was still computationally intensive. Computational resources had the biggest impact on sampling time. Stan took approximately 2.5 h total for the MCMC sampling on a high-performance computing platform (6 cores) and may be reduced further with additional computational resources. The model and comparisons presented show that with adequate computational resources, the Bayesian approaches using Stan and Torsten are useful for population PK analysis, especially for the analysis of special populations, small sample datasets, and when complex model structures are needed.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 2","pages":"351-364"},"PeriodicalIF":3.1,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13279","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142799693","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

A minimal physiologically-based pharmacokinetic modeling platform to predict intratumor exposure and receptor occupancy of an anti-LAG-3 monoclonal antibody 一个最小的基于生理的药代动力学建模平台，用于预测抗lag -3单克隆抗体的肿瘤内暴露和受体占用。

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-12-09 DOI: 10.1002/psp4.13285

Robin Michelet, Klas Petersson, Marc C. Huisman, C. Willemien Menke-van der Houven van Oordt, Iris H. C. Miedema, Andrea Thiele, Ghazal Montaseri, Alejandro Pérez-Pitarch, David Busse

{"title":"A minimal physiologically-based pharmacokinetic modeling platform to predict intratumor exposure and receptor occupancy of an anti-LAG-3 monoclonal antibody","authors":"Robin Michelet, Klas Petersson, Marc C. Huisman, C. Willemien Menke-van der Houven van Oordt, Iris H. C. Miedema, Andrea Thiele, Ghazal Montaseri, Alejandro Pérez-Pitarch, David Busse","doi":"10.1002/psp4.13285","DOIUrl":"10.1002/psp4.13285","url":null,"abstract":"In oncology drug development, measuring drug concentrations at the tumor site and at the targeted receptor remains an ongoing challenge. Positron emission tomography (PET)-imaging is a promising noninvasive method to quantify intratumor exposure of a radiolabeled drug (biodistribution data) and target saturation by treatment doses in vivo. Here, we present the development and application of a minimal physiologically-based pharmacokinetic (mPBPK) modeling approach to integrate biodistribution data in a quantitative platform to characterize and predict intratumor exposure and receptor occupancy (RO) of BI 754111, an IgG-based anti-lymphocyte-activation gene 3 (LAG-3) monoclonal antibody (mAb). Specifically, calibration and qualification of the predictions were performed using 89Zr-labeled BI 754111 biodistribution data, that is, PET-derived intratumor drug concentration data, tumor-to-plasma ratios, and data from Patlak analyses. The model predictions were refined iteratively by the inclusion of additional biological processes into the model structure and the use of sensitivity analyses to assess the impact of model assumptions and parameter uncertainty on the predictions and model robustness. The developed mPBPK model allowed an adequate description of observed tumor radioactivity concentrations and tumor-to-plasma ratios leading to subsequent adequate prediction of LAG-3 RO at different dose levels. In the future, the developed model could be used as a platform for the prediction and analysis of biodistribution data for other mAbs and may ultimately support dose optimization by identifying dosages resulting in saturated RO.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 3","pages":"460-473"},"PeriodicalIF":3.1,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13285","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142799691","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

Semi-mechanistic population PK/PD model to aid clinical understanding of myelodysplastic syndromes following treatment with Venetoclax and Azacitidine 半机械性人群PK/PD模型，以帮助临床理解在Venetoclax和阿扎胞苷治疗后的骨髓增生异常综合征。

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-12-09 DOI: 10.1002/psp4.13284

Neha Thakre, Corinna Maier, Jiuhong Zha, Benjamin Engelhardt, Johannes E. Wolff, Sven Mensing

{"title":"Semi-mechanistic population PK/PD model to aid clinical understanding of myelodysplastic syndromes following treatment with Venetoclax and Azacitidine","authors":"Neha Thakre, Corinna Maier, Jiuhong Zha, Benjamin Engelhardt, Johannes E. Wolff, Sven Mensing","doi":"10.1002/psp4.13284","DOIUrl":"10.1002/psp4.13284","url":null,"abstract":"Myelodysplastic syndromes (MDS) represent a group of bone marrow disorders involving cytopenias, hypercellular bone marrow, and dysplastic hematopoietic progenitors. MDS remains a challenge to treat due to the complex interplay between disease-induced and treatment-related cytopenias. Venetoclax, a selective BCL-2 inhibitor, in combination with azacitidine, a hypomethylating agent, is currently being investigated in patients with previously untreated higher-risk MDS. We present an integrated semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model developed using preliminary clinical data from an ongoing Phase 1b study evaluating the safety and efficacy of venetoclax in combination with azacitidine in treatment-naïve patients with higher-risk MDS. Longitudinal data from 57 patients were used to develop the model, which accounted for venetoclax PK and azacitidine treatment to describe time dynamics of bone marrow blasts, neutrophils, red blood cells, and platelets. The proliferation and maturation of progenitor cells in the bone marrow to peripheral cells is described via three parallel connected transit models including feedback terms. The model also accounted for bone marrow crowding and its impact on hematopoiesis. Model validation demonstrated adequate goodness-of-fit, visual and numerical predictive checks. Model predicted complete remission (CR) rates and marrow complete remission (mCR) rates closely matched observed rates in the clinical study, and simulated efficacy (recovery of blast count, CR, and mCR rates) and safety (neutropenia and thrombocytopenia) endpoints aligned with expected outcomes from various dosing regimens. Importantly, the semi-mechanistic model may aid understanding and discriminating between disease-driven and drug-induced cytopenias.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 3","pages":"448-459"},"PeriodicalIF":3.1,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13284","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142799671","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

Accelerating virtual patient generation with a Bayesian optimization and machine learning surrogate model 利用贝叶斯优化和机器学习代理模型加速虚拟患者生成。

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-12-04 DOI: 10.1002/psp4.13288

Hiroaki Iwata, Ryuta Saito

{"title":"Accelerating virtual patient generation with a Bayesian optimization and machine learning surrogate model","authors":"Hiroaki Iwata, Ryuta Saito","doi":"10.1002/psp4.13288","DOIUrl":"10.1002/psp4.13288","url":null,"abstract":"The pharmaceutical industry has increasingly adopted model-informed drug discovery and development (MID3) to enhance productivity in drug discovery and development. Quantitative systems pharmacology (QSP), which integrates drug action mechanisms and disease complexities to predict clinical endpoints and biomarkers is central to MID3. QSP modeling has proven successful in metabolic and cardiovascular diseases and has expanded into oncology, immunotherapy, and infectious diseases. Despite its benefits, QSP model validation through clinical trial simulations using virtual patients (VPs) is challenging because of parameter variability and high computational costs. To address these challenges, this study proposes a hybrid method that combines Bayesian optimization with machine learning for efficient parameter screening. Our approach achieved an acceptance rate of 27.5% in QSP simulations, which is in sharp contrast with the 2.5% rate of conventional random search methods, indicating more than 10-fold improvement in efficiency. By facilitating faster and more diverse VPs generation, this method promises to advance clinical trial simulations and accelerate drug development in pharmaceutical research.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 3","pages":"486-494"},"PeriodicalIF":3.1,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13288","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142779569","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

Correction to Developmental pharmacokinetics of indomethacin in preterm neonates: Severely decreased drug clearance in the first week of life 对早产儿吲哚美辛发育药代动力学的修正：出生第一周药物清除率严重降低。

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-12-02 DOI: 10.1002/psp4.13289

{"title":"Correction to Developmental pharmacokinetics of indomethacin in preterm neonates: Severely decreased drug clearance in the first week of life","authors":"","doi":"10.1002/psp4.13289","DOIUrl":"10.1002/psp4.13289","url":null,"abstract":"Krzyzanski W, Stockard B, Gaedigk A, et al. Developmental pharmacokinetics of indomethacin in preterm neonates: severely decreased drug clearance in the first week of life. CPT Pharmacometrics Syst Pharmacol. 2023;12:110–121. doi:10.1002/psp4.12881In the published version of the above article, the equation reported to convert dried blood spot (DBS) indomethacin concentrations to plasma concentrations is incorrect. Rather than “plasma[IND] = DBS[IND]/(1 – hematocrit) * 1.608,” the equation should be “C(plasma) = 1.837(C(DBS)/(1 – Hct/100)) – 236.6.” There is also an inaccurate statement in the Bioanalytical methods section: “A correction factor (1.608, mean of the ratio of plasma:DBS concentrations) was used to calculate the theoretical plasma concentrations from the hematocrit-corrected DBS concentration,” which does not align with the data analysis that was performed.This author error in reporting does not affect the results or conclusions of the paper as the correct equation (more accurate and appropriate) was used to convert DBS to plasma concentrations for data analysis, and the wrong equation was reported in the manuscript.We apologize for this error.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 12","pages":"2210"},"PeriodicalIF":3.1,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13289","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142767018","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

Isatuximab–dexamethasone–pomalidomide combination effects on serum M protein and PFS in myeloma: Development of a joint model using phase I/II data 依沙妥昔单抗-地塞米松-泊马度胺联合治疗对骨髓瘤患者血清M蛋白和PFS的影响：基于I/II期数据的联合模型的开发

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-11-28 DOI: 10.1002/psp4.13206

Antoine Pitoy, Solène Desmée, François Riglet, Hoai-Thu Thai, Zandra Klippel, Dorothée Semiond, Christine Veyrat-Follet, Julie Bertrand

{"title":"Isatuximab–dexamethasone–pomalidomide combination effects on serum M protein and PFS in myeloma: Development of a joint model using phase I/II data","authors":"Antoine Pitoy, Solène Desmée, François Riglet, Hoai-Thu Thai, Zandra Klippel, Dorothée Semiond, Christine Veyrat-Follet, Julie Bertrand","doi":"10.1002/psp4.13206","DOIUrl":"10.1002/psp4.13206","url":null,"abstract":"This study aimed at leveraging data from phase I/II clinical trials to build a nonlinear joint model of serum M-protein kinetics and progression-free survival (PFS) accounting for the effects of isatuximab (Isa), pomalidomide (Pom), and dexamethasone (Dex) in patients with relapsed and/or refractory multiple myeloma. Serum M-protein levels and PFS data from 203 evaluable patients, included either in a phase I/II study (n = 173) or in a phase I study (n = 30), were used to build the model. First, we independently developed a longitudinal model and a PFS model. Then, we linked them in a nonlinear joint model by selecting the link function that best captured the association between serum M-protein kinetics and PFS. A Claret tumor growth-inhibition model accounting for the additive effects of Isa, with an Emax function, Pom, and Dex on serum M-protein elimination was selected to describe serum M-protein kinetics. PFS was best described with a log-logistic model and associations with baseline beta-2 microglobulin level, age, and coadministration of Dex were identified. The instantaneous change in serum M-protein level was found to be associated with PFS in the final joint model. Using model simulations, we retrospectively supported the Isa 10 mg/kg weekly for 4 weeks, then biweekly (QW/Q2W) dosing regimen of the ICARIA-MM phase III pivotal study, and validated it using the same phase III pivotal study data.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 12","pages":"2087-2101"},"PeriodicalIF":3.1,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142749990","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

A computational tool to optimize clinical trial parameter selection in Duchenne muscular dystrophy: A practical guide and case studies. 优化杜氏肌营养不良症临床试验参数选择的计算工具：实用指南和案例研究。

IF 3.1 3区医学

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-11-27 DOI: 10.1002/psp4.13281

Jordan Wilk, Varun Aggarwal, Mike Pauley, Diane Corey, Daniela J Conrado, Karthik Lingineni, Juan Francisco Morales, Deok Yong Yoon, Yi Zhang, Zihan Cui, Jackson Burton, Jane Larkindale, Shu Chin Ma, Collin Hovinga, Terina Martinez, Klaus Romero, Ramona Belfiore-Oshan, Sarah Kim

{"title":"A computational tool to optimize clinical trial parameter selection in Duchenne muscular dystrophy: A practical guide and case studies.","authors":"Jordan Wilk, Varun Aggarwal, Mike Pauley, Diane Corey, Daniela J Conrado, Karthik Lingineni, Juan Francisco Morales, Deok Yong Yoon, Yi Zhang, Zihan Cui, Jackson Burton, Jane Larkindale, Shu Chin Ma, Collin Hovinga, Terina Martinez, Klaus Romero, Ramona Belfiore-Oshan, Sarah Kim","doi":"10.1002/psp4.13281","DOIUrl":"https://doi.org/10.1002/psp4.13281","url":null,"abstract":"Duchenne muscular dystrophy (DMD), a rare pediatric disease, presents numerous challenges when designing clinical trials, mainly due to the scarcity of available trial participants and the heterogeneity of disease progression. A quantitative clinical trial simulator (CTS) has been developed based on previously published five disease progression models describing each of the longitudinal changes in the velocity at which individuals can complete specified timed functional tests, frequently used as clinical trial efficacy endpoints (supine-stand, 4-stair climb, and 10 m walk/run test or 30-foot walk/run test), as well as each of the longitudinal changes in forced vital capacity and North Star Ambulatory Assessment total score. The model-based CTS allows researchers to optimize the selection of numerous trial parameters for designing trials for the five functional measures commonly used as endpoints in DMD clinical trials. This case report serves as a demonstration of the tool's functionality while providing an easy-to-follow guide for users to reference when preparing simulations of their own design. Two case studies, using input selection based on previous DMD clinical trials, provide realistic examples of how the tool can help optimize clinical trial design without the risk of decreasing statistical significance. This optimization allows researchers to mitigate the risk of designing trials that may be longer, larger, or more inclusive/exclusive than necessary.","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142726674","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