模拟使用虚拟人群口服曲前列地尼治疗肺动脉高压的临床试验。

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Andrew E Stine, Jignesh Parmar, Amy K Smith, Zachary Cummins, Narasimha Rao Pillalamarri, R Joseph Bender
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引用次数: 0

摘要

肺动脉高压等罕见疾病的药物开发挑战可以通过使用数学建模来解决。本研究采用肺动脉高压病理生理和药理学的定量系统药理学模型,预测口服treprostinil临床研究背景下肺血管阻力和6分钟步行距离的变化。我们生成了一个虚拟人群,它跨越了临床观察的范围,然后校准了虚拟的患者特定权重,以匹配临床试验。然后,我们使用这个虚拟人群根据疾病严重程度、给药方案、诊断后的时间和共同给药的背景治疗来预测临床试验的结果。虚拟人群捕获了试验设计和患者亚群变化对临床反应的影响。我们还展示了虚拟试验工作流程的潜力,可以根据临床生物标志物丰富人群,增加试验成功的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simulation of clinical trials of oral treprostinil in pulmonary arterial hypertension using a virtual population.

Challenges in drug development for rare diseases such as pulmonary arterial hypertension can be addressed through the use of mathematical modeling. In this study, a quantitative systems pharmacology model of pulmonary arterial hypertension pathophysiology and pharmacology was used to predict changes in pulmonary vascular resistance and six-minute walk distance in the context of oral treprostinil clinical studies. We generated a virtual population that spanned the range of clinical observations and then calibrated virtual patient-specific weights to match clinical trials. We then used this virtual population to predict the results of clinical trials on the basis of disease severity, dosing regimen, time since diagnosis, and co-administered background therapies. The virtual population captured the effect of changes in trial design and patient subpopulation on clinical response. We also demonstrated the virtual trial workflow's potential for enriching populations based on clinical biomarkers to increase likelihood of trial success.

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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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