合成数据和计算机试验在产生具有代表性的虚拟人群队列方面的潜在协同作用

IF 5 Q1 ENGINEERING, BIOMEDICAL
P. Myles, Johan Ordish, A. Tucker
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引用次数: 4

摘要

计算机试验方法有望改善药品和医疗器械的上市途径,以产品开发为目标,减少对动物试验的依赖,并提供辅助证据,以支持提交监管文件。计算机试验的效果与支持它们的模拟数据一样好,因此,在创建健壮的计算机模型时,最困难的挑战通常是生成模拟测量结果,甚至是代表真实测量结果和患者的虚拟患者。本文概述了在计算机试验环境之外生成合成患者数据的最新技术,并概述了利用虚拟人群来释放计算机试验潜力的潜在协同作用,方法是利用合成患者数据对更多样化和更具代表性的人群进行建模。合成数据可以定义为模拟真实数据中的属性和关系的人工数据。合成数据生成方法的最新进展已经允许生成高保真的合成数据,这些数据在统计和临床方面都与真实的患者数据无法区分。其他实验工作已经证明,合成数据生成方法可以用于选择性样本增强代表性不足的群体。本文将简要概述合成数据生成方法,并讨论如何开发评估框架来评估合成数据的保真度和实用性,以评估用于计算机试验的虚拟患者与真实患者的相似性。然后,文章将讨论突出的挑战和进一步研究的领域,这将推进合成数据生成方法和硅试验方法。最后,本文还将提供一个视角,说明需要哪些证据来促进更广泛地接受用于药品和医疗器械监管评估的计算机试验,包括对上市后安全监督的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The potential synergies between synthetic data and in silico trials in relation to generating representative virtual population cohorts
In silico trial methods promise to improve the path to market for both medicines and medical devices, targeting the development of products, reducing reliance on animal trials, and providing adjunct evidence to bolster regulatory submissions. In silico trials are only as good as the simulated data which underpins them, consequently, often the most difficult challenge when creating robust in silico models is the generation of simulated measurements or even virtual patients that are representative of real measurements and patients. This article digests the current state of the art for generating synthetic patient data outside the context of in silico trials and outlines potential synergies to unlock the potential of in silico trials using virtual populations, by exploiting synthetic patient data to model effects on a more diverse and representative population. Synthetic data could be defined as artificial data that mimic the properties and relationships in real data. Recent advances in synthetic data generation methodologies have allowed for the generation of high-fidelity synthetic data that are both statistically and clinically, indistinguishable from real patient data. Other experimental work has demonstrated that synthetic data generation methods can be used for selective sample boosting of underrepresented groups. This article will provide a brief outline of synthetic data generation approaches and discuss how evaluation frameworks developed to assess synthetic data fidelity and utility could be adapted to evaluate the similarity of virtual patients used for in silico trials, to real patients. The article will then discuss outstanding challenges and areas for further research that would advance both synthetic data generation methods and in silico trial methods. Finally, the article will also provide a perspective on what evidence will be required to facilitate wider acceptance of in silico trials for regulatory evaluation of medicines and medical devices, including implications for post marketing safety surveillance.
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来源期刊
CiteScore
9.40
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