Credibility assessment of in silico clinical trials for medical devices.

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2024-08-08 eCollection Date: 2024-08-01 DOI:10.1371/journal.pcbi.1012289
Pras Pathmanathan, Kenneth Aycock, Andreu Badal, Ramin Bighamian, Jeff Bodner, Brent A Craven, Steven Niederer
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Abstract

In silico clinical trials (ISCTs) are an emerging method in modeling and simulation where medical interventions are evaluated using computational models of patients. ISCTs have the potential to provide cost-effective, time-efficient, and ethically favorable alternatives for evaluating the safety and effectiveness of medical devices. However, ensuring the credibility of ISCT results is a significant challenge. This paper aims to identify unique considerations for assessing the credibility of ISCTs and proposes an ISCT credibility assessment workflow based on recently published model assessment frameworks. First, we review various ISCTs described in the literature, carefully selected to showcase the range of methodological options available. These studies cover a wide variety of devices, reasons for conducting ISCTs, patient model generation approaches including subject-specific versus 'synthetic' virtual patients, complexity levels of devices and patient models, incorporation of clinician or clinical outcome models, and methods for integrating ISCT results with real-world clinical trials. We next discuss how verification, validation, and uncertainty quantification apply to ISCTs, considering the range of ISCT approaches identified. Based on our analysis, we then present a hierarchical workflow for assessing ISCT credibility, using a general credibility assessment framework recently published by the FDA's Center for Devices and Radiological Health. Overall, this work aims to promote standardization in ISCTs and contribute to the wider adoption and acceptance of ISCTs as a reliable tool for evaluating medical devices.

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医疗器械硅学临床试验的可信度评估。
硅学临床试验(ISCTs)是建模和模拟领域的一种新兴方法,利用患者的计算模型对医疗干预措施进行评估。ISCT 有可能为评估医疗设备的安全性和有效性提供成本效益高、时间效率高、符合道德规范的替代方法。然而,确保 ISCT 结果的可信度是一项重大挑战。本文旨在确定评估 ISCT 可信度的独特考虑因素,并根据最近发布的评估框架模型提出 ISCT 可信度评估工作流程。首先,我们回顾了文献中描述的各种 ISCT,这些文献都经过精心挑选,以展示一系列可用的方法选项。这些研究涵盖了各种设备、开展 ISCT 的原因、患者模型生成方法(包括特定受试者与 "合成 "虚拟患者)、设备和患者模型的复杂程度、临床医生或临床结果模型的纳入,以及将 ISCT 结果与真实世界临床试验相结合的方法。考虑到已确定的 ISCT 方法的范围,我们接下来将讨论验证、确认和不确定性量化如何应用于 ISCT。在分析的基础上,我们利用 FDA 器械与放射卫生中心最近发布的通用可信度评估框架,提出了评估 ISCT 可信度的分层工作流程。总之,这项工作旨在促进 ISCT 的标准化,并推动更广泛地采用和接受 ISCT 作为评估医疗器械的可靠工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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