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本文引用的文献

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Definitions and Characteristics of Patient Digital Twins Being Developed for Clinical Use: Scoping Review.用于临床应用的患者数字孪生体的定义和特征:范围综述。
J Med Internet Res. 2024 Nov 13;26:e58504. doi: 10.2196/58504.
2
The EU passes the AI Act and its implications for digital medicine are unclear.欧盟通过了人工智能法案,但其对数字医学的影响尚不明朗。
NPJ Digit Med. 2024 May 22;7(1):135. doi: 10.1038/s41746-024-01116-6.
3
A digital twin of the infant microbiome to predict neurodevelopmental deficits.婴儿微生物组的数字孪生体预测神经发育缺陷。
Sci Adv. 2024 Apr 12;10(15):eadj0400. doi: 10.1126/sciadv.adj0400. Epub 2024 Apr 10.
4
Digital twins for health: a scoping review.用于健康的数字孪生:一项范围综述。
NPJ Digit Med. 2024 Mar 22;7(1):77. doi: 10.1038/s41746-024-01073-0.
5
Harnessing the power of synthetic data in healthcare: innovation, application, and privacy.利用合成数据在医疗保健领域的力量:创新、应用与隐私。
NPJ Digit Med. 2023 Oct 9;6(1):186. doi: 10.1038/s41746-023-00927-3.
6
Health Digital Twins in Life Science and Health Care Innovation.健康数字孪生在生命科学和医疗保健创新中的应用。
Annu Rev Pharmacol Toxicol. 2024 Jan 23;64:159-170. doi: 10.1146/annurev-pharmtox-022123-022046. Epub 2023 Aug 10.
7
To guide future practice, perinatal trials should be much larger, simpler and less fragile with close to 100% ascertainment of mortality and other key outcomes.为了指导未来的实践,围产期试验应该更大、更简单、更稳健,接近 100%确定死亡率和其他关键结局。
Semin Perinatol. 2023 Aug;47(5):151789. doi: 10.1016/j.semperi.2023.151789. Epub 2023 Jun 11.
8
Synthetic Data Generation by Artificial Intelligence to Accelerate Research and Precision Medicine in Hematology.人工智能生成合成数据以加速血液学研究和精准医学
JCO Clin Cancer Inform. 2023 Jun;7:e2300021. doi: 10.1200/CCI.23.00021.
9
Learning From Experience and Finding the Right Balance in the Governance of Artificial Intelligence and Digital Health Technologies.从经验中学习,在人工智能和数字健康技术的治理中找到正确的平衡。
J Med Internet Res. 2023 Apr 14;25:e43682. doi: 10.2196/43682.
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Synthetic data in medical research.医学研究中的合成数据。
BMJ Med. 2022 Sep 26;1(1):e000167. doi: 10.1136/bmjmed-2022-000167. eCollection 2022.

数字孪生、合成患者数据和虚拟试验:它们能否助力儿科临床试验?

Digital twins, synthetic patient data, and in-silico trials: can they empower paediatric clinical trials?

作者信息

Pammi Mohan, Shah Prakesh S, Yang Liu K, Hagan Joseph, Aghaeepour Nima, Neu Josef

机构信息

Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, TX, USA.

Department of Paediatrics, Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada.

出版信息

Lancet Digit Health. 2025 May;7(5):100851. doi: 10.1016/j.landig.2025.01.007. Epub 2025 May 13.

DOI:10.1016/j.landig.2025.01.007
PMID:40360351
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12171946/
Abstract

Randomised controlled trials are the gold standard to assess the effectiveness and safety of clinical interventions; however, many paediatric trials are discontinued early due to challenges in patient enrolment. Hence, most paediatric clinical trials suffer from lack of adequate power. Additionally, trials are expensive and might expose patients to unproven therapies. Alternatives to overcome these issues using virtual patient data-namely, digital twins, synthetic patient data, and in-silico trials-are now possible due to rapid advances in digital health-care tools and interventions. However, such digital innovations have been rarely used in paediatric trials. In this Viewpoint, we propose using virtual patient data to empower paediatric trials. The use of virtual patient data has the advantages of decreased exposure of children to potentially ineffective or risky interventions, shorter trial durations leading to more rapid ascertainment of safety and effectiveness of interventions, and faster drug approvals. Use of virtual patient data could lead to more personalised treatment options with low costs and could result in faster clinical implementation of interventions in children. However, ethical and regulatory concerns, including replacing humans with digital data, data privacy, and security should be addressed and the safety and sustainability of digital data innovation ensured before virtual patient data are adopted widely.

摘要

随机对照试验是评估临床干预措施有效性和安全性的金标准;然而,由于患者招募方面的挑战,许多儿科试验提前终止。因此,大多数儿科临床试验缺乏足够的效力。此外,试验成本高昂,可能会让患者接触到未经证实的疗法。由于数字医疗工具和干预措施的迅速发展,现在可以使用虚拟患者数据(即数字孪生、合成患者数据和虚拟试验)来克服这些问题。然而,这种数字创新在儿科试验中很少使用。在本观点文章中,我们建议使用虚拟患者数据来加强儿科试验。使用虚拟患者数据具有以下优点:减少儿童接触潜在无效或有风险干预措施的机会、缩短试验持续时间从而更快确定干预措施的安全性和有效性,以及加快药物审批。使用虚拟患者数据可以带来成本低廉的更个性化治疗选择,并可能使针对儿童的干预措施更快地在临床中得到应用。然而,在广泛采用虚拟患者数据之前,应解决包括用数字数据取代人类、数据隐私和安全等伦理和监管问题,并确保数字数据创新的安全性和可持续性。