Molecular, Cellular and Developmental Biology Unit (MCD), Centre de Biologie Integrative (CBI), University of Toulouse, UPS, CNRS, Toulouse, France.
Lifeware Group, Inria, Saclay-île de France, Palaiseau, France.
NPJ Syst Biol Appl. 2024 Nov 30;10(1):141. doi: 10.1038/s41540-024-00450-5.
Digital twins represent a key technology for precision health. Medical digital twins consist of computational models that represent the health state of individual patients over time, enabling optimal therapeutics and forecasting patient prognosis. Many health conditions involve the immune system, so it is crucial to include its key features when designing medical digital twins. The immune response is complex and varies across diseases and patients, and its modelling requires the collective expertise of the clinical, immunology, and computational modelling communities. This review outlines the initial progress on immune digital twins and the various initiatives to facilitate communication between interdisciplinary communities. We also outline the crucial aspects of an immune digital twin design and the prerequisites for its implementation in the clinic. We propose some initial use cases that could serve as "proof of concept" regarding the utility of immune digital technology, focusing on diseases with a very different immune response across spatial and temporal scales (minutes, days, months, years). Lastly, we discuss the use of digital twins in drug discovery and point out emerging challenges that the scientific community needs to collectively overcome to make immune digital twins a reality.
数字孪生代表了精准健康的关键技术。医疗数字孪生由计算模型组成，这些模型代表了个体患者随时间推移的健康状态，从而实现最佳治疗和预测患者预后。许多健康状况都涉及免疫系统，因此在设计医疗数字孪生时，必须包含其关键特征。免疫反应复杂，在疾病和患者之间存在差异，其建模需要临床、免疫学和计算建模领域的集体专业知识。这篇综述概述了免疫数字孪生的初步进展以及促进跨学科社区之间交流的各种举措。我们还概述了免疫数字孪生设计的关键方面及其在临床中的实施前提。我们提出了一些初始用例，这些用例可以作为免疫数字技术效用的“概念验证”，重点关注在空间和时间尺度（分钟、天、月、年）上具有非常不同免疫反应的疾病。最后，我们讨论了数字孪生在药物发现中的应用，并指出科学界需要共同克服的新兴挑战，以使免疫数字孪生成为现实。
