Laubenbacher R, Niarakis A, Helikar T, An G, Shapiro B, Malik-Sheriff R S, Sego T J, Knapp A, Macklin P, Glazier J A
Department of Medicine, University of Florida, Gainesville, FL, USA.
Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde - Genhotel, Univ Evry, Evry, France.
NPJ Digit Med. 2022 May 20;5(1):64. doi: 10.1038/s41746-022-00610-z.
Digital twins, customized simulation models pioneered in industry, are beginning to be deployed in medicine and healthcare, with some major successes, for instance in cardiovascular diagnostics and in insulin pump control. Personalized computational models are also assisting in applications ranging from drug development to treatment optimization. More advanced medical digital twins will be essential to making precision medicine a reality. Because the immune system plays an important role in such a wide range of diseases and health conditions, from fighting pathogens to autoimmune disorders, digital twins of the immune system will have an especially high impact. However, their development presents major challenges, stemming from the inherent complexity of the immune system and the difficulty of measuring many aspects of a patient's immune state in vivo. This perspective outlines a roadmap for meeting these challenges and building a prototype of an immune digital twin. It is structured as a four-stage process that proceeds from a specification of a concrete use case to model constructions, personalization, and continued improvement.
数字孪生是在工业领域率先开创的定制化仿真模型,如今正开始在医学和医疗保健领域得到应用,并取得了一些重大成功,例如在心血管诊断和胰岛素泵控制方面。个性化计算模型也在从药物研发到治疗优化等一系列应用中发挥着辅助作用。更先进的医学数字孪生对于实现精准医疗至关重要。由于免疫系统在从对抗病原体到自身免疫性疾病等如此广泛的疾病和健康状况中都发挥着重要作用,免疫系统的数字孪生将产生特别重大的影响。然而,它们的开发面临着重大挑战,这源于免疫系统固有的复杂性以及在体内测量患者免疫状态诸多方面的困难。本观点概述了应对这些挑战并构建免疫数字孪生原型的路线图。它被构建为一个四阶段过程,从具体用例的规范开始,依次进行模型构建、个性化定制和持续改进。