From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling

arXiv - QuanBio - Other Quantitative Biology Pub Date : 2024-03-05 DOI:arxiv-2403.03335

Hanwen WangDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA, Theinmozhi ArulrajDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA, Alberto IppolitoDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA, Aleksander S. PopelDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USADepartments of Medicine and Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA

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Abstract

Virtual patients and digital patients/twins are two similar concepts gaining increasing attention in health care with goals to accelerate drug development and improve patients' survival, but with their own limitations. Although methods have been proposed to generate virtual patient populations using mechanistic models, there are limited number of applications in immuno-oncology research. Furthermore, due to the stricter requirements of digital twins, they are often generated in a study-specific manner with models customized to particular clinical settings (e.g., treatment, cancer, and data types). Here, we discuss the challenges for virtual patient generation in immuno-oncology with our most recent experiences, initiatives to develop digital twins, and how research on these two concepts can inform each other.

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免疫肿瘤学中从虚拟患者到数字双胞胎：从机理定量系统药理学建模中汲取的经验教训

虚拟病人和数字病人/双胞胎是两个类似的概念，在医疗保健领域日益受到关注，其目标是加快药物开发和提高病人生存率，但也有各自的局限性。虽然有人提出了利用机理模型生成虚拟患者群体的方法，但在免疫肿瘤学研究中的应用数量有限。此外，由于数字孪生有更严格的要求，它们通常是以特定研究的方式生成的，模型是根据特定临床环境（如治疗、癌症和数据类型）定制的。在此，我们将讨论免疫肿瘤学虚拟病人生成所面临的挑战，以及我们的最新经验、开发数字孪生的举措，以及这两个概念的研究如何相互借鉴。

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arXiv - QuanBio - Other Quantitative Biology

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