AbstractVirtual 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.

摘要虚拟患者和数字患者/双胞胎是两个类似的概念，在医疗保健中越来越受到关注，其目标是加速药物开发和提高患者的生存率，但有其自身的局限性。尽管已经提出了使用机械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。

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..

此外，由于数字双胞胎的要求更为严格，它们通常以特定于研究的方式生成，并根据特定的临床环境（例如治疗，癌症和数据类型）定制模型。在这里，我们讨论了免疫肿瘤学中虚拟患者生成的挑战，以及我们最近的经验，开发数字双胞胎的举措，以及对这两个概念的研究如何相互借鉴。。

IntroductionCancer is a leading cause of death worldwide with the lowest success rate of clinical trials among all complex diseases. In an analysis of clinical trials from 2000 to 2015, the overall probability (defined by Wong et al. 1) of a drug successfully moving from phase I to approval was merely 3.4% in oncology, but for trials that used biomarkers for patient selection, the probability of success was significantly improved1.

引言癌症是全球死亡的主要原因，在所有复杂疾病中，临床试验的成功率最低。在2000年至2015年的临床试验分析中，药物从I期成功进入批准的总体概率（由Wong等人定义）在肿瘤学中仅为3.4%，但对于使用生物标志物进行患者选择的试验，成功的可能性显着提高1。

With increasing number of newly discovered drugs and potential biomarkers to investigate, it is extremely difficult to test and compare all dose levels, therapy combinations, and predictive biomarkers for each cancer type via clinical trials, which necessitates development of computational tools to accelerate the process.Since 1990s, mathematical models have and continue to play important roles in drug development as a cost-efficient tool to inform clinical trial design (i.e., model-informed drug development or MIDD)2.

随着新发现的药物和潜在生物标志物数量的增加，通过临床试验测试和比较每种癌症类型的所有剂量水平，治疗组合和预测性生物标志物是极其困难的，这需要开发计算工具来加速这一过程。自20世纪90年代以来，数学模型已经并将继续在药物开发中发挥重要作用，作为告知临床试验设计（即模型知情药物开发或MIDD）的具有成本效益的工具2。

Semi-mechanistic approaches like pharmacokinetic-pharmacodynamic (PKPD) models started first to accompany regulatory submissions, and, with advancing mechanistic understanding of pathophysiology and increasing computational power, mechanistic models, such as physiologically-based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) models, were developed2,3.

药代动力学-药效学（PKPD）模型等半机械方法首先开始伴随监管提交，随着对病理生理学的机械理解和计算能力的提高，机械模型，如基于生理的药代动力学（PBPK）和定量系统药理学（QSP）模型，被开发出来2,3。

From 2013 to 2020, the US Food and Drug Administration has received a rising number of new drug applications with the support of QSP models, more than one fifth of which were for oncologic diseases4. Therefore, hypothesis-driven mechanistic QSP modeling has begun to play a critical role in predicting effectiveness of newly discovered drugs and determining the optimal dosage to assist clinical trial design via clinical trial simulation (i.e., in silico/virtual .

从2013年到2020年，美国食品和药物管理局在QSP模型的支持下收到了越来越多的新药申请，其中超过五分之一用于肿瘤疾病4。因此，假设驱动的机制QSP建模已经开始在预测新发现药物的有效性和确定通过临床试验模拟（即计算机/虚拟）辅助临床试验设计的最佳剂量方面发挥关键作用。

Data availability

数据可用性

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

数据共享不适用于本文，因为在当前研究期间没有生成或分析数据集。

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Download referencesAcknowledgementsThis work is supported in part by NIH grant R01CA138264.Author informationAuthors and AffiliationsDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USAHanwen Wang, Theinmozhi Arulraj, Alberto Ippolito & Aleksander S.

下载参考文献致谢这项工作得到了NIH grant R01CA138264的部分支持。作者信息作者和附属机构约翰·霍普金斯大学医学院生物医学工程系，马里兰州巴尔的摩，USAHanwen Wang，Theinmozhi Arulraj，Alberto Ippolito＆Aleksander S。

PopelDepartments of Medicine and Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USAAleksander S. PopelAuthorsHanwen WangView author publicationsYou can also search for this author in.

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PubMed Google ScholarContributionsAll authors conceptualized this study. A.S.P. supervised this study and acquired funding. H.W., T.A., and A.I. designed the methodology. H.W. analyzed the results and wrote the manuscript. All authors reviewed and revised the manuscript.Corresponding authorCorrespondence to.

PubMed谷歌学术贡献所有作者都将这项研究概念化。A、 S.P.监督了这项研究并获得了资金。H、 W.，T.A。和A.I.设计了该方法。H、 W.分析了结果并撰写了手稿。所有作者都审阅并修订了手稿。对应作者对应。

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A.S.P. is a consultant to Incyte, J&J/Janssen, and AsclepiX Therapeutics. The terms of these arrangements are being managed by the Johns Hopkins University in accordance with its conflict-of-interest policies. A.I. is a current employee of AstraZeneca PLC. The remaining authors declare no competing interests..

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Reprints and permissionsAbout this articleCite this articleWang, H., Arulraj, T., Ippolito, A. et al. From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling.

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