Kleeberger Jan A
Department of Cardiology, Universität Zürich, Zürich, CHE.
Center for Translational and Experimental Cardiology (CTEC), Universität Zürich, Zürich, CHE.
Cureus. 2025 Jun 4;17(6):e85380. doi: 10.7759/cureus.85380. eCollection 2025 Jun.
The pharmaceutical industry faces significant challenges due to the prolonged development timelines, high failure rates of innovative drugs, and escalating regulatory demands for robust data. A novel solution to address these challenges is the utilization of virtual patient cohorts to simulate drug effects in computer models. This review article provides an overview of the application of virtual patient cohorts in drug development and the diverse methodologies employed, based on a comprehensive literature review. Complementing previous reviews, this work emphasizes the practical applications of virtual patients across various disease conditions, especially rare diseases where patient recruitment is particularly challenging. It also highlights the challenges of conventional trial methodologies, such as limited generalizability, ethical concerns, and recruitment barriers, and discusses virtual patients as a potential solution to these longstanding issues. Since all literature was included according to the judgment of a single reviewer, a potential selection bias must be taken into account when reading this article. Virtual patients can be generated as digital twins through statistical inference or by randomly assigning parameters, followed by plausibility assessments, with the method choice depending upon study objectives and available data. These cohorts find utility across all clinical phases of drug development. The principal advantages of leveraging virtual patient cohorts include potential cost savings through heightened development success and increased innovation. Moreover, they offer improved representation of patient groups often marginalized in drug development efforts. However, it is imperative to acknowledge the computational nature of virtual patients, which can yield erroneous outcomes and necessitate substantial expertise and computational resources. Currently, there is a lack of standardized protocols for generating and utilizing virtual patient cohorts. Nonetheless, virtual patient cohorts hold promise in fundamentally transforming drug development and patient treatment approaches. By creating realistic virtual patients, more efficient and personalized drug development strategies can be pursued. Integrating this technology alongside in vitro and in vivo studies, while considering their respective limitations, might significantly enhance the success rate of drug development across the pipeline, ultimately advancing patient health outcomes.
由于研发周期延长、创新药物失败率高以及对可靠数据的监管要求不断提高，制药行业面临着重大挑战。应对这些挑战的一种新解决方案是利用虚拟患者队列在计算机模型中模拟药物效果。本文基于全面的文献综述，概述了虚拟患者队列在药物研发中的应用以及所采用的各种方法。与之前的综述相辅相成，这项工作强调了虚拟患者在各种疾病状况下的实际应用，特别是在患者招募特别具有挑战性的罕见疾病中。它还强调了传统试验方法的挑战，如普遍性有限、伦理问题和招募障碍，并讨论了虚拟患者作为解决这些长期问题的潜在方案。由于所有文献都是根据单一审稿人的判断纳入的，因此在阅读本文时必须考虑到潜在的选择偏差。虚拟患者可以通过统计推断或随机分配参数生成数字孪生，然后进行合理性评估，方法的选择取决于研究目标和可用数据。这些队列在药物研发的所有临床阶段都有用处。利用虚拟患者队列的主要优势包括通过提高研发成功率和增加创新来节省潜在成本。此外，它们能更好地代表在药物研发工作中经常被边缘化的患者群体。然而，必须认识到虚拟患者的计算性质，这可能会产生错误结果，并且需要大量专业知识和计算资源。目前，缺乏用于生成和利用虚拟患者队列的标准化协议。尽管如此，虚拟患者队列有望从根本上改变药物研发和患者治疗方法。通过创建逼真的虚拟患者，可以追求更高效和个性化的药物研发策略。将这项技术与体外和体内研究相结合，同时考虑它们各自的局限性，可能会显著提高整个研发流程的药物研发成功率，最终改善患者健康结果。
