Biomedical Engineering Department and Chemical & Biochemical Engineering Department, Rutgers, The State University of New Jersey, New Brunswick, USA.
J Pharmacokinet Pharmacodyn. 2024 Oct;51(5):521-531. doi: 10.1007/s10928-022-09820-0. Epub 2022 Aug 13.
Quantitative Systems Pharmacology (QSP) has emerged as a powerful ensemble of approaches aiming at developing integrated mathematical and computational models elucidating the complex interactions between pharmacology, physiology, and disease. As the field grows and matures its applications expand beyond the boundaries of research and development and slowly enter the decision making and regulatory arenas. However, widespread acceptance and eventual adoption of a new modeling approach requires assessment criteria and quantifiable metrics that establish credibility and increase confidence in model predictions. QSP aims to provide an integrated understanding of pathology in the context of therapeutic interventions. Because of its ambitious nature and the fact that QSP emerged in an uncoordinated manner as a result of activities distributed across organizations and academic institutions, high entropy characterizes the tools, methods, and computational methodologies and approaches used. The eventual acceptance of QSP model predictions as supporting material for an application to a regulatory agency will require that two key aspects are considered: (1) increase confidence in the QSP framework, which drives standardization and assessment; and (2) careful articulation of the expectations. Both rely heavily on our ability to rigorously and consistently assess QSP models. In this manuscript, we wish to discuss the meaning and purpose of such an assessment in the context of QSP model development and elaborate on the differentiating features of QSP that render such an endeavor challenging. We argue that QSP establishes a conceptual, integrative framework rather than a specific and well-defined computational methodology. QSP elicits the use of a wide variety of modeling and computational methodologies optimized with respect to specific applications and available data modalities, which exceed the data structures employed by chemometrics and PK/PD models. While the range of options fosters creativity and promises to substantially advance our ability to design pharmaceutical interventions rationally and optimally, our expectations of QSP models need to be clearly articulated and agreed on, with assessment emphasizing the scope of QSP studies rather than the methods used. Nevertheless, QSP should not be considered an independent approach, rather one of many in the broader continuum of computational models.
定量系统药理学（QSP）已成为一种强大的方法集合，旨在开发综合的数学和计算模型，阐明药理学、生理学和疾病之间的复杂相互作用。随着该领域的发展和成熟，其应用范围已超越研发领域的边界，逐渐进入决策制定和监管领域。然而，广泛接受和最终采用新的建模方法需要评估标准和可量化的指标，以建立模型预测的可信度并增强信心。QSP 的目的是提供治疗干预背景下病理学的综合理解。由于其雄心勃勃的性质，以及 QSP 是作为一个分散在组织和学术机构中的活动的结果而以不协调的方式出现的事实，因此所使用的工具、方法和计算方法和方法具有很高的复杂性。最终将 QSP 模型预测作为向监管机构提交申请的支持材料被接受，需要考虑两个关键方面：（1）提高对 QSP 框架的信心，这推动了标准化和评估；（2）谨慎阐明预期。这两个方面都严重依赖于我们严格和一致地评估 QSP 模型的能力。在本文中，我们希望在 QSP 模型开发的背景下讨论这种评估的含义和目的，并详细阐述 QSP 的区别特征，这些特征使得这项工作具有挑战性。我们认为，QSP 建立了一个概念性的、综合性的框架，而不是一个特定的、定义明确的计算方法。QSP 引发了对各种建模和计算方法的使用，这些方法针对特定的应用和可用的数据模式进行了优化，超出了化学计量学和 PK/PD 模型所使用的数据结构。虽然选择范围促进了创造力，并有望极大地提高我们合理和优化设计药物干预的能力，但我们对 QSP 模型的期望需要明确阐明并达成一致，评估重点是 QSP 研究的范围，而不是所使用的方法。尽管如此，QSP 不应被视为一种独立的方法，而应是更广泛的计算模型连续体中的众多方法之一。
