Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.
J Pharmacokinet Pharmacodyn. 2022 Feb;49(1):19-37. doi: 10.1007/s10928-021-09790-9. Epub 2021 Oct 20.
Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019-2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development.
定量系统药理学（QSP）是一个定量和机制平台，用于描述药物、生物网络和疾病状况之间的表型相互作用，以预测最佳治疗反应。在这项荟萃分析研究中，我们根据最近的出版物（2019-2021 年）回顾了 QSP 平台在药物开发和治疗策略中的应用。我们收集了最近的原始 QSP 模型，并根据治疗领域、方法学、软件平台和功能描述了它们应用的多样性。收集和调查这些出版物可以帮助提供最近的 QSP 研究存储库，以促进 QSP 模型的发现和进一步重用。我们的综述表明，近年来 QSP 研究的最大数量集中在肿瘤免疫治疗领域。我们还通过介绍机器学习方法在药物发现和 QSP 模型中的应用，讨论了该领域综合方法的优势。基于这项荟萃分析，我们讨论了 QSP 模型的优点和局限性，并提出了 QSP 方法构成更深入研究复杂疾病和改善药物开发有价值接口的领域。
