Biochemistry, Department of
Recent applications of quantitative systems pharmacology and machine learning models across diseases
Date of this Version
10-7-2021
Citation
Journal of Pharmacokinetics and Pharmacodynamics https://doi.org/10.1007/s10928-021-09790-9(0123456789().,-volV)(0123456789,-().volV
Abstract
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.
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Comments
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021