Recent applications of quantitative systems pharmacology and machine learning models across diseases

Authors

Sara Sadat Aghamiri1, University of Nebraska- Lincoln
Rada Amin, University of Nebraska-LincolnFollow
Tomáš Helikar, University of Nebraska-LincolnFollow

Document Type

Article

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

Comments

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021

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.