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

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.

Automated summary

Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform that describes the interactions between drugs, biological networks, and disease conditions to predict optimal therapeutic responses. Recent studies have shown a growing diversity in the applications of QSP models in various therapeutic areas, methodologies, and software platforms, which can facilitate the discovery and reusability of these models. The meta-analysis highlights the increasing focus on Immuno-Oncology within QSP efforts, as well as the benefits of integrative approaches and Machine Learning methods in drug discovery and model development.