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

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.

Keywords: Immuno-oncology; Immunotherapy; Machine learning; Predictive models; Quantitative systems pharmacology; Systems biology.

Fig. 1

The recently published QSP models and their disease areas. The bar chart presents the number of articles published between 2019 and 2021 for developing original QSP models. Categorizing these articles based on the biological questions they focused on (presented by their MeSH terms), revealed that most models are related to neoplasms

Fig. 2

Application of Machine learning in supporting challenges and limitations of quantitative system pharmacology