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

Sara Sadat Aghamiri, Rada Amin, Tomáš Helikar

Research output: Contribution to journalReview articlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)19-37
Number of pages19
JournalJournal of Pharmacokinetics and Pharmacodynamics
Volume49
Issue number1
DOIs
StatePublished - Feb 2022

Keywords

  • Immuno-oncology
  • Immunotherapy
  • Machine learning
  • Predictive models
  • Quantitative systems pharmacology
  • Systems biology

ASJC Scopus subject areas

  • Pharmacology

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