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

Autor: Aghamiri SS; Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA., Amin R; Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA. raminali2@unl.edu., Helikar T; Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA. thelikar2@unl.edu.
Jazyk: angličtina
Zdroj: Journal of pharmacokinetics and pharmacodynamics [J Pharmacokinet Pharmacodyn] 2022 Feb; Vol. 49 (1), pp. 19-37. Date of Electronic Publication: 2021 Oct 20.
DOI: 10.1007/s10928-021-09790-9
Abstrakt: 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.
(© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze: MEDLINE
Externí odkaz: