Review of applications and challenges of quantitative systems pharmacology modeling and machine learning for heart failure

Quantitative systems pharmacology (QSP) is an important approach in pharmaceutical research and development that facilitates in silico generation of quantitative mechanistic hypotheses and enables in silico trials. As demonstrated by applications from numerous industry groups and interest from regul...

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Bibliographic Details
Published in:Journal of pharmacokinetics and pharmacodynamics 2022-02, Vol.49 (1), p.39-50
Main Authors: Cheng, Limei, Qiu, Yuchi, Schmidt, Brian J., Wei, Guo-Wei
Format: Article
Language:English
Subjects:
Biochemistry
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Calibration
Clinical trials
Computer applications
Congestive heart failure
Deep learning
Drug development
Heart failure
Heart Failure - diagnosis
Heart Failure - drug therapy
Humans
Learning algorithms
Machine Learning
Models, Biological
Network Pharmacology
Pharmacology
Pharmacology/Toxicology
Pharmacy
R&D
Research & development
Review Paper
Veterinary Medicine/Veterinary Science
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Summary:Quantitative systems pharmacology (QSP) is an important approach in pharmaceutical research and development that facilitates in silico generation of quantitative mechanistic hypotheses and enables in silico trials. As demonstrated by applications from numerous industry groups and interest from regulatory authorities, QSP is becoming an increasingly critical component in clinical drug development. With rapidly evolving computational tools and methods, QSP modeling has achieved important progress in pharmaceutical research and development, including for heart failure (HF). However, various challenges exist in the QSP modeling and clinical characterization of HF. Machine/deep learning (ML/DL) methods have had success in a wide variety of fields and disciplines. They provide data-driven approaches in HF diagnosis and modeling, and offer a novel strategy to inform QSP model development and calibration. The combination of ML/DL and QSP modeling becomes an emergent direction in the understanding of HF and clinical development new therapies. In this work, we review the current status and achievement in QSP and ML/DL for HF, and discuss remaining challenges and future perspectives in the field.
ISSN:1567-567X
1573-8744
1573-8744
DOI:10.1007/s10928-021-09785-6