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

Abstract

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.

Keywords: Heart failure; Machine learning; Modeling and simulation; Physiological modeling; QSP modeling; Quantitative systems pharmacology.

Fig. 1

Diagram of a quantitative systems pharmacology model of heart failure

Fig. 2

An overview and illustration of machine learning assisted quantitative system pharmacology modeling for heart failure. It involves systems biology, physiology and pathophysiology, biochemistry, signaling pathways, and patient data