ABSTRACT
Quantitative systems pharmacology (QSP) modeling is an emerging computational medicine approach with growing applications and significance in modern drug development. QSP models are generally formulated based on multiscale disease mechanisms and drug-target interactions, which makes them capable of integrating multimodal data from the preclinical and clinical space. This also enables them to generate quantitative characterization of the dynamic disease progression as well as high-throughput predictions of drug-induced efficacy and toxicity signals. Therefore, QSP modeling and model-based virtual clinical trials have been widely implemented to guide drug development, in scenarios such as target identification and assessment, clinical trial design, evaluation of combination therapy and biomarkers, and personalized medicine. In US and Europe, QSP modeling has been developing rapidly in the past 10 years and is now an integral part of the model-informed drug development paradigm; however, in China it is still a nascent field. Here we will present a comprehensive review of the recent advancements of QSP and its impact in modern drug development through a number of case studies. This review will provide guidance for the future drug development efforts and the growth of QSP practice in China.
ABSTRACT
The high mortality of tumor is one of the most urgent problems to be solved. However, the current clinical trials provide limited quantitative descriptions on dynamic changes of drug efficacy, which restrict the selection of dosing regimens. Quantitative systems pharmacology (QSP) is a new approach for precise treatment of tumors. It quantifies the network relationship between drug action and diseases by integrating the tumor growth and molecules, cells in vivo, thereby predicting the efficacy, toxicity, and mechanism of antitumor drugs as well as identifying predictive biomarkers. In this review, we provide an overview of definition of QSP, current approaches and typical applications in research of antitumor drugs to enhance our understanding of QSP.