Weirong Wang, Alexander V Ratushny, Steve Chang, Yougan Cheng, Jingqi Q X Gong, Abhishek Gulati, Emma Hansson, Alexander Kulesza, Federico Reali, Conner Sandefur, Brian J Schmidt, Fulya Akpinar Singh, Monica E Susilo, Susana Zaph, Blerta Shtylla
Quantitative systems pharmacology (QSP) integrates mechanistic representations of biology with quantitative pharmacology to support decision‑making across the drug development continuum. Over the past two decades, QSP has evolved from an exploratory research activity into an established component of model‑informed drug development (MIDD), particularly in settings where system‑level interactions complicate the interpretation of exposure-response relationships or where direct clinical data are limited. This review outlines the historical development and conceptual foundations of QSP and summarizes representative clinical applications spanning early, mid, and late development. Examples highlight how QSP is used to support mechanism-based dose and regimen selection, optimization of combination strategies, biomarker‑informed patient stratification, and lifecycle management decisions. We also discuss considerations for rigor, credibility, and trustworthiness in alignment with the ICH M15 framework, emphasizing clearly defined questions of interest, context of use, and proportional, decision-driven evaluation of assumptions and uncertainty. Finally, we consider emerging challenges and opportunities for QSP adoption, including reuse of platform models, integration of multi-omics data, and selective incorporation of AI-enabled methods within mechanistically interpretable frameworks.