Combining mechanistic quantitative systems pharmacology modeling and patient-derived organoid testing in MET-aberrant non-small cell lung cancer for high-throughput combination efficacy analysis and personalized treatment design

作者：Jingjing Hu, Yanyong Zhao, Qi Rao, Geli Li, Zichen Jiao, Haoxiang Wang, Yuchen Qu, Shihui Xu, Zhongze Gu, Tao Wang, Zaozao Chen, Chen Zhao, Guohua Zhou · 发表于：Frontiers in Pharmacology · 年份：2025 · DOI：10.3389/fphar.2025.1685468 · 被引用次数：4 · 研究领域：Lung Cancer Treatments and Mutations、Liver physiology and pathology、Melanoma and MAPK Pathways

Non-small cell lung cancer (NSCLC) harboring MET exon 14 skipping mutations or MET overexpression/amplification typically exhibits highly proliferative and invasive phenotypes and is a significant threat to human health. Although tyrosine kinase inhibitors targeting MET have been approved for clinical use over the past decade, treatment for MET-aberrant patients still face large unmet needs with issues such as limited response duration and drug resistance, low response rates and need for effective combination therapies, differential treatment response in patient subgroups, as well as clinical dose optimization and possibility of personalized medicine. To address these challenges, we developed a quantitative systems pharmacology (QSP) model that mechanistically recapitulated the complex regulation within the MET signaling network and integrated multiscale preclinical-clinical datasets for a total of 16 candidate drugs to drive translational drug research. This comprehensive QSP model framework, upon rigorous stepwise calibration and validation, has enabled high-throughput clinical efficacy analysis of different emerging combination therapies across varying dose ranges, offering crucial insights for drug development and dose optimization in MET-aberrant patients. We further integrated cancer patient-derived organoid (PDO) data on drug sensitivity into the QSP framework and explored the translational utility of this hybrid drug analysis paradigm towards the design of optimal per...