NAM‑informed quantitative systems pharmacology: Advancing human‑relevant modeling for translational cancer immunotherapy

The field of cancer immunotherapy and immunology is rapidly reshaping modern oncology, yet the translational gap between mechanistic discoveries and durable patient benefit remains a persistent challenge. Breakthroughs such as immune checkpoint inhibitors, adoptive cellular therapies, antibody–drug conjugates, and bispecific T cell engagers have revolutionized treatment paradigms, but their success has exposed the limits of existing preclinical systems. Conventional in vitro and in vivo models often fail to recapitulate the complexity of human tumor–immune interactions, resulting in unpredictable clinical outcomes. To overcome this limitation, the next generation of immunotherapy research demands the integration of human‑relevant experimental systems with advanced computational modeling frameworks capable of predicting therapeutic efficacy and resistance mechanisms.This Research Topic aims to bring together innovative advances in new approach methodologies (NAMs) and quantitative systems pharmacology (QSP) to strengthen the translational continuum in cancer immunology. While NAM platforms and QSP modeling have shown individual promise, their systematic integration remains underexplored. We seek contributions that demonstrate how human‑relevant preclinical models—such as patient‑derived organoids, humanized mouse models, organ‑on‑chip, and other microphysiological systems—can parameterize, validate, and refine mechanistic QSP frameworks. This integration will not only improve the predictive power of immune response models but also facilitate precision approaches that connect molecular mechanisms to therapeutic outcomes. Emerging tools such as digital twins, multi‑omics data fusion, and artificial intelligence can further enhance these efforts, offering quantitative insights into treatment optimization and resistance evolution across diverse immunotherapy contexts.Submissions should emphasize translational relevance and functional validation, showing how experimental data and computational predictions jointly advance understanding of tumour–immune dynamics and therapeutic response. All studies must include rigorous validation—through independent cohorts, biological experiments, or clinical confirmation—and discuss reproducibility and model generalizability. We welcome articles and reviews addressing, but not limited to:• NAM‑informed QSP modeling: Development and use of patient‑derived or humanized platforms to calibrate and validate mechanistic immunotherapy models.• Translational QSP frameworks: Model‑based strategies demonstrating preclinical‑to‑clinical translation across immune‑targeting therapies.• Integrative technologies: Use of digital twins, machine learning, and multi‑omics analysis when coupled with NAM‑QSP frameworks for predictive and mechanistic insight.Please note: Manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.Please note that Dr. Hanwen Wang is an employee of AstraZeneca, and Dr. Huilin Ma is an employee of Bristol Myers Squibb. Topic editors: Huilin Ma, Hanwen Wang, Jiao Zheng Source: https://www.frontiersin.org/research-topics/77618/nam-informed-quantitative-systems-pharmacology-advancing-human-relevant-modeling-for-translational-cancer-immunotherapy