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We developed a spatial quantitative systems pharmacology (QSP) framework integrating ADC\u2010analyte physiologically based pharmacokinetics (PBPK), a Krogh\u2010cylinder tumor model, and a non\u2010small cell lung cancer (NSCLC) tumor size\u2010overall survival (TS\u2010OS) relationship developed without ADC\u2010treated populations. Applied to trastuzumab deruxtecan (T\u2010DXd) in HER2\u2010mutant NSCLC and datopotamab deruxtecan (Dato\u2010DXd), a TROP2\u2010directed ADC, in EGFR\u2010mutated NSCLC, all parameters came from literature or independent assays; only nonspecific antibody uptake and unconjugated\u2010payload hepatic clearances were estimated from population\u2010level T\u2010DXd serum PK. Using a common structure and virtual\u2010patient framework with independently informed ADC\u2010specific inputs, simulations generated PK, objective response rate (ORR), disease control rate (DCR), and exploratory OS trends consistent with clinical data. Heterogeneous tumor trajectories emerged without fitting individual trajectories, adding ad hoc response compartments, or retuning tumor growth solely to reproduce ORR\/DCR. Tumor growth kinetics and target\u2010expression characteristics, represented by histological score and antigen\u2010positive cell fraction, were associated with response, but responder\/non\u2010responder distributions overlapped, indicating limited discrimination and need for predictive biomarkers. The TS\u2010OS sub\u2010model reproduced median OS and survival trends, supporting transportability despite absent individual\u2010level tumor and survival data. Tumor growth rate was the strongest survival\u2010associated input, consistent with TS\u2010OS covariate structure. Across doses, 5.4\u2009mg\/kg Q3W T\u2010DXd and 6\u2009mg\/kg Q3W Dato\u2010DXd were preferred based on predicted efficacy and off\u2010tumor tissue payload exposure, consistent with approved doses. The framework supports ADC design and dose evaluation despite limited individual\u2010level data.<\/jats:p>","DOI":"10.1002\/psp4.70352","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T14:05:42Z","timestamp":1791554742000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating a Tumor Size\u2010Overall Survival Model Into a Spatial Quantitative Systems Pharmacology Framework for Antibody\u2010Drug Conjugates in Non\u2010Small Cell Lung Cancer"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4575-2727","authenticated-orcid":false,"given":"Mehdi","family":"Nikfar","sequence":"first","affiliation":[{"name":"Clinical Pharmacology Modeling and Simulation, GlaxoSmithKline  Collegeville Pennsylvania 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