ArticleCPT: pharmacometrics & systems pharmacology2024
Tumor growth inhibition-overall survival modeling in non-small cell lung cancer: A case study from GEMSTONE-302.
Article in CPT: pharmacometrics & systems pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Novel Drug-Disease Modeling Framework for Oncology Benefit-Risk Evaluation: Application to Tusamitamab Ravtansine.CPT: pharmacometrics & systems pharmacology · 2026Article
- Application of Machine Learning for Predicting Progression-Free and Overall Survival in Patients With Renal Cell Carcinoma.Clinical and translational science · 2025Article
- Disease Modeling and External Model Evaluation Through Clinical Data Sharing Platform for HR+/HER2- Breast Cancer.CPT: pharmacometrics & systems pharmacology · 2025Article
- Development of a Joint Tumor Size-Overall Survival Modeling and Simulation Framework Supporting Oncology Development Decision-Making.CPT: pharmacometrics & systems pharmacology · 2025Article
- Tutorial on Conditional Simulations With a Tumor Size-Overall Survival Model to Support Oncology Drug Development.CPT: pharmacometrics & systems pharmacology · 2025Article
- A sample size analysis of a mathematical model of longitudinal tumor volume and progression-free survival for Bayesian individual dynamic predictions in recurrent high-grade glioma.CPT: pharmacometrics & systems pharmacology · 2025Article
- Tumor growth inhibition-overall survival modeling in non-small cell lung cancer: A case study from GEMSTONE-302.CPT: pharmacometrics & systems pharmacology · 2024Article
- Assessing immunotherapy response: going beyond RECIST by integrating early tumor growth kinetics.Frontiers in immunology · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Overall survival is vital for approving new anticancer drugs but is often impractical for early-phase studies. The tumor growth inhibition-overall survival (TGI-OS) model could bridge the gap between early- and late-stage development. This study aimed to identify an appropriate TGI-OS model for patients with non-small cell lung cancer from the GEMSTONE-302 study of sugemalimab. We used three TGI models to delineate tumor trajectories and investigated three OS model for linking TGI metric to OS. All three TGI models accurately captured tumor profiles at the individual level. The published atezolizumab-based TGI-OS model predicted survival time satisfactorily through simulation-based evaluation, whereas the other published model built from multi-treatment underestimated OS. Our study-specific TGI-OS model identified time-to-growth as the most significant metric with the number of metastatic sites and neutrophil-to-lymphocyte ratio at baseline as covariates and exhibited robust OS predictability. Our findings demonstrated the effectiveness of the TGI-OS models in predicting phase III outcomes, which underpins their value as a powerful tool for antitumor drug development.
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