ArticleCPT: pharmacometrics & systems pharmacology2021
Prediction of overall survival in patients across solid tumors following atezolizumab treatments: A tumor growth inhibition-overall survival modeling framework.
Article in CPT: pharmacometrics & systems pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed, 33 citations in OpenAlex.
- External validation of a tumor growth inhibition-overall survival model in non-small-cell lung cancer based on atezolizumab studies using alectinib data.Cancer chemotherapy and pharmacology · 2023Trial
- Tumor Dynamic Model-Based Decision Support for Phase Ib/II Combination Studies: A Retrospective Assessment Based on Resampling of the Phase III Study IMpower150.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Trial
- Harnessing Quantitative Medicine to Advance Oncology Drug Development.Clinical and translational science · 2026Review
- Bridging tumor growth dynamics and survival in preclinical oncology drug development - a Tumor Growth Inhibition and Time-to-Event (TGI-TTE) modeling framework.Cancer chemotherapy and pharmacology · 2026Article
- Integration of hierarchical clustering in hematologic biomarkers prediction for endocrine toxicity during immunotherapy in lung cancer patients.Scientific reports · 2026Article
- Evaluating the Impact of Enfortumab Vedotin Dose Modifications on Clinical Efficacy Using Tumor Growth Inhibition Modeling in Patients With Advanced Urothelial Cancer.Journal of clinical pharmacology · 2026Article
- Quantitative Lesion-Level Modeling Reveals Organ-Dependent Therapeutic Response.Clinical pharmacology and therapeutics · 2026Article
- Bridging Tumor Growth Dynamics and Survival in Preclinical Oncology Drug Development - A Translational Tumor Growth Inhibition and Time to Event (TGI-TTE) Modeling Framework.Research square · 2026Article
- Longitudinal Tumor Size and Survival Modeling for Exposure-Response Analysis of Drugs with Frequent Dose Reductions: Dose Justification of Abemaciclib in Patients with Metastatic Breast Cancer.Clinical pharmacology and therapeutics · 2026Article
- Cancer interception during treatment: using growth kinetics to create a continuous variable for assessing disease response.The oncologist · 2025Article
- 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
- Predicting survival in prospective clinical trials using weakly-supervised QSP.NPJ precision oncology · 2025Article
- Tutorial on Conditional Simulations With a Tumor Size-Overall Survival Model to Support Oncology Drug Development.CPT: pharmacometrics & systems pharmacology · 2025Article
- Future Directions for Quantitative Systems Pharmacology.Handbook of experimental pharmacology · 2025Review
- Improving Collection and Analysis of Overall Survival Data.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024Review
- Population pharmacokinetics of adebrelimab - Support of alternative flat dose regimen in extensive-stage small-cell lung cancer.CPT: pharmacometrics & systems pharmacology · 2024Article
- Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development.CPT: pharmacometrics & systems pharmacology · 2024Review
- Tumor growth inhibition-overall survival modeling in non-small cell lung cancer: A case study from GEMSTONE-302.CPT: pharmacometrics & systems pharmacology · 2024Article
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
Abstract
The objectives of the study were to use tumor size data from 10 phase II/III atezolizumab studies across five solid tumor types to estimate tumor growth inhibition (TGI) metrics and assess the impact of TGI metrics and baseline prognostic factors on overall survival (OS) for each tumor type. TGI metrics were estimated from biexponential models and posttreatment longitudinal data of 6699 patients. TGI-OS full models were built using parametric survival regression by including all significant baseline covariates from the Cox univariate analysis followed by a backward elimination step. The model performance was evaluated for each trial by 1000 simulations of the OS distributions and hazard ratios (HR) of the atezolizumab-containing arms versus the respective controls. The tumor growth rate estimate was the most significant predictor of OS across all tumor types. Several baseline prognostic factors, such as inflammatory status (C-reactive protein, albumin, and/or neutrophil-to-lymphocyte ratio), tumor burden (sum of longest diameters, number of metastatic sites, and/or presence of liver metastases), Eastern Cooperative Oncology Group performance status, and lactate dehydrogenase were also highly significant across multiple studies in the final multivariate models. TGI-OS models adequately described the OS distribution. The model-predicted HRs indicated good model performance across the 10 studies, with observed HRs within the 95% prediction intervals for all study arms versus controls. Multivariate TGI-OS models developed for different solid tumor types were able to predict treatment effect with various atezolizumab monotherapy or combination regimens and could be used to support design and analysis of future studies.
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