Related Experiment Video
Updated: Aug 15, 2025
A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
Published on: April 6, 2016
Tumor Dynamic Model-Based Decision Support for Phase Ib/II Combination Studies: A Retrospective Assessment Based on
René Bruno1, Mathilde Marchand2, Kenta Yoshida3
1Clinical Pharmacology, Genentech-Roche, Marseille, France.
Purpose:
Model-based tumor growth inhibition (TGI) metrics are increasingly incorporated into go/no-go decisions in early clinical studies. To apply this methodology to new investigational combinations requires independent evaluation of TGI metrics in recently completed Phase III trials of effective immunotherapy.
Patients And Methods:
Data were extracted from IMpower150, a positive, randomized, Phase III study of first-line therapy in 1,202 patients with non-small cell lung cancer. We resampled baseline characteristics and longitudinal sum of longest diameters of tumor lesions of patients from both arms, atezolizumab+ bevacizumab+chemotherapy (ABCP) versus BCP, to mimic Phase Ib/II studies of 15 to 40 patients/arm with 6 to 24 weeks follow-up. TGI metrics were estimated using a bi-exponential TGI model. Effect sizes were calculated as TGI metrics geometric mean ratio (GMR), objective response rate (ORR) difference (d), and progression-free survival (PFS), hazard ratio (HR) between arms. Correct and incorrect go decisions were evaluated as the probability to achieve desired effect sizes in ABCP versus BCP and BCP versus BCP, respectively, across 500 replicated subsamples for each design.
Results:
For 40 patients/24 weeks follow-up, correct go decisions based on probability tumor growth rate (KG) GMR <0.90, dORR >0.10, and PFS HR <0.70 were 83%, 69%, and 58% with incorrect go decision rates of 4%, 12%, and 11%, respectively. For other designs, the ranking did not change with TGI metrics consistently overperforming RECIST endpoints. The predicted overall survival (OS) HR was around 0.80 in most of the scenarios investigated.
Conclusions:
Model-based estimate of KG GMR is an exploratory endpoint that informs early clinical decisions for combination studies.
Insights
Model-based tumor growth inhibition metrics accurately predict clinical trial success for immunotherapy combinations. These metrics offer reliable go/no-go decision-making in early drug development for non-small cell lung cancer.
Area of Science:
- Oncology
- Clinical Trials
- Biostatistics
Background:
- Model-based tumor growth inhibition (TGI) metrics are crucial for go/no-go decisions in early clinical studies.
- Evaluating TGI metrics in Phase III trials of immunotherapy combinations is essential for validating their predictive power.
Purpose of the Study:
- To independently evaluate model-based TGI metrics using data from a Phase III trial of atezolizumab plus bevacizumab plus chemotherapy (ABCP) versus bevacizumab plus chemotherapy (BCP) in non-small cell lung cancer.
- To assess the performance of TGI metrics in mimicking Phase Ib/II study decision-making.
Main Methods:
- Resampled data from the IMpower150 Phase III trial (1,202 patients) to simulate smaller Phase Ib/II studies (15-40 patients/arm, 6-24 weeks follow-up).
- Estimated TGI metrics using a bi-exponential TGI model and calculated effect sizes (geometric mean ratio [GMR], objective response rate [ORR] difference, progression-free survival [PFS] hazard ratio [HR]).
- Evaluated correct and incorrect go decisions based on simulated study designs and TGI metric thresholds.
Main Results:
- For 40 patients and 24 weeks follow-up, correct go decision rates were 83% (KG GMR <0.90), 69% (dORR >0.10), and 58% (PFS HR <0.70), with low incorrect go decision rates (4-12%).
- TGI metrics consistently outperformed RECIST endpoints across various simulated designs.
- Predicted overall survival (OS) hazard ratio was consistently around 0.80.
Conclusions:
- Model-based estimation of tumor growth rate (KG) GMR serves as a valuable exploratory endpoint.
- These findings support the use of TGI metrics for informing early clinical decisions in investigational combination studies for non-small cell lung cancer.
More Related Videos
08:26Live Imaging of Drug Responses in the Tumor Microenvironment in Mouse Models of Breast Cancer
Published on: March 24, 2013
09:24Generation of Microtumors Using 3D Human Biogel Culture System and Patient-derived Glioblastoma Cells for Kinomic Profiling and Drug Response Testing
Published on: June 9, 2016
Related Concept Videos
02:50Combination Therapies and Personalized Medicine
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
01:21Cancer Survival Analysis