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. METHODS: Data were extracted from IMpower150, a positive, randomized, Phase III study of first-line therapy in 1202 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) vs. BCP, to mimic Phase Ib/II studies of 15-40 patients/arm with 6-24 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 vs. BCP and BCP vs. 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 over-performing RECIST endpoints. The predicted 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.
- Bruno, R.
- Marchand, M.
- Yoshida, K.
- Chan, P.
- Li, H.
- Zou, W.
- Mercier, F.
- Chanu, P.
- Wu, B.
- Lee, A.
- Li, C.
- Jin, J. Y.
- Maitland, M. L.
- Reck, M.
- Socinski, M. A.
