Literature DB >> 35484442

Tumor Growth Inhibition-Overall Survival (TGI-OS) Model for Subgroup Analysis Based on Post-Randomization Factors: Application for Anti-drug Antibody (ADA) Subgroup Analysis of Atezolizumab in the IMpower150 Study.

Kenta Yoshida1, Phyllis Chan2, Mathilde Marchand3, Rong Zhang2, Benjamin Wu2, Marcus Ballinger4, Nitzan Sternheim5, Jin Y Jin2, René Bruno6.   

Abstract

Longitudinal changes of tumor size or tumor-associated biomarkers have been receiving growing attention as early markers of treatment benefits. Tumor growth inhibition-overall survival (TGI-OS) models represent mathematical frameworks used to establish a link from tumor size trajectory to survival outcome with the aim of predicting survival benefit with tumor data from a small number of subjects with a short follow-up time. In the present study, we applied the TGI-OS model to assess treatment benefit in the IMpower150 study for patients who exhibited development of anti-drug antibodies (ADA). Direct comparison between subgroups of the active arm [ADA positive (ADA +) and negative (ADA -) groups] to the entire control group is not appropriate, due to potential imbalances of baseline prognostic factors between ADA + and ADA - patients. Thus, the TGI-OS modeling framework was employed to adjust for differences in prognostic factors between the ADA subgroups to more accurately estimate the treatment benefits. After adjustment, the TGI-OS model predicted comparable hazard ratios (HRs) of OS between ADA + and ADA - subgroups, suggesting that the development of ADA does not have a clinically significant impact on the treatment benefit of atezolizumab.
© 2022. The Author(s), under exclusive licence to American Association of Pharmaceutical Scientists.

Entities:  

Keywords:  anti-drug antibody; atezolizumab; nonlinear mixed effect modeling; tumor growth inhibition

Mesh:

Substances:

Year:  2022        PMID: 35484442     DOI: 10.1208/s12248-022-00710-4

Source DB:  PubMed          Journal:  AAPS J        ISSN: 1550-7416            Impact factor:   4.009


  13 in total

Review 1.  Progress and Opportunities to Advance Clinical Cancer Therapeutics Using Tumor Dynamic Models.

Authors:  René Bruno; Dean Bottino; Dinesh P de Alwis; Antonio T Fojo; Jérémie Guedj; Chao Liu; Kristin R Swanson; Jenny Zheng; Yanan Zheng; Jin Y Jin
Journal:  Clin Cancer Res       Date:  2019-12-23       Impact factor: 12.531

2.  A Model of Overall Survival Predicts Treatment Outcomes with Atezolizumab versus Chemotherapy in Non-Small Cell Lung Cancer Based on Early Tumor Kinetics.

Authors:  Laurent Claret; Jin Y Jin; Charles Ferté; Helen Winter; Sandhya Girish; Mark Stroh; Pei He; Marcus Ballinger; Alan Sandler; Amita Joshi; Achim Rittmeyer; David Gandara; Jean-Charles Soria; René Bruno
Journal:  Clin Cancer Res       Date:  2018-04-23       Impact factor: 12.531

3.  Time-dependent population PK models of single-agent atezolizumab in patients with cancer.

Authors:  Mathilde Marchand; Rong Zhang; Phyllis Chan; Valerie Quarmby; Marcus Ballinger; Nitzan Sternheim; Benjamin Wu; Jin Y Jin; René Bruno
Journal:  Cancer Chemother Pharmacol       Date:  2021-04-27       Impact factor: 3.333

4.  Tumor regression and growth rates determined in five intramural NCI prostate cancer trials: the growth rate constant as an indicator of therapeutic efficacy.

Authors:  Wilfred D Stein; James L Gulley; Jeff Schlom; Ravi A Madan; William Dahut; William D Figg; Yang-Min Ning; Phil M Arlen; Doug Price; Susan E Bates; Tito Fojo
Journal:  Clin Cancer Res       Date:  2010-11-24       Impact factor: 12.531

Review 5.  Tumor Growth Dynamic Modeling in Oncology Drug Development and Regulatory Approval: Past, Present, and Future Opportunities.

Authors:  Nidal Al-Huniti; Yan Feng; Jingyu Jerry Yu; Zheng Lu; Mario Nagase; Diansong Zhou; Jennifer Sheng
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2020-07-22

Review 6.  A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors.

Authors:  Anyue Yin; Dirk Jan A R Moes; Johan G C van Hasselt; Jesse J Swen; Henk-Jan Guchelaar
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2019-08-09

7.  Evaluation of atezolizumab immunogenicity: Clinical pharmacology (part 1).

Authors:  Benjamin Wu; Nitzan Sternheim; Priya Agarwal; Julia Suchomel; Shweta Vadhavkar; Rene Bruno; Marcus Ballinger; Coen A Bernaards; Phyllis Chan; Jane Ruppel; Jin Jin; Sandhya Girish; Amita Joshi; Valerie Quarmby
Journal:  Clin Transl Sci       Date:  2021-08-25       Impact factor: 4.689

8.  Atezolizumab for First-Line Treatment of Metastatic Nonsquamous NSCLC.

Authors:  Mark A Socinski; Robert M Jotte; Federico Cappuzzo; Francisco Orlandi; Daniil Stroyakovskiy; Naoyuki Nogami; Delvys Rodríguez-Abreu; Denis Moro-Sibilot; Christian A Thomas; Fabrice Barlesi; Gene Finley; Claudia Kelsch; Anthony Lee; Shelley Coleman; Yu Deng; Yijing Shen; Marcin Kowanetz; Ariel Lopez-Chavez; Alan Sandler; Martin Reck
Journal:  N Engl J Med       Date:  2018-06-04       Impact factor: 91.245

9.  Evaluation of atezolizumab immunogenicity: Efficacy and safety (Part 2).

Authors:  Solange Peters; Peter R Galle; Coen A Bernaards; Marcus Ballinger; René Bruno; Valerie Quarmby; Jane Ruppel; Alexandr Vilimovskij; Benjamin Wu; Nitzan Sternheim; Martin Reck
Journal:  Clin Transl Sci       Date:  2021-09-28       Impact factor: 4.689

10.  Prediction of overall survival in patients across solid tumors following atezolizumab treatments: A tumor growth inhibition-overall survival modeling framework.

Authors:  Phyllis Chan; Mathilde Marchand; Kenta Yoshida; Shweta Vadhavkar; Nina Wang; Alyse Lin; Benjamin Wu; Marcus Ballinger; Nitzan Sternheim; Jin Y Jin; René Bruno
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2021-08-04
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