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.
anti-drug antibody
atezolizumab
nonlinear mixed effect modeling
tumor growth inhibition
Journal
The AAPS journal
ISSN: 1550-7416
Titre abrégé: AAPS J
Pays: United States
ID NLM: 101223209
Informations de publication
Date de publication:
28 04 2022
28 04 2022
Historique:
received:
02
03
2022
accepted:
13
04
2022
entrez:
28
4
2022
pubmed:
29
4
2022
medline:
3
5
2022
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 35484442
doi: 10.1208/s12248-022-00710-4
pii: 10.1208/s12248-022-00710-4
doi:
Substances chimiques
Antibodies, Monoclonal, Humanized
0
atezolizumab
52CMI0WC3Y
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
58Informations de copyright
© 2022. The Author(s), under exclusive licence to American Association of Pharmaceutical Scientists.
Références
Al-Huniti N, Feng Y, Yu JJ, Lu Z, Nagase M, Zhou D, et al. Tumor growth dynamic modeling in oncology drug development and regulatory approval: past, present, and future opportunities. CPT Pharmacometrics Syst Pharmacol. 2020;9(8):419–27. https://doi.org/10.1002/psp4.12542 .
doi: 10.1002/psp4.12542
pubmed: 32589767
pmcid: 7438808
Bruno R, Bottino D, de Alwis DP, Fojo AT, Guedj J, Liu C, et al. Progress and opportunities to advance clinical cancer therapeutics using tumor dynamic models. Clin Cancer Res. 2020;26(8):1787–95. https://doi.org/10.1158/1078-0432.CCR-19-0287 .
doi: 10.1158/1078-0432.CCR-19-0287
pubmed: 31871299
Yin A, Moes D, van Hasselt JGC, Swen JJ, Guchelaar HJ. A review of mathematical models for tumor dynamics and treatment resistance evolution of solid tumors. CPT Pharmacometrics Syst Pharmacol. 2019;8(10):720–37. https://doi.org/10.1002/psp4.12450 .
doi: 10.1002/psp4.12450
pubmed: 31250989
pmcid: 6813171
Stein WD, Gulley JL, Schlom J, Madan RA, Dahut W, Figg WD, et al. Tumor regression and growth rates determined in five intramural NCI prostate cancer trials: the growth rate constant as an indicator of therapeutic efficacy. Clin Cancer Res. 2011;17(4):907–17. https://doi.org/10.1158/1078-0432.CCR-10-1762 .
doi: 10.1158/1078-0432.CCR-10-1762
pubmed: 21106727
Claret L, Jin JY, Ferte C, Winter H, Girish S, Stroh M, et al. A model of overall survival predicts treatment outcomes with atezolizumab versus chemotherapy in non-small cell lung cancer based on early tumor kinetics. Clin Cancer Res. 2018;24(14):3292–8. https://doi.org/10.1158/1078-0432.CCR-17-3662 .
doi: 10.1158/1078-0432.CCR-17-3662
pubmed: 29685883
Chan P, Marchand M, Yoshida K, Vadhavkar S, Wang N, Lin A, et al. Prediction of overall survival in patients across solid tumors following atezolizumab treatments: a tumor growth inhibition-overall survival modeling framework. CPT Pharmacometrics Syst Pharmacol. 2021;10(10):1171–82. https://doi.org/10.1002/psp4.12686 .
doi: 10.1002/psp4.12686
pubmed: 34270868
pmcid: 8520743
TECENTRIQ (atezolizumab) [prescribing information]. https://www.gene.com/download/pdf/tecentriq_prescribing.pdf . Genentech, Inc; 2021. Accessed 2 Mar 2022.
Marchand M, Zhang R, Chan P, Quarmby V, Ballinger M, Sternheim N, et al. Time-dependent population PK models of single-agent atezolizumab in patients with cancer. Cancer Chemother Pharmacol. 2021;88(2):211–21. https://doi.org/10.1007/s00280-021-04276-4 .
doi: 10.1007/s00280-021-04276-4
pubmed: 33904970
Wu B, Sternheim N, Agarwal P, Suchomel J, Vadhavkar S, Bruno R, et al. Evaluation of atezolizumab immunogenicity: clinical pharmacology (part 1). Clin Transl Sci. 2022;15(1):130–40. https://doi.org/10.1111/cts.13127 .
doi: 10.1111/cts.13127
pubmed: 34432389
Peters S, Galle PR, Bernaards CA, Ballinger M, Bruno R, Quarmby V, et al. Evaluation of atezolizumab immunogenicity: efficacy and safety (part 2). Clin Transl Sci. 2022;15(1):141–57. https://doi.org/10.1111/cts.13149 .
doi: 10.1111/cts.13149
pubmed: 34582105
Shengchun Kong DH, Lauer S, Tian L. Weighted approach for estimating effects in principal strata with missing data for a categorical post-baseline variable in randomized controlled trials. Stat Biopharm Res. 2022. https://doi.org/10.1080/19466315.2021.2009020 .
doi: 10.1080/19466315.2021.2009020
Socinski MA, Jotte RM, Cappuzzo F, Orlandi F, Stroyakovskiy D, Nogami N, et al. Atezolizumab for first-line treatment of metastatic nonsquamous NSCLC. N Engl J Med. 2018;378(24):2288–301. https://doi.org/10.1056/NEJMoa1716948 .
doi: 10.1056/NEJMoa1716948
pubmed: 29863955
Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45(2):228–47. https://doi.org/10.1016/j.ejca.2008.10.026 .
doi: 10.1016/j.ejca.2008.10.026
pubmed: 19097774
Kawakatsu S, Bruno R, Kagedal M, Li C, Girish S, Joshi A, et al. Confounding factors in exposure-response analyses and mitigation strategies for monoclonal antibodies in oncology. Br J Clin Pharmacol. 2021;87(6):2493–501. https://doi.org/10.1111/bcp.14662 .
doi: 10.1111/bcp.14662
pubmed: 33217012
Morrissey KM, Marchand M, Patel H, Zhang R, Wu B, Phyllis Chan H, et al. Alternative dosing regimens for atezolizumab: an example of model-informed drug development in the postmarketing setting. Cancer Chemother Pharmacol. 2019;84(6):1257–67. https://doi.org/10.1007/s00280-019-03954-8 .
doi: 10.1007/s00280-019-03954-8
pubmed: 31542806
pmcid: 6820606