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基于随机化后因素的亚组分析的肿瘤生长抑制-总生存(TGI-OS)模型:在IMpower150研究中用于阿替利珠单抗抗药物抗体(ADA)亚组分析的应用

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.

作者信息

Yoshida Kenta, Chan Phyllis, Marchand Mathilde, Zhang Rong, Wu Benjamin, Ballinger Marcus, Sternheim Nitzan, Jin Jin Y, Bruno René

机构信息

Department of Clinical Pharmacology, Genentech, Inc., 1 DNA Way, South San Francisco, CA, 94080, USA.

Certara Strategic Consulting, Certara, Paris, France.

出版信息

AAPS J. 2022 Apr 28;24(3):58. doi: 10.1208/s12248-022-00710-4.

DOI:10.1208/s12248-022-00710-4
PMID:35484442
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.

摘要

肿瘤大小或肿瘤相关生物标志物的纵向变化作为治疗获益的早期标志物越来越受到关注。肿瘤生长抑制-总生存(TGI-OS)模型是一种数学框架,用于建立从肿瘤大小轨迹到生存结果的联系,目的是利用少量随访时间短的受试者的肿瘤数据预测生存获益。在本研究中,我们应用TGI-OS模型评估IMpower150研究中出现抗药抗体(ADA)的患者的治疗获益。由于ADA阳性(ADA +)和阴性(ADA -)患者之间基线预后因素可能存在不平衡,将活性组的亚组(ADA +组和ADA -组)与整个对照组进行直接比较并不合适。因此,采用TGI-OS建模框架来调整ADA亚组之间预后因素的差异,以更准确地估计治疗获益。调整后,TGI-OS模型预测ADA +和ADA -亚组之间的总生存风险比(HR)相当,这表明ADA的出现对阿特珠单抗的治疗获益没有临床显著影响。

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本文引用的文献

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Clin Transl Sci. 2022 Jan;15(1):141-157. doi: 10.1111/cts.13149. Epub 2021 Sep 28.
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Evaluation of atezolizumab immunogenicity: Clinical pharmacology (part 1).评估阿替利珠单抗的免疫原性:临床药理学(第一部分)。
Clin Transl Sci. 2022 Jan;15(1):130-140. doi: 10.1111/cts.13127. Epub 2021 Aug 25.
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Prediction of overall survival in patients across solid tumors following atezolizumab treatments: A tumor growth inhibition-overall survival modeling framework.
肿瘤生长和总生存建模以支持 Ib/II 期试验中的决策:联合和两阶段方法的比较。
CPT Pharmacometrics Syst Pharmacol. 2024 Jun;13(6):1017-1028. doi: 10.1002/psp4.13137. Epub 2024 Apr 17.
4
Tumor growth inhibition-overall survival modeling in non-small cell lung cancer: A case study from GEMSTONE-302.非小细胞肺癌肿瘤生长抑制-总生存建模:来自 GEMSTONE-302 的案例研究。
CPT Pharmacometrics Syst Pharmacol. 2024 Mar;13(3):437-448. doi: 10.1002/psp4.13094. Epub 2023 Dec 21.
预测接受阿特珠单抗治疗的各类实体瘤患者的总生存期:一种肿瘤生长抑制-总生存期建模框架。
CPT Pharmacometrics Syst Pharmacol. 2021 Oct;10(10):1171-1182. doi: 10.1002/psp4.12686. Epub 2021 Aug 4.
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Time-dependent population PK models of single-agent atezolizumab in patients with cancer.癌症患者中单药阿特珠单抗的时变群体 PK 模型。
Cancer Chemother Pharmacol. 2021 Aug;88(2):211-221. doi: 10.1007/s00280-021-04276-4. Epub 2021 Apr 27.
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Confounding factors in exposure-response analyses and mitigation strategies for monoclonal antibodies in oncology.肿瘤学中单克隆抗体的暴露-反应分析中的混杂因素及缓解策略。
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Tumor Growth Dynamic Modeling in Oncology Drug Development and Regulatory Approval: Past, Present, and Future Opportunities.肿瘤生长动态建模在肿瘤药物开发和监管审批中的应用:过去、现在和未来的机遇。
CPT Pharmacometrics Syst Pharmacol. 2020 Aug;9(8):419-427. doi: 10.1002/psp4.12542. Epub 2020 Jul 22.
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Progress and Opportunities to Advance Clinical Cancer Therapeutics Using Tumor Dynamic Models.利用肿瘤动态模型推进癌症临床治疗的进展和机遇。
Clin Cancer Res. 2020 Apr 15;26(8):1787-1795. doi: 10.1158/1078-0432.CCR-19-0287. Epub 2019 Dec 23.
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