Modernizing the assessment and reporting of adverse events in oncology clinical trials using complementary statistical approaches: a case study of the MOTIVATE trial.

IF 3 3区 医学 Q2 ONCOLOGY Investigational New Drugs Pub Date : 2024-11-04 DOI:10.1007/s10637-024-01481-9
Mathilde Morisseau, Carlos Gomez-Roca, Marie Viala, Audrey Rabeau, Delphine Loirat, Nadia Munsch, Kristell Thomas, Cécile Pages, Iphigenie Korakis, Vincent Sibaud, Jean-Pierre Delord, Thomas Filleron, Bastien Cabarrou
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Abstract

The reporting of adverse events (AEs) is fundamental to characterize safety profiles of novel therapeutic drug classes, however, conventional analysis strategies are suboptimal tools for this task. We therefore attempted to contribute to the modernization of AE analysis by encompassing the dimension of time, the duration and the recurrent nature of AEs induced by these extended treatment durations. This paper presents and highlights the benefits of alternative approaches to modernize AE analysis based on the MOTIVATE prospective study modeling immune-related AEs (irAEs) in patients with solid tumors (regardless of the primary site) treated with immune checkpoint inhibitor irrespective of disease stage. The probability of presenting an irAE over time was estimated using the prevalence function. The time-to-onset (TTO) and the mean number of recurrent irAEs were also assessed. Among the 147 patients analyzed, 39.7% had a melanoma, 37.7% a non-small cell lung cancer (NSCLC) and 74.8% were treated for metastatic disease. Despite a higher proportion of melanoma patients presenting at least one irAE, the prevalence of irAEs was lower in melanoma than in NSCLC patients over time. TTO analysis showed that irAEs occurred earlier in NSCLC patients whereas melanoma patients experienced more recurrent irAEs over the long-term. The prevalence function of non-metastatic and metastatic patients revealed different long-term toxicity profiles. These alternative methodologies capture different toxicity patterns (time-to-onset, recurrent, acute episodic or long-term moderate AEs) and provide a more consistent safety assessment for new therapeutics, thereby assisting clinicians and health authorities in their therapeutic decision-making processes.

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利用互补统计方法实现肿瘤临床试验中不良事件评估和报告的现代化:MOTIVATE 试验案例研究。
不良事件(AEs)报告是描述新型治疗药物安全性特征的基础,然而,传统的分析策略并不是完成这项任务的最佳工具。因此,我们尝试将时间维度、疗程和这些延长的疗程所诱发的 AE 的复发性纳入 AE 分析,从而为 AE 分析的现代化做出贡献。本文以 MOTIVATE 前瞻性研究为基础,对接受免疫检查点抑制剂治疗的实体瘤患者(无论原发部位)(无论疾病分期)的免疫相关 AEs(irAEs)进行建模,介绍并强调了现代化 AE 分析替代方法的益处。使用流行率函数估算了随着时间推移出现irAE的概率。此外,还评估了发病时间(TTO)和复发虹膜不良反应的平均次数。在分析的147名患者中,39.7%患有黑色素瘤,37.7%患有非小细胞肺癌(NSCLC),74.8%接受了转移性疾病治疗。尽管黑色素瘤患者中出现至少一种虹膜AE的比例较高,但随着时间的推移,黑色素瘤患者的虹膜AE发生率低于非小细胞肺癌患者。TTO分析表明,NSCLC患者的虹膜AE发生较早,而黑色素瘤患者的虹膜AE长期复发率较高。非转移性和转移性患者的患病率函数显示了不同的长期毒性特征。这些替代方法捕捉到了不同的毒性模式(发病时间、复发性、急性发作性或长期中度 AEs),为新疗法提供了更一致的安全性评估,从而帮助临床医生和卫生机构做出治疗决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.60
自引率
0.00%
发文量
121
审稿时长
1 months
期刊介绍: The development of new anticancer agents is one of the most rapidly changing aspects of cancer research. Investigational New Drugs provides a forum for the rapid dissemination of information on new anticancer agents. The papers published are of interest to the medical chemist, toxicologist, pharmacist, pharmacologist, biostatistician and clinical oncologist. Investigational New Drugs provides the fastest possible publication of new discoveries and results for the whole community of scientists developing anticancer agents.
期刊最新文献
Phase II trial of multi-tyrosine kinase inhibitor ESK981 in combination with PD-1 inhibitor nivolumab in patients with metastatic castration-resistant prostate cancer. Modernizing the assessment and reporting of adverse events in oncology clinical trials using complementary statistical approaches: a case study of the MOTIVATE trial. A phase II study of ME2136 (Asenapine Maleate) plus standard antiemetic therapy for patients, including diabetic patients, receiving cisplatin-based chemotherapy. Anti-ovarian cancer migration and toxicity characteristics of a platinum(IV) pro-drug with axial HDAC inhibitor ligands in zebrafish models. Unraveling the potential biomarkers of immune checkpoint inhibitors in advanced ovarian cancer: a comprehensive review.
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