目前肿瘤学早期阶段剂量发现设计与毒性和疗效的统计运行特征。

IF 1.2 4区 医学 Q4 PHARMACOLOGY & PHARMACY Journal of Biopharmaceutical Statistics Pub Date : 2024-11-16 DOI:10.1080/10543406.2024.2424845
Hao Sun, Hsin-Yu Lin, Jieqi Tu, Revathi Ananthakrishnan, Eunhee Kim
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引用次数: 0

摘要

传统的癌症 I 期剂量发现临床试验设计旨在根据单一的毒性结果来确定研究性细胞毒性药物的最大耐受剂量 (MTD),并假设存在单调的剂量-反应关系。然而,对于新出现的疗法,如免疫肿瘤疗法和分子靶向疗法,这一假设可能并不总是成立,因此基于毒性的传统剂量发现试验设计不再适用。为解决这一问题,人们开发了许多早期剂量寻找临床试验设计,以确定最佳生物剂量(OBD),并同时考虑毒性和疗效结果。在本文中,我们回顾了目前的模型辅助剂量寻找设计:BOIN-ET、BOIN12、UBI、TEPI-2、PRINTE、STEIN 和 uTPI,以确定 OBD 并比较它们的运行特征。我们进行了广泛的模拟研究,并利用 CAR T 细胞疗法 I 期试验进行了案例研究,以比较上述设计在不同可能的剂量-反应关系情况下的性能。模拟结果表明,不同设计的性能取决于特定的剂量-反应关系和考虑的具体指标。根据我们的模拟结果和实际考虑,STEIN、PRINTE 和 BOIN12 从不同角度来看都优于其他设计。
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Statistical operating characteristics of current early phase dose finding designs with toxicity and efficacy in oncology.

Traditional phase I dose finding cancer clinical trial designs aim to determine the maximum tolerated dose (MTD) of the investigational cytotoxic agent based on a single toxicity outcome, assuming a monotone dose-response relationship. However, this assumption might not always hold for newly emerging therapies such as immuno-oncology therapies and molecularly targeted therapies, making conventional dose finding trial designs based on toxicity no longer appropriate. To tackle this issue, numerous early-phase dose finding clinical trial designs have been developed to identify the optimal biological dose (OBD), which takes both toxicity and efficacy outcomes into account. In this article, we review the current model-assisted dose finding designs, BOIN-ET, BOIN12, UBI, TEPI-2, PRINTE, STEIN, and uTPI to identify the OBD and compare their operating characteristics. Extensive simulation studies and a case study using a CAR T-cell therapy phase I trial have been conducted to compare the performance of the aforementioned designs under different possible dose-response relationship scenarios. The simulation results demonstrate that the performance of different designs varies depending on the particular dose-response relationship and the specific metric considered. Based on our simulation results and practical considerations, STEIN, PRINTE, and BOIN12 outperform the other designs from different perspectives.

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来源期刊
Journal of Biopharmaceutical Statistics
Journal of Biopharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.50
自引率
18.20%
发文量
71
审稿时长
6-12 weeks
期刊介绍: The Journal of Biopharmaceutical Statistics, a rapid publication journal, discusses quality applications of statistics in biopharmaceutical research and development. Now publishing six times per year, it includes expositions of statistical methodology with immediate applicability to biopharmaceutical research in the form of full-length and short manuscripts, review articles, selected/invited conference papers, short articles, and letters to the editor. Addressing timely and provocative topics important to the biostatistical profession, the journal covers: Drug, device, and biological research and development; Drug screening and drug design; Assessment of pharmacological activity; Pharmaceutical formulation and scale-up; Preclinical safety assessment; Bioavailability, bioequivalence, and pharmacokinetics; Phase, I, II, and III clinical development including complex innovative designs; Premarket approval assessment of clinical safety; Postmarketing surveillance; Big data and artificial intelligence and applications.
期刊最新文献
Latent class analysis of post-acute sequelae of SARS-CoV-2 infection. Machine learning approach for detection of MACE events within clinical trial data. Statistical operating characteristics of current early phase dose finding designs with toxicity and efficacy in oncology. Defective regression models for cure rate data with competing risks. An investigation to improve a nonlinear mixed-effects approach for EC50 estimation based on multi-donor dose-response data.
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