Bayesian Methods for Complex Precision Biotherapy Trials in Oncology

肿瘤学中复杂精密生物治疗试验的贝叶斯方法

• 批准号: 10693233
• 负责人: Ruitao Lin
• 金额: $ 36.32万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10693233
• 关键词: Acceleration; Accounting; Acute Graft Versus Host Disease; Address; Allogenic; Area; B-Lymphocytes; Bayesian Method; Bayesian Modeling; Biological; Biological Markers; Biological Response Modifier Therapy; Biometry; Biotechnology; CD19 gene; CD22 gene; Cations; Cell Therapy; Cell surface; Cells; Characteristics; Clinic; Clinical; Clinical Trials; Clinical Trials Design; Complex; Computer software; Cytometry; Cytotoxic Chemotherapy; Decision Making; Development; Diagnosis; Diagnostic; Dimensions; Disease; Disease Progression; Dose; Drops; Equilibrium; Evaluation; Futility; Future; Genes; Genomics; Goals; Growth Factor; Guidelines; Hematologic Neoplasms; Hematological Disease; Heterogeneity; Hybrids; Immune response; Immunotherapeutic agent; Immunotherapy; Malignant Neoplasms; Mass Spectrum Analysis; Mean Survival Times; Medical; Mesenchymal Stem Cells; Methods; Modeling; Modernization; Molecular Target; Monitor; Natural Killer Cells; Non-Small-Cell Lung Carcinoma; Oncology; Operative Surgical Procedures; Outcome; Partial Remission; Patients; Phase; Physicians; Precision therapeutics; Probability; Proteomics; Protocols documentation; Radiation; Randomized; Refractory; Research; Resources; Risk; Running; Safety; Salvage Therapy; Sample Size; Savings; Schedule; Stable Disease; Statistical Models; Stem cell transplant; Steroid therapy; Structure; Subgroup; Testing; Therapeutic; Therapeutic Uses; Time; Toxic effect; Transplant Recipients; adverse outcome; arm; cancer immunotherapy; chemotherapy; chimeric antigen receptor; chimeric antigen receptor T cells; clinical practice; computer program; conventional therapy; cytokine release syndrome; design; disease prognostic; disorder subtype; engineered NK cell; graphical user interface; immunotherapy trials; improved; individual patient; novel; patient safety; patient subsets; precision medicine; primary outcome; prognostic; programmed cell death ligand 1; programs; research clinical testing; response; screening; simulation; social; software development; targeted agent; tool; trial comparing; trial design; tumor; user friendly software; user-friendly; vector; web site

Project Summary/Abstract Most clinical trial designs use \one-size- ts-all" rules for treatment assignment and evaluation based on models that ignore patient heterogeneity. This is disconnected from medical practice, where physicians use each patient's diagnosis and prognostic variables to make personalized, precision medicine treatment decisions. Modern precision medicine exploits biotechnologies such as proteomics, genomics, gene sequencing, mass spectrometry, or cytometry methods that evaluate multiple cell surface markers. These generate vectors of biomarkers that may be used to re ne existing disease subgroup de nitions, construct new disease classi cations, and formulate clinical trial designs and statistical rules for personalized/precision treatment assignment. In oncology and other disease areas, there is rapidly increasing development of new biotherapies, including cell therapies, immunotherapies, and targeted molecular agents. A biotherapy may be administered once or in multiple cycles; used in combination with conventional treatments such as cytotoxic chemotherapy, radiation, or surgery; and often generates complex outcomes, such as repeatedly evaluated tumor status, multiple biological variables, and occurrence times of both early and late onset toxicities. This complicates the de nitions of \response" and \toxicity," and produces multidimensional treatment e ects that may di er between subgroups. An example is a phase I-II trial to optimize subgroup-speci c doses of donor derived natural killer (NK) cells for treating B-cell hematologic malignancies, where donated NK cells are engineered using chimeric antigen receptors to enhance their cancer killing e ects, then expanded using growth factors to obtain cell doses large enough for therapeutic use. Subgroups may be de ned using disease subtypes and prognostic variables. Co-primary outcomes may include ordinal disease status, including complete or partial remission, stable disease, or disease progression, evaluated either once or at monthly intervals; time to severe NK cell-related toxicity, such as cytokine release syndrome; and a binary indicator of 100-day survival. Considering (biotherapy, dose, administration schedule) a treatment regime, a clinical trial of one or more new biotherapies may include a subgroup-speci c risk-bene t tradeo based dose or schedule optimization for each biotherapy, randomization among regimes restricted to achieve balance within subgroups, and subgroup-speci c group sequential rules to select superior regimes or drop unsafe or ine ective regimes. The proposed research will construct robust Bayesian regression models for regime-outcome e ects that account for patient heterogeneity, including possible regime-subgroup interactions. These will be the basis for sequential decision making and regime assignment, and they may include latent variables to adaptively combine subgroups with similar regime-outcome e ects. Each clinical trial design will be tailored to address a combination of these goals in speci c biotherapy settings. For each design, user-friendly computer software will be provided, including programs for trial simulation to establish design operating characteristics, trial conduct, and use by practicing physicians to choose optimal regimes for their patients. The overarching goal of the proposed research is to develop and identify optimal personalized biotherapy regimes, spanning a variety of di erent diseases and clinical settings, for greater anti-disease e ects, increased safety, and improved survival.

项目总结/摘要 大多数临床试验设计使用“一刀切”规则进行治疗分配和评估， 忽略患者异质性。这与医疗实践脱节，医生使用每个病人的诊断 和预后变量来做出个性化的、精确的医学治疗决策。现代精准医学 利用生物技术，如蛋白质组学、基因组学、基因测序、质谱或细胞计数方法 评估多种细胞表面标记物。这些产生的生物标志物的载体，可用于重新存在的 疾病亚组定义，构建新的疾病分类，并制定临床试验设计和统计 个性化/精确治疗分配的规则。在肿瘤学和其他疾病领域， 开发新的生物疗法，包括细胞疗法、免疫疗法和靶向分子制剂。一种生物疗法 可以一次或多次给药;与常规治疗如细胞毒性 化疗、放疗或手术;并且经常产生复杂的结果，例如反复评估肿瘤状态， 多个生物学变量以及早发性和迟发性毒性的发生时间。这使定义复杂化 的“反应”和“毒性”，并产生多维的治疗效果，可能不同的亚组。一个 一个例子是I-II期试验，以优化亚组特异性剂量的供体来源的自然杀伤（NK）细胞，用于治疗 B细胞恶性血液病，其中捐赠的NK细胞使用嵌合抗原受体进行工程改造，以增强 然后使用生长因子扩增它们的抗癌效应，以获得足够大的治疗用细胞剂量。 亚组可以使用疾病亚型和预后变量来定义。共同主要结局可能包括顺序 疾病状态，包括完全或部分缓解、疾病稳定或疾病进展，评价一次或 每月一次;至严重NK细胞相关毒性的时间，如细胞因子释放综合征;以及 100-日生存。考虑（生物疗法、剂量、给药时间表）治疗方案、一种或多种药物的临床试验， 更多新的生物疗法可能包括针对每个亚组的基于剂量或时间表优化的特定风险-效益权衡。 生物治疗，方案间的随机化限制，以实现亚组内的平衡，以及亚组特异性组 顺序规则来选择上级机制或丢弃不安全或无效的机制。该研究将构建 用于解释患者异质性的方案-结局效应的稳健贝叶斯回归模型，包括可能的 机制-子群相互作用这些将是顺序决策和政权分配的基础， 它们可以包括潜变量以自适应地联合收割机组合具有相似的状态结果效果的子组。每个临床 试验设计将针对特定生物治疗环境中这些目标的组合进行调整。对于每种设计， 将提供用户友好的计算机软件，包括试验模拟程序，以建立设计操作 特征，试验进行，并由执业医师使用，以选择最佳方案为他们的病人。的 拟议研究的总体目标是开发和确定最佳的个性化生物治疗方案， 各种不同的疾病和临床环境，更大的抗病效果，增加安全性，提高生存率。

项目成果

BOB: Bayesian optimal design for biosimilar trials with co-primary endpoints.

• DOI: 10.1002/sim.9571
• 发表时间: 2022-11-20
• 期刊: STATISTICS IN MEDICINE
• 影响因子: 2
• 作者: Chi, Xiaohan;Yu, Zhangsheng;Lin, Ruitao
• 通讯作者: Lin, Ruitao

Accuracy and Safety of Novel Designs for Phase I Drug-Combination Oncology Trials.

• DOI: 10.1080/19466315.2022.2081602
• 发表时间: 2022
• 期刊: STATISTICS IN BIOPHARMACEUTICAL RESEARCH
• 影响因子: 1.8
• 作者: Liu, Rong;Yuan, Ying;Sen, Suman;Yang, Xin;Jiang, Qi;Li, Xiaoyun (Nicole);Lu, Chengxing (Cindy);Gonen, Mithat;Tian, Hong;Zhou, Heng;Lin, Ruitao;Marchenko, Olga
• 通讯作者: Marchenko, Olga

Bayesian adaptive model selection design for optimal biological dose finding in phase I/II clinical trials.

贝叶斯自适应模型选择设计，用于 I/II 期临床试验中最佳生物剂量的发现。

• DOI: 10.1093/biostatistics/kxab028
• 发表时间: 2023
• 期刊: Biostatistics (Oxford, England)
• 作者: Lin,Ruitao;Yin,Guosheng;Shi,Haolun
• 通讯作者: Shi,Haolun