权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
10271754
负责人：
Ruitao Lin
金额：
$ 37.06万
依托单位：
UNIVERSITY OF TX MD ANDERSON CAN CTR
依托单位国家：
美国
项目类别：
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/10271754
关键词：
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 Chemotherapy and/or radiation Clinic Clinical Clinical Trials Clinical Trials Design Complex Computer software Cytometry Cytotoxic Chemotherapy Decision Making Development Diagnosis Diagnostic 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 Phase I/II Trial Physicians Precision therapeutics Probability Proteomics Protocols documentation Randomized Refractory Research Resources Risk Running Safety Salvage Therapy Sample Size Savings Schedule Software Design Stable Disease Statistical Models Stem cell transplant Steroid therapy Structure Subgroup Testing Therapeutic Therapeutic Uses Time Toxic effect Transplant Recipients adverse outcome arm base cancer immunotherapy chemotherapy chimeric antigen receptor chimeric antigen receptor T cells clinical practice computer program conventional therapy cytokine release syndrome design disorder subtype engineered NK cell graphical user interface immunotherapy trials improved individual patient model design 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

项目摘要

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/文摘