Statistical Methods for Multi-Drug Combinations

多药组合的统计方法

• 批准号: 8392050
• 负责人: MING Tony TAN
• 金额: $ 5.67万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-09 至 2012-10-11
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8392050
• 关键词: Acquired Immunodeficiency Syndrome; Algorithms; Antineoplastic Agents; Apoptosis; Ara-C; Area; Biological; Cancer cell line; Clinic; Clinical Protocols; Communities; Complex; Computer Simulation; Data; Development; Disease; Dose; Drug Combinations; Environment; Enzyme Kinetics; Evaluation; Experimental Designs; Funding; Funding Agency; Goals; HIV; Hypertension; Individual; Information Networks; Intervention; Investigational Drugs; Knowledge; Laboratories; Malignant Neoplasms; Mathematics; Measures; Methodology; Methods; Modeling; Network-based; Organism; Pathway interactions; Pharmaceutical Preparations; Pharmacology; Pharmacotherapy; Physiology; Procedures; Relative (related person); Research; Research Design; Resort; Sample Size; Signal Pathway; Staging; Statistical Data Interpretation; Statistical Methods; Statistical Models; Testing; Therapeutic; Translating; Translational Research; Variant; Vorinostat; base; cancer Biomedical Informatics Grid; cancer therapy; computer program; design; graphical user interface; in vivo; innovation; novel; pre-clinical; programs; research study; response; success

DESCRIPTION (provided by applicant): The goal of this proposal is to develop statistical methods and algorithms for the design and analysis of multi-drug combination studies. Drug combinations are the hallmark of therapies for complex diseases such as cancer, HIV and hypertension. Because drug-effect is dose-dependent, multiple doses of an individual drug need to be examined, yielding rapidly rising number of combinations and a challenging high dimensional statistical problem. The lack of proper design and analysis methods for multi-drug combination studies have resulted in many missed therapeutic opportunities. Our preliminary studies have identified an analytic formula for determining the relative potency of two anticancer drugs, which contradicts the common assumption of constant relative potency in the field. Furthermore, we have developed a maximal power approach for the design and analysis of combination studies so that the statistical power to detect departures from additively is maximized, the dose-response can be estimated with moderate sample size. Currently multi-drug combination studies have to resort to suboptimal design such as pair wise evaluation of two-drugs. We propose a novel two-stage procedure starting with an initial selection by utilizing an in silico model built upon experimental data of single drugs in conjunction with available network or pathway information and followed with efficient experimental designs on selected multi-drug combinations and statistical analysis of the data. Integrating modern statistical methods, mathematics, pharmacology and computing, we propose to (1) develop the statistical models and algorithms for the optimal selection of drugs and their interactions utilizing single-drug dose response data and signaling pathway/network information; (2) develop experimental designs and statistical analysis for characterizing dose-responses using the in silico results in Aim 1; (3) develop statistical methods for the interaction (synergy) analysis of multi-drug combinations, (4) test the methods in cancer cell lines in studies already funded by NCI, and other funding agencies; and (5) enrich computer programs developed in Aims 1-3. Upon completion of the project, it is anticipated that the method will be able to serve a much larger translational research community and it will also have bearing to statistical research dealing with high dimensional data. PUBLIC HEALTH RELEVANCE: Pre-clinical laboratory testing is an important step to translate laboratory discoveries to clinic. The goal of this proposal is to develop statistical methods to optimally design and efficiently analyze preclinical drug combination studies in cancer and other complex diseases such as AIDS and hypertension. Drug combinations are central to cancer therapy. With increased complexity in drug therapy, the optimized design and interaction analysis yield a methodology for identifying most synergistic combinations and provide a basis to further study the combination in vivo.

描述（由申请人提供）：本提案的目标是开发用于设计和分析多药物组合研究的统计方法和算法。药物组合是治疗癌症、艾滋病毒和高血压等复杂疾病的标志。由于药物作用是剂量依赖性的，因此需要检查单个药物的多个剂量，从而产生快速增加的组合数量和具有挑战性的高维统计问题。多药联合研究缺乏适当的设计和分析方法导致错失许多治疗机会。我们的初步研究已经确定了用于确定两种抗癌药物的相对效力的分析公式，这与该领域相对效力恒定的常见假设相矛盾。此外，我们开发了一种最大功效方法来设计和分析组合研究，以便最大化检测相加偏差的统计功效，可以用适度的样本量来估计剂量反应。目前的多药物组合研究必须采用次优设计，例如两种药物的成对评估。我们提出了一种新颖的两阶段程序，首先利用基于单一药物实验数据并结合可用网络或途径信息构建的计算机模型进行初步选择，然后对选定的多药物组合进行有效的实验设计以及统计分析数据。结合现代统计方法、数学、药理学和计算，我们建议（1）开发统计模型和算法，利用单药剂量反应数据和信号通路/网络信息来优化药物选择及其相互作用； (2) 使用目标 1 中的计算机结果开发实验设计和统计分析来表征剂量反应； (3) 开发多种药物组合的相互作用（协同）分析的统计方法，(4) 在 NCI 和其他资助机构资助的研究中测试癌细胞系中的方法； (5) 丰富目标 1-3 中开发的计算机程序。该项目完成后，预计该方法将能够为更大的转化研究社区服务，并且还将对处理以下问题的统计研究产生影响： 高维数据。 公共卫生相关性：临床前实验室检测是将实验室发现转化为临床的重要一步。该提案的目标是开发统计方法，以优化设计和有效分析癌症和其他复杂疾病（例如艾滋病和高血压）的临床前药物组合研究。药物组合是癌症治疗的核心。随着药物治疗复杂性的增加，优化的设计和相互作用分析产生了一种识别大多数协同组合的方法，并为进一步研究体内组合提供了基础。