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联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。由于药物效应是剂量依赖的，因此需要检查一种药物的多剂量，这导致组合数量迅速增加，并且是一个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，导致许多治疗机会的错失。我们的初步研究已经确定了确定两种抗癌药物相对效力的分析公式，这与该领域恒定相对效力的常见假设相矛盾。此外，我们还开发了一种用于组合研究设计和分析的最大功率方法，以便最大限度地提高检测偏离加性的统计功率，可以在中等样本量下估计剂量反应。目前的多药联合研究多采用次优设计，如双药对评价。我们提出了一种新的两阶段程序，首先利用基于单一药物实验数据的计算机模型，结合可用的网络或途径信息进行初始选择，然后对选定的多药物组合进行有效的实验设计，并对数据进行统计分析。整合现代统计方法、数学、药理学和计算机，我们提出(1)利用单药剂量反应数据和信号通路/网络信息，开发药物及其相互作用的最佳选择的统计模型和算法；(2)开发实验设计和统计分析，利用Aim 1中的计算机结果来表征剂量反应；(3)为多种药物组合的相互作用（协同）分析开发统计方法；(4)在NCI和其他资助机构已经资助的研究中，在癌细胞系中测试这些方法；(5)丰富目标1-3中开发的计算机程序。在项目完成后，预计该方法将能够服务于更大的翻译研究界，它也将对处理统计研究产生影响