Statistical Methods for Multi-Drug Combinations

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联合研究的统计方法和算法。药物组合是癌症、艾滋病毒和高血压等复杂疾病治疗的标志。因为药物效应是剂量依赖的，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，导致许多治疗机会错失。我们的初步研究已经确定了一个确定两种抗癌药物相对效力的解析公式，这与该领域中普遍认为的相对效力恒定的假设相矛盾。此外，我们开发了一种最大功率方法来设计和分析组合研究，以便最大化检测相加偏离的统计功率，可以在适度的样本量下估计剂量-反应。目前，多药联合研究不得不采用次优设计，如两药的配对评价。我们提出了一种新的两阶段程序，首先利用建立在单一药物实验数据和现有网络或途径信息的计算机模型进行初始选择，然后对选定的多药组合进行有效的实验设计和数据统计分析。结合现代统计学方法、数学、药理学和计算机，我们建议(1)利用单一药物剂量反应数据和信号通路/网络信息开发用于最优选择药物及其相互作用的统计模型和算法；(2)开发实验设计和统计分析以利用目标1中的计算机结果来表征剂量反应；(3)开发用于多药组合相互作用(协同)分析的统计方法；(4)在已经由NCI和其他资助机构资助的研究中测试该方法在癌细胞系中的方法；以及(5)丰富在目标1-3中开发的计算机程序。项目完成后，预计该方法将能够服务于更大的翻译研究社区，并将对涉及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步骤。这项建议的目标是开发统计方法，以优化设计和有效地分析癌症和其他复杂疾病(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，并为进一步在体内研究组合提供了基础。

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.