Design & Analysis of Preclinical Combination Studies

描述(由申请人提供)：本提案的目标是开发统计方法，以优化设计和有效地分析癌症的临床前药物组合研究。药物组合是癌症化疗的核心。统计方法是必要的，因为即使对几乎基因相同的动物施以完全相同的剂量，也可能导致不同的效果衡量标准。这种数据差异需要在试验设计中加以控制，并在分析中加以考虑。经典的等同构图和中间效应模型被用来表示协同/拮抗。这两种方法在很大程度上都忽略了与估计的组合指数相关的不确定性，并且没有充分描述治疗效果在统计学上与最佳效果不同的地方，联合效果优于单一药物治疗的地方，给药的时间顺序间隔是否影响疗效，以及如何有效地利用来自异种移植模型的数据来比较不同的治疗方案。更重要的是，很少有方法可以选择实现研究目标所需的组合和样本量(例如动物数量)。我们最近的初步研究表明，对组合的肿瘤异种移植实验可以进行优化设计，以便可以在适度的样本量下估计组合的剂量效应，并更有效地进行分析，从而优化研究资源分配并产生最可解释的数据。该方法代表了现代统计方法、数论方法和药理学中的概念的整合。在这一应用中，我们建议(1)发展实验设计，在两种药物的联合研究中，根据强大的统计检验，最优地选择组合并确定样本量；(2)扩展方法，以考虑两种药物给药之间的时间间隔的影响，并将多药联合应用于肺癌细胞系的三种药物联合；(3)基于验证的统计模型推广组合指数，并开发表征协同作用的方法，以便在比较剂量计划时探索剂量-效应区域和肿瘤生长的内在本质；(4)将所开发的统计方法应用于目前由NCI资助的儿童实体肿瘤计划项目和NCI合作协议的组合研究，并用可自由获得的R语言丰富AIMS 1-3中开发的计算机程序。预计该方法将使涉及发展治疗学组合的研究广泛受益，并且在特定目标4中的分析将直接有益于NCI资助的两个项目的治疗发展目标。

DESCRIPTION (provided by applicant): The goal of this proposal is to develop statistical methods to optimally design and efficiently analyze preclinical drug combination studies in cancer. Drug combination is central to cancer chemotherapy. A statistical approach is necessary since even the administration of precisely the same dose to virtually genetically identical animals may result in different measures of effect. Such data variation needs to be controlled in the experimental design and accounted for in the analysis. Classically, the isobologram and the median effect model have been used to indicate synergism/antagonism. Both methods, for the most part, ignore the uncertainty associated with the estimated combination index and do not adequately describe where the treatment effect is statistically different from the optimum effect, where the joint effect is superior to single-drug treatments, whether the efficacy is affected by the time sequential interval in the administration of the drug and how to efficiently use data from xenograft models to compare different treatment regimens. More importantly, few methods are available to choose combinations and samples sizes (e.g., number of animals) needed to achieve the goal of the study. Our recent preliminary studies have shown that tumor xenograft experiments on combinations can be optimally designed so that dose-effect of the combination can be estimated with moderate sample sizes and more efficiently analyzed, allowing optimal allocation of research resources and produces most interpretable data. The approach represents an integration of concepts in modem statistical methods, number-theoretic methods and pharmacology. In this application, we propose (1) To develop experimental design to optimally choose combinations and determine sample sizes in combination studies of two drugs for common classes of single drug dose-effect models that include non-constant relative potency based on a powerful statistical test; (2) To extend the methods to account for the effect of the time interval between administrations of the two drugs and to include multiple-drug combinations with applications to a three-drug combination for lung cancer cell lines; (3) To generalize the combination index based on a validated statistical model and to develop methods to characterize synergy so that the regions of dose-effect can be explored and the intrinsic nature of tumor growth can be accounted for in comparing dose schedules; (4) To apply the developed statistical methods to combination studies in a currently NCI funded program project on childhood solid tumors and an NCI cooperative agreement and to enrich computer programs in developed in Aims 1-3 in the freely available R language. It is expected that the method will benefit studies involving combinations in developmental therapeutics broadly and analyses in Specific Aim 4 will of immediate benefit to the therapeutic development goals of two NCI funded projects.