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，以优化设计和有效分析癌症临床前药物联合研究。联合用药是癌症化疗的核心。统计方法是必要的，因为即使对几乎遗传相同的动物给予完全相同的剂量，也可能导致不同的效果测量。这种数据变化需要在实验设计中加以控制，并在分析中加以考虑。传统上，等效线图和中位效应模型已用于指示协同/拮抗作用。这两种方法在很大程度上忽略了与估计的联合指数相关的不确定性，并且没有充分描述治疗效果与最佳效果的统计学差异，联合效果上级单药治疗，疗效是否受给药时间间隔的影响，以及如何有效地使用异种移植模型的数据来比较不同的治疗养生法更重要的是，很少有方法可用于选择组合和样本量（例如，动物数量）。我们最近的初步研究表明，肿瘤异种移植实验的组合可以优化设计，使组合的剂量效应可以估计与适度的样本量和更有效地分析，允许最佳分配的研究资源，并产生最可解释的数据。该方法代表了现代统计方法，数论方法和药理学概念的整合。在本申请中，我们提出（1）开发实验设计，以最佳选择组合并确定两种药物联合研究中常见类别的单药剂量效应模型的样本量，这些模型包括基于强大的统计检验的非恒定相对效价;（2）扩展方法，以说明两种药物给药之间的时间间隔的影响，并包括多个-（3）基于经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，以便可以探索剂量效应区域，并在比较剂量方案时可以解释肿瘤生长的内在性质;（4）将开发的统计方法应用于目前NCI资助的儿童实体瘤项目和NCI合作协议的组合研究，并丰富目标1-3中免费提供的R语言开发的计算机程序。预计该方法将有利于广泛涉及发育治疗学组合的研究，并且具体目标4中的分析将直接有利于两个NCI资助项目的治疗开发目标。

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.