Molecular signatures of synergy

针对单个分子靶点的药物往往不足以对抗真菌感染、癌症等多基因疾病以及包括免疫和炎症性疾病在内的多种细胞或组织类型疾病。同时影响多个靶点的多组分疗法不太容易产生耐药性，并提高治疗效果。多组分疗法的主要好处之一是潜在的协同效应：即药物组合的总体治疗效益大于单独药物效果的总和。特别是，组成化合物之间的协同作用可以提供更广泛的药理窗口和降低毒性。分子协同效应的鉴定存在许多挑战，而且这种协同效应似乎很少见。事实上，即使是研究得很好的酵母，人们也只知道大约20种协同作用。高通量筛查可以检测几千个化合物对，但这只是可用化学组合指数数的一小部分。如果我们考虑到筛选库中有1000万种化合物，那么实验技术不太可能足以以经济高效和及时的方式完成对仅成对协同作用(约1013对)的巨大空间的调查。显然需要一种方法，将这一空间细化为可管理的大型组合集，以丰富协同组合。我们建议构建一种基因组学和生物信息学相结合的方法来识别和表征分子协同作用。利用酵母或人类细胞系在单一化学扰动下产生的高通量基因表达谱，我们的从头生物信息学工具将预测极有可能表现出成对协同作用的化合物对。此外，分析这两种药物联合或单独引起的下游转录变化，将提供对协同作用的分子性质的基本见解。

Drugs that act against individual molecular targets are often insufficient to combat fungal infections, multigenic diseases such as cancer, and multiple cell or tissue type diseases including immune and inflammatory disorders. Multicomponent therapies that impact several targets simultaneously are less prone to the development of drug resistance, and increase therapeutic efficacy. One of the major benefits of multicomponent therapies is the potential for synergistic effects: that is, the overall therapeutic benefit of the drug combination is greater than the sum of the effects of the individual agents. In particular, synergies between the constituent compounds can provide broader pharmacological windows and reduced toxicity. There are many challenges associated with the identification of molecular synergy and it appears that such synergies are rare. In fact, even for the well-studied yeast, only ~20 synergies are known. High-throughput screens can examine on the order of several thousand of compound pairs, but this represents a very small fraction of the exponential number of chemical combinations available. If we consider that there are >10 million compounds in screening libraries, it is unlikely that experimental techniques will be sufficient to completely survey the vast space of only pairwise synergies (~1013 pairs) in a cost effective and timely fashion. There is a clear need for an approach that winnows this space to a manageably large set of combinations that is enriched for synergistic combinations. We propose to construct a combined genomics and bioinformatics approach to identify and characterize molecular synergies. Using high-throughput gene expression profiles generated from yeast or human cell lines subjected to a single chemical perturbation, our de novo bioinformatics tool will predict pairs of chemical compounds which are highly likely to exhibit a pair-wise synergistic affect. Moreover, analysis of the downstream transcriptional changes caused by the pair of drugs, either in combination or individually, will provide fundamental insight into the molecular nature of synergy.