Molecular signatures of synergy

作用于单个分子靶标的药物通常不足以对抗真菌感染、多基因疾病（如癌症）和多细胞或组织类型疾病（包括免疫和炎症性疾病）。同时影响几个靶点的多组分疗法不易产生耐药性，并提高治疗效果。多组分疗法的主要益处之一是潜在的协同效应：即，药物组合的总体治疗益处大于单个药剂的效应之和。特别地，组成化合物之间的协同作用可以提供更宽的药理学窗口和降低的毒性。有许多挑战与分子协同作用的鉴定，似乎这种协同作用是罕见的。 事实上，即使是经过充分研究的酵母，也只知道约20种协同作用。高通量筛选可以检测数千个化合物对的数量级，但这代表了可用化学组合指数的一小部分。如果我们考虑筛选文库中有> 1000万种化合物，则实验技术不太可能足以以成本有效和及时的方式完全调查仅成对协同作用（~1013对）的巨大空间。显然需要一种方法，将这一空间筛选为可管理的大量组合，以丰富协同组合。我们建议构建一个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。使用从酵母或人类细胞系产生的高通量基因表达谱进行一个单一的化学扰动，我们的从头生物信息学工具将预测的化合物，这是非常有可能表现出成对的协同作用对。此外，分析的下游转录变化所造成的对药物，无论是在组合或单独，将提供基本的洞察协同作用的分子性质。

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.