Assigning mode of action to phenotypically discovered anticancer leads.

抽象的。 将作用模式赋予生物活性化合物是药物发现的重要步骤，也是一项重大挑战。 在化学生物学方面。对于来自自然的药物发现来说，这个问题尤其严重。天然产物(NP) 提供合成图书馆中找不到的独特支架，丰富的NP集合是一个巨大的多样性 药物开发的蓄水池。然而，缺乏机械性的理解是临床前研究的主要障碍。 发展。现有的HTS技术允许有效地筛选NP文库，但后续的纯化， 对纯化的代谢物进行化学结构鉴定和MOA评估是冗长、昂贵和乏味的。 更糟糕的是，由于MOA只在最后才确定，许多努力被浪费在隔离多余和无关的点击上。 由于活性成分的提纯需要大量的工作，因此应该只对高价值的成分进行提纯 具有药理新颖性的分子。 目前使用的MOA评估方法使用各种平台，包括基于单元的小组和 生化分析和系统生物学技术，但没有一个能为MOA提供令人满意的解决方案 有问题。在这里，我们描述了一种基于系统生物学方法的替代MOA评估技术 在Attagene开发的。在这种方法下，细胞的反应以转录因子的活性为特征。 (Tf)将细胞信号通路与基因联系起来。使能技术是阶乘，一种专有技术 用于定量转录因子活性分析(TFAP)的Attagene平台。我们演示了TFAP签名支持 通过精确定位受干扰的生物过程和细胞系统，对化学品进行简单的MOA评估。多数 重要的是，这种方法不涉及复杂的生物信息学推断。在这里，我们将扩展TFAP 将MOA归因于天然抗癌药物的途径。在试点研究中，我们检查了TFAP 批准的抗癌药物和抗癌真菌代谢物的签名。我们发现：(I)主要班级 批准的抗癌药物有特定的TFAP特征；(2)抗癌真菌代谢产物也有不同的 TFAP签名。此外，这些特征能够正确地识别代谢物的MOA；(Iii)大多数 出乎意料的是，真菌粗提物和纯化的活性代谢物显示出相同的TFAP特征。这些数据 提出了一种新的方法来评估天然来源的抗癌先导化合物的机理。我们将开发这一技术 与UNC-Greensboro团队合作，拥有700多种纯化的抗癌真菌代谢物，并已建立 结构。首先，我们将为所有FDA批准的药物和联合国食品和药物管理局的大部分药物获得TFAP签名 库(SA1)。然后，我们将分析这些TFAP数据集，并比较抗癌真菌的MOA空间 代谢物和经批准的药物，用于鉴定具有新型MOA(SA2)的代谢物。最后，我们将验证 鉴定真菌粗提物中MOA的方法，允许优先选择高价值菌株进行纯化 (SA3)。实施这一提议将建立一种使用单一工具平台的新方法， 不涉及生物信息学分析，并允许将MOA归因于未经纯化的NP提取物。

ABSTRACT. Assigning the mode of action to bioactive compounds is an essential step in drug discovery and a major challenge in chemical biology. This problem is particularly acute for drug discovery from nature. Natural products (NP) provide unique scaffolds not found in synthetic libraries, and the abundant NP collections are a vast diversity reservoir for drug development. However, the lack of mechanistic understanding is a major hindrance to preclinical development. Existing HTS techniques permit efficacious screening of NP libraries, but the follow-up purification, chemical structure identification, and MOA assessment of purified metabolites are lengthy, costly, and tedious. Worse, as the MOA is determined only at the end, much effort is wasted on isolating redundant and irrelevant hits. Since the purification of active constituents requires significant work, it should be performed only for high-value molecules with pharmacological novelty. Currently used MOA assessment approaches employ various platforms, including panels of cell-based and biochemical assays and systems biology techniques, but none provide a satisfactory solution for the MOA problem. Here, we describe an alternative MOA evaluation technique based on a systems biology approach developed at Attagene. Under this approach, cell response is characterized by the activity of transcription factors (TF) that link cellular signaling pathways to genes. The enabling technology is the FACTORIAL, a proprietary Attagene platform for quantitative TF activity profiling (TFAP). We demonstrated that TFAP signatures enable a straightforward MOA assessment of chemicals by pinpointing perturbed bioprocesses and cell systems. Most importantly, this approach does not involve complex bioinformatic inferences. Here, we will extend the TFAP approach to ascribe the MOA to anticancer drug leads from nature. In pilot studies, we examined TFAP signatures of approved anticancer drugs and anticancer fungal metabolites. We found that (i) major classes of approved anticancer drugs have specific TFAP signatures; (ii) anticancer fungal metabolites, too, have distinct TFAP signatures. Moreover, these signatures allowed correct identification of metabolites' MOA; (iii) most unexpectedly, crude fungal extracts and purified active metabolites showed identical TFAP signatures. These data suggest a new approach to the mechanistic evaluation of nature-derived anticancer leads. We will develop this approach with a UNC-Greensboro team with over 700 purified anticancer fungal metabolites with established structures. First, we will obtain TFAP signatures for all FDA-approved drugs and a large fraction of the UNCG library (SA1). Then, we will analyze these TFAP datasets and compare the 'MOA spaces' for the anticancer fungal metabolites and approved drugs to identify metabolites with novel MOA (SA2). Finally, we will validate the approach to identify the MOA in crude fungal extracts, allowing prioritizing high-value strains for purification (SA3). Implementing this proposal will establish a new approach that uses a single instrumental platform, does not involve bioinformatic analyses, and allows ascribing the MOA to unpurified NP extracts.