Proteome Mining as a Predicitve Tool of Drug Toxicity

描述(由申请人提供)： 在这项提案中，我们将利用化学蛋白质组学、代谢组学和信息学的独特组合来开发一种在发现过程的早期阶段对药物毒性进行高度预测的分析方法。为了实现这一目标，我们将特别降低铅的消耗率，这通常发生在药物开发的更昂贵的后期阶段。传统的药物发现方法始于针对单个酶/受体靶点或疾病细胞系的小分子文库的高通量筛选。重要的是，这两种分析都不能预测与其他脱靶蛋白的相互作用，这些脱靶蛋白在药物开发过程的后期更昂贵的阶段，特别是在动物和人类研究中，总是表现为毒性和不良反应。我们的技术称为蛋白质组挖掘，使包含数百个潜在药物靶点(包括治疗和毒性靶点)的靶向蛋白质组能够在可逆亲和阵列中定量捕获，并针对类药物分子进行集体筛选。从这一筛选中获得的知识可用于识别所有偏离目标的负债，并用于推动迭代化学以同时提高效力和选择性。作为概念的证明，我们将利用蛋白质组来针对嘌呤。嘌呤利用酶是人类基因组中表达最频繁的酶，既包含经典的药物靶点(如二氢叶酸还原酶和HMG辅酶A还原酶)，也包含前沿靶点(蛋白激酶和应激诱导蛋白)。重要的是，对目前OMIM数据库的检查表明，利用酶的嘌呤也可能占药物毒性基因组的很大比例，有1500多种不同的酶被确定与先天性新陈代谢错误有关。为了验证我们的技术，我们将检查几种已确定的药物及其代谢物的选择性分布，这些药物在动物和人类中表现出良好的副作用特征。使用最先进的质谱学技术的定向代谢组谱将被用来验证蛋白质组挖掘中确定的潜在毒性靶点确实在体内被抑制。

DESCRIPTION (provided by applicant): In this proposal we will utilize a unique blend of chemoproteomics, metabolomics and informatics to develop a highly predictive assay of drug toxicity at the earliest stages of the discovery process. In achieving this goal we will specifically reduce lead attrition rates that often occur in later more expensive stages of drug development. Traditional approaches to drug discovery begin with high throughput screens of small molecule libraries against single enzyme/receptor target or disease cell line. Importantly, neither assay can predict interactions with other off target proteins that invariable manifest themselves as toxicities and adverse reactions in later more expensive stages of the drug development process, in particular, in animal and human studies. Our technology called proteome mining, enables a targeted proteome containing many hundreds of potential drugs targets (includes therapeutic and toxicity targets) to be quantitatively captured in reversible affinity arrays and screened en masse against drug like molecules. Knowledge derived from this screen can be used to identify all off target liabilities and used to drive iterative chemistry to improve potency and selectivity simultaneously. As a proof of concept we will target the purine utilizing proteome. Purine utilizing enzymes are the most frequently expressed enzymes in the human genome and contain both classical drug targets (e.g. dihydrofolate reductase and HMG CoA reductase) as well as cutting edge targets (protein kinases and stress induced proteins). Importantly, inspection of the current OMIM database reveals that purine utilizing enzymes potentially also represent a significant proportion of the drug toxicity genome, with over 1500 distinct enzymes being identified with associations with inborn errors in metabolism. To validate our technology we will examine the selectivity profiles of several established drugs and their metabolites exhibiting well characterized side effects in animals and humans. Directed metabolomic profiling using state of the art techniques in mass spectrometry will be used to verify that potential toxicity targets identified in proteome mining are indeed inhibited in vivo.