CHEMICAL ADDRESS TAGS: A Cheminformatic & Image Data Management and Analysis Plan

描述(由申请人提供)：药品不良反应(ADRs)是美国住院和死亡的主要原因之一。ADR通常与不利的药物生物利用度或生物分布情况有关。因此，可以通过优化药物传输特性来预防不良反应--从系统、器官水平到微观、细胞水平。为了提高进入临床试验的药物的质量，开发了新一代显微成像仪器--被称为“高含量筛选”或“HCS”系统。HCS仪器可以提供临床前、基于人体细胞的数据，以补充预测毒理学测试中的动物研究。作为一个高通量平台，HCS系统可用于在与生理相关的检测中筛选大量小分子。现在的挑战是将HCS技术纳入标准的生物医学研究实践，以促进发现毒性较低的候选药物，并提高临床成功率。为了迎接这一挑战，我们建议开发一种化学信息学和图像数据管理和分析计划，以研究荧光小分子在活细胞中的亚细胞定位。受机器视觉方法被用来分析基因组范围内蛋白质亚细胞分布的启发(位置蛋白质组学)，我们提出机器视觉也可以被用来分析小分子荧光候选药物的分布。类似于蛋白质定位是如何由信号肽编码的，我们假设亚细胞小分子定位是由小分子化学结构中发现的“化学地址标签”编码的。为了验证这一假设，我们计划：1)开发自动化、图像分析和化学信息学工具，以客观、定量和高通量的方式对化学地址标签进行反向工程；2)开发和比较两种定量、机器视觉方法来分析线粒体靶向分子的运输特性；3)演示化学信息学驱动的图像数据管理和分析计划如何影响抗癌药物的优化工作。

DESCRIPTION (provided by applicant): Adverse drug reactions (ADRs) are one of the leading causes of hospitalization and death in the United States. ADRs are often associated with unfavorable drug bioavailability or biodistribution profiles. Thus, ADRs could be prevented by optimizing drug transport properties -from the systemic, organ level down to the microscopic, cellular level. To improve the quality of drugs entering clinical trials, a new generation of microscopic imaging instruments -known as "high content screening" or "HCS" systems has been developed. HCS instruments can provide preclinical, human cell-based data to complement animal studies in predictive toxicology testing. As a high-throughput platform, HCS systems can be used to screen large collections of small molecules in physiologically-relevant assays. Now the challenge is to incorporate HCS technology into standard biomedical research practice, to facilitate discovery of less toxic drug candidates with improved clinical success rates. To meet this challenge, we propose to develop a cheminformatic and image data management and analysis plan to study the subcellular localization of fluorescent, small molecules -in living cells. Inspired by machine vision approaches currently being used as a tool to analyze the subcellular distribution of proteins on a genome-wide scale ("location proteomics"), we propose that machine vision could also be adopted as a tool to analyze the distribution of small molecule fluorescent drug candidates. In analogy to how protein location is encoded by signal peptides, we hypothesize that subcellular small molecule localization is encoded by "Chemical Address Tags" to be discovered within the chemical structure of small molecules. To test this hypothesis, we plan to: 1) Develop automated, image analysis and cheminformatic tools to reverse- engineer Chemical Address Tags in an objective, quantitative and high-throughput manner; 2) Develop and compare two quantitative, machine vision approaches to assay the transport properties of mitochondria- targeting molecules; 3) Demonstrate how a cheminformatics-driven, image data management and analysis plan can impact an anticancer drug lead optimization effort.