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

化学地址标签：化学信息学

• 批准号: 7132037
• 负责人: GUS R ROSANIA
• 金额: $ 21.85万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7132037
• 关键词: analog; automated data processing; cell line; chemical kinetics; chemical registry /resource; cheminformatics; computer program /software; data management; drug discovery /isolation; evaluation /testing; fluorescence; glycoproteins; high throughput technology; imaging /visualization /scanning; membrane transport proteins; method development; microscopy; mitochondria; protein localization

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.

描述（由申请人提供）：不良药物反应（ADR）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概况有关。因此，可以通过优化药物转运特性来预防ADR-从系统性的器官水平到显微镜，细胞水平。为了提高进入临床试验的药物的质量，已经开发了新一代的微观成像仪器（称为“高内容筛选”或“ HCS”系统）。 HCS仪器可以提供基于人类细胞的临床前数据，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系统可用于筛选与生理相关的测定法中的大量小分子。现在，挑战是将HCS技术纳入标准生物医学研究实践中，以促进发现毒性较小的候选药物，并提高了临床成功率。为了应对这一挑战，我们建议开发一个化学物质和图像数据管理和分析计划，以研究荧光，小分子 - 活细胞中荧光细胞的亚细胞定位。受到当前用作机器视觉方法的启发，用于分析蛋白质范围内蛋白质的亚细胞分布（“位置蛋白质组学”），我们建议也可以将机器视觉作为分析小分子荧光药物候选物的分布的工具。与信号肽如何编码蛋白质位置相比，我们假设亚细胞小分子定位由在小分子的化学结构中发现的“化学地址标签”编码。为了检验这一假设，我们计划：1）开发自动化，图像分析和化学物质工具，以客观，定量和高通量方式反向工程化学地址标签； 2）开发和比较两种定量的机器视觉方法，以测定线粒体靶向分子的运输特性； 3）证明化学信息元素驱动的，图像数据管理和分析计划如何影响抗癌药物铅优化工作。