Chemical Address Tags: A Cheminformatics & Image Data Management and Analysis Pla

化学地址标签：化学信息学

• 批准号: 8369178
• 负责人: GUS R ROSANIA
• 金额: $ 46.27万
• 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 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8369178
• 关键词: Address; Adopted; Advanced Development; Adverse effects; Animals; Antibiotics; Area; Biodistribution; Biological; Biological Assay; Biological Availability; Cells; Cessation of life; Chemical Engineering; Chemical Structure; Chemicals; Clinical; Clinical Trials; Collection; Complement; Data; Data Analyses; Data Base Management; Databases; Development; Drug Delivery Systems; Drug Transport; Electron Microscope; Engineering; FDA approved; Face; Gastrointestinal Diseases; Generations; Genus Mycobacterium; Healthcare; Hospitalization; Human; Image; Image Analysis; In Vitro; Incidence; Infarction; Kinetics; Lead; Libraries; Life; Link; Location; Measurement; Mediating; Membrane; Microscopic; Microscopy; Molecular; Morbidity - disease rate; Morphology; Multi-Drug Resistance; Mycobacterium tuberculosis; Nausea; Optics; Organ; Organelles; Outcome; Pathway interactions; Patients; Peptide Signal Sequences; Pharmaceutical Preparations; Phenazines; Pigmentation physiologic function; Polarization Microscopy; Prodrugs; Property; Proteins; Proteomics; Reaction; Risk; Scientific Advances and Accomplishments; Screening procedure; Site; Skin; Structure; System; Techniques; Technology; Testing; Therapeutic; Time; Toxic effect; Toxicology; United States; Variant; Vision; Withdrawal; antibody conjugate; antimicrobial; base; bioaccumulation; bioimaging; cheminformatics; data management; drug candidate; drug distribution; drug market; drug quality; genome-wide; improved; instrument; mortality; next generation; pre-clinical; prevent; protein distribution; research study; small molecule; success; tool

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 advance HCS technology to facilitate development of less toxic drug candidates with improved clinical success rates. To face this challenge, we propose to develop a cheminformatics and image data management and analysis plan to study the subcellular localization of 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 drug candidates possessing a detectable optical signature. 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 cheminformatics 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 organelle-targeting molecules; 3) Demonstrate how a cheminformatics-driven, image data management and analysis plan can impact a drug lead optimization effort. PUBLIC HEALTH RELEVANCE: Scientific advances to reduce the risk of adverse drug reactions (ADRs) will transform health care by: i) reducing the incidence of drug-related morbidity and mortality; ii) reducing withdrawal of otherwise effective drugs from the market; and, iii) facilitating regulatory approval of new, safer drugs. Strategies to reduce the incidence f side-effects by improving drug transport properties will be of great benefit to patients across all therapeutic areas.

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，可以通过优化从系统性的，器官水平到显微镜，细胞水平的药物传输特性来预防ADR。为了提高进入临床试验的药物的质量，已经开发了一种新一代的微观成像仪器，称为“高素质筛选”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，挑战是促进HCS技术，以促进具有提高临床成功率的毒性较小的候选药物的发展。为了面对这一挑战，我们建议开发一种化学信息和图像数据管理和分析计划，以研究活细胞中小分子的亚细胞定位。受到当前用作机器视觉方法的启发，用于分析蛋白质在全基因组范围内的亚细胞分布（“位置蛋白质组学”），我们建议还可以将机器视觉作为分析具有可检测光学签名的小分子药物分布的工具。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，图像分析和化学信息学工具以客观，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，以测定细胞器靶向分子的传输特性； 3）演示化学信息元素驱动的，图像数据管理和分析计划如何影响药物铅优化工作。 公共卫生相关性：降低不良药物反应风险（ADR）的科学进步将通过：i）降低与药物相关的发病率和死亡率的发生率； ii）减少从市场上撤出其他有效的药物； iii）促进新的，更安全的药物的监管批准。通过改善药物运输特性来降低副作用的策略对所有人的患者有很大的好处 治疗区域。