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A scalable image-based approach for profiling and annotating very large compound

一种可扩展的基于图像的方法，用于分析和注释非常大的化合物

基本信息

批准号：
8762292
负责人：
LANI F WU
金额：
$ 47.92万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-08-01 至 2019-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8762292
关键词：
Adverse effects Affect Animal Model Antineoplastic Agents Biological Biological Assay Biological Markers Biological Process Caring Categories Cell Line Cells Chemical Structure Chemicals Cisplatin Complex Cytotoxic agent Disease Drug resistance Erlotinib Genetic Goals Human Image Informatics Knowledge Libraries Life Malignant Neoplasms Methods Microscopy Molecular Mechanisms of Action Pathway interactions Patients Pattern Pharmaceutical Preparations Process Proteomics Reagent Reporter Resistance Resolution Solutions System Testing Therapeutic Time Tumor-Derived Uncertainty Validation base chemotherapeutic agent chemotherapy cost cost effective drug discovery fighting genetic profiling imaging informatics improved novel pre-clinical programs public health relevance response screening small molecule libraries transcriptomics tumor

项目摘要

DESCRIPTION (provided by applicant): There is a pressing need to dramatically increase the repertoire of drugs available to fight cancer: many drugs have severe side effects and drug resistance can rapidly emerge. An effective approach for diversifying our current selection of chemotherapeutic agents is to identify compounds that show similar effects to proven drugs in cells, but target different pathway components or mechanisms of action. Large compound libraries are becoming increasingly available and serve as starting points for such searches. However, biological activities are completely unknown for the vast majority of chemicals in these libraries. Currently, entire compound libraries need to be re-screened for the seemingly simple task of querying for more drugs with similar biological function-a process that is costly, time consuming and inefficient. High-dimensional phenotypic screens are well suited to characterize systems-level responses to compounds across multiple pathways and genetic backgrounds. However, approaches such as transcriptomics or proteomics are far too expensive and time consuming to be scaled routinely to libraries with hundreds of thousands of compounds. A powerful method for annotating compound libraries with predicted biological function is the use of microscopy-based cytological profiles, an approach for quantifying cellular responses to perturbations that our lab has pioneered over the past decade. Often, only a single screen is necessary to obtain profiles that can be used to predict mechanisms of action across multiple functional categories. Although this approach shows promise, its use has been limited to small compound libraries due to the high cost of reagents and uncertainty about which cellular readouts would best allow broad classes of biological function to be distinguished. In Aim 1, we overcome current limitations and develop a scalable and cost-effective approach for identifying compounds that give similar responses to multiple classes of proven cancer drugs. In Aim 2, we calibrate and test our approach on a medium-sized compound library. In Aim 3, we annotate large compound libraries containing hundreds of thousands of chemicals, identify high-value pre-therapeutic leads in multiple proven drug categories, and search for compounds with completely novel mechanisms of action. Our Aims will provide a new paradigm for accelerating the pace of cancer drug discovery.

描述（由申请人提供）：迫切需要大幅增加可用于抗癌的药物种类：许多药物具有严重的副作用，并且可能迅速出现耐药性。使我们目前选择的化疗药物多样化的一个有效方法是识别出在细胞中表现出与已证实的药物相似效果的化合物，但针对不同的途径成分或作用机制。大型复合库的可用性越来越高，可以作为此类搜索的起点。然而，这些文库中绝大多数化学物质的生物活性是完全未知的。目前，整个化合物库都需要重新筛选，以完成一项看似简单的任务，即查询更多具有相似生物功能的药物——这一过程成本高、耗时长、效率低。高维表型筛选非常适合表征跨多种途径和遗传背景的化合物的系统级反应。然而，转录组学或蛋白质组学等方法过于昂贵和耗时，无法常规地扩展到包含数十万种化合物的文库。对具有预测生物学功能的化合物文库进行注释的一种强大方法是使用基于显微镜的细胞学谱，这是我们实验室在过去十年中率先采用的一种量化细胞对扰动反应的方法。通常，只需要一个屏幕就可以获得可用于预测跨多个功能类别的作用机制的概要文件。尽管这种方法显示出前景，但由于试剂成本高，以及不确定哪种细胞读数最适合区分广泛的生物功能，它的使用仅限于小型化合物文库。在Aim 1中，我们克服了目前的限制，开发了一种可扩展且具有成本效益的方法，用于识别对多种已证实的癌症药物具有相似反应的化合物。在Aim 2中，我们在一个中型复合库上校准和测试我们的方法。在Aim 3中，我们注释了包含数十万种化学物质的大型化合物文库，在多个已证实的药物类别中确定高价值的治疗前先导，并寻找具有全新作用机制的化合物。我们的目标将为加快癌症药物发现的步伐提供一个新的范例。