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

• 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
• 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

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）：迫切需要大大增加可与癌症作斗争的药物的曲目：许多药物具有严重的副作用，耐药性可以迅速出现。一种使我们当前选择化学治疗剂选择的有效方法是鉴定出与细胞中验证的药物相似的化合物，但靶向不同的途径成分或作用机理。 大型复合库变得越来越多，并用作此类搜索的起点。但是，这些图书馆中绝大多数化学物质的生物活性是完全未知的。当前，需要重新筛选整个复合库，以查询更多具有类似生物功能的药物的简单任务 - 昂贵，耗时且效率低下的过程。 高维表型筛选非常适合表征系统级别对多种途径和遗传背景的化合物的响应。但是，诸如转录组学或蛋白质组学之类的方法太昂贵且耗时，无法定期缩放到具有成千上万种化合物的库。 一种具有预测生物学功能的化合物库的有力方法是使用基于显微镜的细胞学谱，这是一种量化对扰动的细胞反应的方法，我们实验室在过去十年中率先开创了扰动。通常，仅需要一个屏幕才能获得可用于预测多个功能类别的作用机理的概况。尽管这种方法表现出了希望，但由于试剂的高成本和不确定性哪种细胞读数将最好允许区分广泛的生物功能，因此其使用限于小型化合物库。 在AIM 1中，我们克服了当前的局限性，并开发了一种可扩展且成本效益的方法来识别对多种验证的癌症药物产生相似反应的化合物。在AIM 2中，我们在中型化合物库上校准并测试我们的方法。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库，在多种经过验证的药物类别中识别高价值的治疗前铅，并搜索具有完全新颖的作用机理的化合物。我们的目标将为加速癌症药物发现的速度提供新的范式。