Calculating target bias in small molecules for library design

计算文库设计中小分子的目标偏差

• 批准号: 8124290
• 负责人: Carl Nicholas Hodge
• 金额: $ 25.02万
• 依托单位: SEACHANGE PHARMACEUTICALS, INC.
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8124290
• 关键词: Animal Model; Animals; Biological; Biological Assay; Biology; Cataloging; Catalogs; Cells; Chemical Structure; Chemicals; Collaborations; Development; Disease; Drug Delivery Systems; Drug usage; Genotype; Goals; Hand; Letters; Libraries; Ligands; Methods; Molecular Target; Motivation; Organ; Organism; Pattern; Pharmaceutical Preparations; Pharmacologic Substance; Phenotype; Proteins; Research; Research Personnel; Series; Services; Staging; System; Testing; Time; Tissues; Vendor; Whole Organism; Work; design; drug discovery; interest; receptor binding; small molecule; small molecule libraries; theories; therapeutic target; tool; web interface

DESCRIPTION (provided by applicant): Goals: The goal is to develop a computational system to predict the targets toward which small molecules are biased. The tool will optimize biased libraries, prioritize them for testing against particular targets, identify targets in phenotypic screens, and allow users to easily move from compounds to targets. Significance: The tool would allow investigators to easily identify small molecules to modulate a target. It also would allow them to build libraries that are biased toward such targets. Users of the tool include investigators looking to quickly access biological molecules for their targets, companies interested in building libraries biased toward such targets, and vendors looking to expand the usefulness of libraries that they sell. A motivation is a tool that can rapidly bring chemical matter to biologists. Theory/Background: We and others have shown that it is possible to predict previously unknown "off- targets" for drugs. Our Similarity Ensemble Approach (SEA) uses chemical structure to predict targets for any molecule by associating that molecule with similar patterns found in ligands annotated to protein targets. Whereas many commercially available molecules have no target linkage, on average for any given vendor we predict that about T of them do. Aim 1. To develop a service to assign targets to small molecules in large libraries. We will build a system that will allow users to query a library of small molecules, asking one of two questions. a. For a given target, what small molecules in this library would be expected to modulate it? b. For a given library, on what targets are its molecules most likely to work? Milestone: The essential features of this system exist, and proof of principle has been demonstrated in predicting new targets for over 25 drugs using SEA. Here we broaden the method to enable use by non- experts on large compound libraries. There are two pragmatic milestones. i. Experimentally testing predicted compound-target associations to demonstrate feasibility. ii. Development of a web-interface that can be integrated with vendor catalogs. Aim 2. To develop a service that can optimize a library for target coverage. a. Given a particular library size, can we optimize it to cover the maximum number of pharmacologically relevant targets? b. Correspondingly, can we optimize a library for the maximum target bias? Milestone: This method is essentially in hand, but has not been tested. We will work with our commercial partners to optimize their library for target coverage, demonstrating this by predicting and experimentally testing molecules for target activity (six months). Aim 3. To predict the targets for compounds active in phenotypic and animal assays. A recent development in pharmaceutical research and chemical biology has been the rediscovery of phenotypic, even whole organism screens for compound activity, frequently using modified cells or organisms that carry a known disease-associated genotype. A core challenge is identifying the molecular targets responsible for the observed phenotype. Indeed, our partners in pharma indicate that they can have hundreds of related compounds with activity in cells, tissues, organs or animal models, but do not know the actual molecular targets, limiting mechanistic understanding and optimization. We will use SEA to predict targets for these active molecules. Milestone: In collaboration with a pharmaceutical partner, we will predict targets for a compound series with animal model activity, but for which targets remain unknown. Initially we anticipate testing ten such compounds in receptor-binding assays. Whereas these goals are ambitious, extensive preliminary results suggest that they are feasible. PUBLIC HEALTH RELEVANCE: Investigators often test large numbers of compounds to discover a chemical starting point for a new drug. In this proposal we focus on providing a tool to predict which commercially-available compounds are most likely to be active at therapeutic targets. This saves time and money by reducing the number of compounds investigators need to test during early-stage drug discovery.

描述（由申请人提供）：目标：目标是开发一种计算系统来预测小分子偏向的靶标。该工具将优化有偏见的库，优先考虑它们对特定目标的测试，在表型筛选中识别目标，并允许用户轻松地从化合物转移到目标。 意义：该工具将使研究人员能够轻松识别调节靶点的小分子。这也将允许他们建立偏向于这些目标的库。该工具的用户包括希望快速获取目标生物分子的研究人员，有兴趣构建偏向此类目标的库的公司，以及希望扩大其销售的库的有用性的供应商。动机是一种工具，可以迅速将化学物质带给生物学家。 理论/背景：我们和其他人已经表明，预测药物的先前未知的“脱靶”是可能的。我们的相似性包围方法（SEA）使用化学结构来预测任何分子的目标，通过将该分子与蛋白质目标注释的配体中发现的相似模式相关联。尽管许多市售分子没有靶向连接，但平均而言，对于任何给定的供应商，我们预测约T个分子有靶向连接。 目标1.开发一项服务，为大型库中的小分子分配目标。我们将建立一个系统，允许用户查询小分子库，提出两个问题之一。a.对于一个给定的靶点，这个文库中的哪些小分子有望调节它？B.对于一个给定的文库，它的分子最有可能作用于什么靶点？ 里程碑：该系统的基本特征存在，并证明了使用SEA预测超过25种药物的新靶点的原理。在这里，我们扩大了方法，使非专家对大型化合物库的使用。有两个务实的里程碑。I.实验测试预测化合物-目标关联以证明可行性。二.开发可与供应商目录集成的网络界面。 目标2.开发一项服务，可以优化图书馆的目标覆盖范围。a.给定一个特定的库大小，我们是否可以优化它以覆盖最大数量的非相关目标？B.相应地，我们能优化库以获得最大目标偏差吗？ Milestone：这个方法基本上已经掌握，但还没有经过测试。我们将与我们的商业合作伙伴合作，优化他们的目标覆盖率库，通过预测和实验测试分子的目标活性（六个月）来证明这一点。 目标3.预测化合物在表型和动物试验中的活性靶点。药物研究和化学生物学的最新发展是重新发现表型，甚至整个生物体筛选化合物活性，经常使用携带已知疾病相关基因型的修饰细胞或生物体。一个核心挑战是确定负责观察到的表型的分子靶标。事实上，我们的制药合作伙伴表示，他们可以在细胞，组织，器官或动物模型中拥有数百种具有活性的相关化合物，但不知道实际的分子靶点，限制了对机制的理解和优化。我们将使用SEA来预测这些活性分子的靶点。 里程碑：在与制药合作伙伴的合作中，我们将预测具有动物模型活性的化合物系列的靶点，但其靶点仍然未知。最初，我们预计在受体结合试验中测试十种这样的化合物。 虽然这些目标雄心勃勃，但广泛的初步结果表明它们是可行的。 公共卫生相关性：研究人员经常测试大量的化合物，以发现新药的化学起点。在这项提案中，我们专注于提供一种工具来预测哪些市售化合物最有可能在治疗靶点上具有活性。这通过减少研究人员在早期药物发现期间需要测试的化合物数量来节省时间和金钱。