Discovering Chemical Activity Networks-Predicting Bioactivity Based on Structure

发现化学活性网络——根据结构预测生物活性

• 批准号: 10646393
• 负责人: Robyn L Tanguay
• 金额: $ 85.61万
• 依托单位: OREGON STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-16 至 2029-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10646393
• 关键词: Adolescent; Animals; Biological; CRISPR/Cas technology; Cell Culture Techniques; Chemical Exposure; Chemical Warfare; Chemicals; Communities; Computers; Data; Drug Metabolic Detoxication; Embryo; Environmental Health; Gene Expression; Gene Targeting; Genes; Goals; Health; Human; Human Biology; Informatics; Knock-out; Lead; Libraries; Machine Learning; Mammals; Methods; National Institute of Environmental Health Sciences; Pathway interactions; Pharmacologic Substance; Phenotype; Physiology; Research; Risk; Rodent; Scientific Advances and Accomplishments; Scientist; Structure; Testing; Toxic effect; Toxicology; Uncertainty; Work; Zebrafish; adverse outcome; body system; drug development; experimental study; human disease; overexpression; public database; zebrafish genome

PROJECT SUMMARY NIEHS has established Predictive Toxicology as a strategic goal for advancing environmental health sciences. The overarching goal of this RIVER proposal is to predict animal toxicity of chemicals based on their structure. My team and I will expose millions of zebrafish embryos to a library of 10,000 synthetic chemicals across wide concentration ranges. If a chemical shows signs of bioactivity, we will systematically analyze whole animal gene expression changes before the phenotype appears. We will formulate hypotheses about which biomolecular targets the chemicals attacked initially and which pathways led to the observed endpoint. To test those hypotheses, we will edit the zebrafish genome via CRISPR/Cas9 to knock out or over-express critical genes, to discover the ones causally related to the chemical phenotypes. These studies will be highly relevant to human health. Zebrafish possess fully integrated vertebrate organ systems that perform the same functions as their human counterparts and demonstrate well-conserved physiology. Eighty-four percent of the genes that participate in human disease also exist in zebrafish. Zebrafish studies provide a fast, inexpensive way to screen a large volume of chemicals, generate rich hypotheses for drug development, and prioritize candidates for toxicity studies with mammals and human cell cultures. We will compare our results with those of human cell culture studies to clarify the strengths and weaknesses of each method and to reduce the uncertainty associated with applying zebrafish results to human biology. We will post our experimental results in a public database that explains which of the 10,000 Tox21 chemicals are bioactive, which initial targets they strike, and which pathways lead to which endpoints in embryonic and juvenile zebrafish. This information will enable green chemists to detoxify products by substituting a biologically inactive molecule. It will help toxicologists and risk assessors to prioritize chemicals for expensive experiments with rodents and human cell cultures. It will give pharmaceutical scientists thousands of new data points upon which to develop hypotheses about how to modulate a given gene target or activate a given pathway. We will use machine-learning-based chemoinformatic approaches to analyze our zebrafish data and infer the relationship between the structure of a chemical and its biological activity. Our rich data about chemical activity networks will advance the scientific community’s understanding of linkages between chemical exposure and phenotypes. Our work will enable scientists to predict whether a chemical will be biologically active, what target it will act upon, and what networks it will perturb, solely on the basis of its structure. It will enable scientists to reduce, refine, and replace experiments with animals, including zebrafish, and to predict chemical activity networks with computers.

项目概要 NIEHS 将预测毒理学作为推进环境健康科学的战略目标。 该 RIVER 提案的首要目标是根据化学品的结构预测其对动物的毒性。 我和我的团队将把数百万个斑马鱼胚胎暴露在包含 10,000 种合成化学物质的库中 如果化学物质显示出生物活性迹象，我们将系统地分析整个动物基因。 在表型出现之前表达发生变化我们将提出关于哪种生物分子的假设。 针对最初受到攻击的化学物质以及导致观察到的终点的途径。 假设，我们将通过 CRISPR/Cas9 编辑斑马鱼基因组，敲除或过度表达关键基因， 发现与化学表型因果相关的那些。 这些研究将与人类健康高度相关。斑马鱼拥有完全整合的脊椎动物器官。 与人类伙伴执行相同功能并表现出良好保守性的系统 与人类疾病有关的 84% 的基因也存在于斑马鱼中。 研究提供了一种快速、廉价的方法来筛选大量化学物质，产生丰富的药物假设 我们将优先考虑对哺乳动物和人类细胞培养物进行毒性研究的候选者。 将我们的结果与人类细胞培养研究的结果进行比较，以阐明各自的优点和缺点 方法并减少将斑马鱼结果应用于人类生物学相关的不确定性。 我们将在公共数据库中发布我们的实验结果，解释 10,000 种 Tox21 化学品中的哪些 具有生物活性，它们攻击哪些初始目标，以及哪些途径导致胚胎和胚胎中的哪些终点 这些信息将使绿色化学家能够通过替代来对产品进行解毒。 它将帮助毒理学家和风险评估人员优先考虑昂贵的化学品。 啮齿动物和人类细胞培养物的实验将为制药科学家提供数千个新数据。 提出有关如何调节给定基因靶标或激活给定基因的假设的要点 途径。 我们将使用基于机器学习的化学信息学方法来分析我们的斑马鱼数据并推断 化学物质的结构与其生物活性之间的关系。我们丰富的化学数据。 活动网络将促进科学界对化学品接触之间联系的理解 我们的工作将使科学家能够预测化学物质是否具有生物活性，以及​​具有什么活性。 它将针对哪些目标采取行动，以及它将扰乱什么网络，完全取决于它的结构。 科学家们减少、改进和取代了包括斑马鱼在内的动物实验，并预测化学物质 与计算机的活动网络。

项目成果

Advancing Computational Toxicology by Interpretable Machine Learning.

通过可解释的机器学习推进计算毒理学。

• 发表时间: 2023-11-21
• 影响因子: 11.4
• 作者: Jia, Xuelian;Wang, Tong;Zhu, Hao
• 通讯作者: Zhu, Hao

Comparative analysis between zebrafish and an automated live-cell assay to classify developmental neurotoxicant chemicals.

斑马鱼和自动活细胞测定之间的比较分析，以对发育神经毒性化学物质进行分类。

• 发表时间: 2023-10-01
• 影响因子: 3.8
• 作者: St Mary, Lindsey;Truong, Lisa;Bieberich, Andrew A;Fatig 3rd, Raymond O;Rajwa, Bartek;Tanguay, Robyn L
• 通讯作者: Tanguay, Robyn L

Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data.

基于化学结构和生物学数据的结合预测产前发育毒性。

• 发表时间: 2022-05-03
• 影响因子: 11.4
• 作者: Ciallella, Heather L;Russo, Daniel P;Sharma, Swati;Li, Yafan;Sloter, Eddie;Sweet, Len;Huang, Heng;Zhu, Hao
• 通讯作者: Zhu, Hao