ToxIndex-CPG: Machine learning driven platform integrating a hazard susceptibility database to quantify chemical toxicity factors, predict risk levels and classify biological responses

ToxIndex-CPG：机器学习驱动的平台，集成危害敏感性数据库，以量化化学毒性因素、预测风险水平并对生物反应进行分类

• 批准号: 10377742
• 负责人: Thomas Luechtefeld
• 金额: $ 25.59万
• 依托单位: INSILICA, LLC
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-18 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10377742
• 关键词: Agriculture; Animals; Automobile Driving; Biological; Catalogs; Chemical Exposure; Chemical Models; Chemicals; Clinical Data; Complex; Complex Variables; Computational Biology; Cosmetics; Custom; Data; Data Aggregation; Data Set; Data Sources; Databases; Development; Ensure; Etiology; Food Additives; Formulation; Future; Gold; Growth; Health; Health Hazards; Human; Industry; Intuition; Link; Literature; Machine Learning; Mediation; Medical; Medical Device; Medical History; Modeling; Outcome; Pattern; Phase; Predisposition; Product Packaging; PubMed; Publications; Publishing; Quantitative Structure-Activity Relationship; Reproductive Health; Research; Research Design; Research Personnel; Risk; Safety; Scientist; Service delivery model; Source; Testing; Time; Toxic effect; Toxicology; Visualization; Visualization software; Vulnerable Populations; adaptive learning; application programming interface; base; bioinformatics tool; cheminformatics; computerized tools; cost; demographics; design; hazard; in silico; innovation; insight; machine learning model; novel; prediction algorithm; product development; prototype; reproductive; response; searchable database; simulation; software as a service; success; therapy design; tool; toxicant; usability; web app; web based interface; web-based tool

The overall global market for toxicology testing was $8.1 billion in 2019 and is expected to reach $27 billion by 2025. As toxicological testing is a pre-requisite step in most product development, it adds significant time and costs, as well as represents human health hazards when key data is not captured. In order to minimize time to market, expense, and animal use, advances in computational biology and machine learning (ML) are helping conduct more efficient in-silico simulations. These strategies are driving strong growth for advanced computational tools. More specifically, there is currently a strong value proposition in the $635 billon CPG market for tools that ensure safety & expedite product design strategies by linking toxicology hazard profiles in reproductive health to chemicals, exposure & product use cases. This will allow a better understanding and prioritization of chemicals for integration in products to minimize associated reproductive health hazards. The ToxIndex-CPG platform will solve this growing market need through a web-based interface that allows CPG toxicology researchers access to customized data for early product planning & study design. The platform will focus on continuous curation of a database to maintain known relationships in existing literature and data sources, as well as advanced algorithms for predictive relationships for unknown combinations. This project will target CPG products and reproductive health hazards, as this represents major markets & risks to vulnerable populations. The user front end will be designed as a web-based tool for toxicology researchers to query specific chemicals, CPG use cases, & health hazards. Based on query inputs, the platform will return a sorted and ranked list of potential adverse reproductive health outcomes. Researchers will be able to explore impact of specific chemicals on ranked reproductive hazards through advanced visualization tools. Hazard relationships between chemicals and human factors & planned CPG product use cases will be learned through ML using quantitative structure-activity relationship (QSAR) models. The platform will leverage existing data sources for chemical & medical data to build models & continue to adaptively learn as datasets continue to grow. The platform will prioritize application programming interfaces (API) to support a growing market of cheminformatics developers. Phase I will target feasibility of data aggregation, ML development, & prototype interface. Development will leverage an existing tool, Sysrev, for automated data extraction from publications & data sources to increase likelihood of success. The Sysrev platform will parse existing data sources to extract known human factors and use case susceptibility factors for a given chemical toxicant and reproductive health hazards. This will create an initial hazard database of known factors as a gold standard for ML testing. Next, QSAR ML models will be developed to associate chemicals to hazards, and then mediation models from chemicals through hazards to understand causality likelihood in specific human factors and use cases of those chemicals. Finally, a prototype web app and visualizations will be developed and deployed in a usability study with toxicology market users.

2019年，全球毒理学测试市场总额为81亿美元，预计将达到270亿美元