Award

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 and expedite product design strategies by linking toxicology hazard profiles in reproductive health to chemicals, exposure and 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 and 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 and 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, and 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 and 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 and medical data to build models and 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, and prototype interface. Development will leverage an existing tool, Sysrev, for automated data extraction from publications and 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.