Knowledge infrastructure for integrated data management and analysis supporting new approach methods in predictive toxicology and risk assessment

Toxicol In Vitro. 2024 Oct:100:105903. doi: 10.1016/j.tiv.2024.105903. Epub 2024 Jul 22.

Abstract

The EU-ToxRisk project (2016-2021) was a large European project working towards shifting toxicological testing away from animal tests, towards a toxicological assessment based on comprehensive mechanistic understanding of cause-consequence relationships of chemical adverse effects. More than 40 partners from scientific institutions, industry and regulators coordinated their work towards this goal in a six-year long programme. The breadth and variety of data and knowledge generated, presented a challenging data management landscape. Here, we describe our approach to data management as developed under EU-ToxRisk. The main building blocks of the data infrastructure are: 1) An easy-to-use, extensible data and metadata format; 2) A flexible system with protocols for data capture and sharing from the entire consortium; 3) A methods database for describing and reviewing data generation and processing protocols; 4) Data archiving using a sustainable resource; 5) Data transformation from the archive to the system that provides granular access; 6) Application Programming Interface (API) for access to individual data points; 7) Data exploration and analysis modules, based on a «web notebook» approach to executable data processing documentation; and 8) Knowledge portal that ties together all of the above and provides a collaboration space for information exchange across the consortium. This knowledge infrastructure is being extended and refined for the support of follow-up projects (RISK-HUNT3R, ASPIS cluster, European Open Science Cloud (2021-2026)).

Keywords: Data management; Data resource sustainability; FAIR data; Knowledge management; Toxicology data.

MeSH terms

  • Animals
  • Data Management
  • Databases, Factual*
  • Humans
  • Risk Assessment / methods
  • Toxicology* / methods