Artificial intelligence-based data extraction for next generation risk assessment: Is fine-tuning of a large language model worth the effort?

To underpin scientific evaluations of chemical risks, agencies such as the European Food Safety Authority (EFSA) heavily rely on the outcome of systematic reviews, which currently require extensive manual effort. One specific challenge constitutes the meaningful use of vast amounts of valuable data from new approach methodologies (NAMs) which are mostly reported in an unstructured way in the scientific literature. In the EFSA-initiated project 'AI4NAMS', the potential of large language models (LLMs) was explored.

A quantitative medium-throughput assay to measure development and reproduction.

Growth and offspring count are two commonly determined toxicological endpoints for chemical- or gene-induced developmental and reproductive effects in . Here, we present a protocol for a 96 h, medium-throughput assay, assessing both endpoints quantitatively within an automated framework using open-source software. The assay utilizes whole 96-well fluorescence images taken with a high-content screening system.

As a Promising Alternative Model for Environmental Chemical Mixture Effect Assessment-A Comparative Study.

A key challenge of mixture toxicity testing is that a multitude of substances with even more combinations need to be tested in a broad dose range. Consequently testing in rodent bioassays, the current gold standard of toxicity testing, is hardly feasible. High-throughput compatible cell culture systems, however, suffer from limitations with respect to toxicokinetics, tissue interactions, and compensatory mechanisms.