Automated Author ProfileMuller, Iris
Unilever (United Kingdom)
Muller, Iris
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 2.2 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Traditional chemical safety assessment involves identifying the lowest level of a chemical that impacts endpoints measured in standardized animal studies together with appropriate safety factors to establish human exposure limits. In vitro assays have shown promise in providing points of departure that can be protective of human health when combined with exposure predictions into a bioactivity:exposure ratio (BER). Using a combination of broad screening tools and DART-targeted assays, we previously demonstrated high biological coverage of this NAM toolbox against a list of DART-relevant genes and pathways. To fully transition to an animal-free paradigm, it is crucial to establish confidence that these in vitro assays sufficiently represent the DART toxicity mechanisms, ensuring a level of protection that is safe for non-pregnant adults, pregnant women, and fetal populations. In this proof-of-concept study, we have extended the toolbox to include additional in vitro and in silico tools and have performed an evaluation using 37 benchmark compounds across 49 exposure scenarios. According to existing regulatory opinions, 18 of these scenarios would be considered high-risk chemical exposures from a DART perspective. Our DART NAM toolbox approach identified 17 out of these 18 high-risk scenarios. We further investigated the impact of gross physiological changes in pregnancy and the fetus on internal exposures by evaluating human clinical data where available for the 37 compounds. In most instances, the variability resulting from pregnancy or gestational changes falls within the range of toxicokinetic variability observed in the general population. This work demonstrates that protective safety decisions can be made for DART without generating new animal test data.
Authors
- Muller, Iris ;
- Abdelkhaliq, Ashraf ;
- Carmichael, Paul ;
- Dent, Matthew ;
- Feliksik, Marleen ;
- Flatt, Luke ;
- Houghton, Jade ;
- Horcas-Nieto, Jose Manuel ;
- Jamalpoor, Amer ;
- Kukic, Predrag ;
- Malcomber, Sophie ;
- Nicol, Beate ;
- Pawar, Gopal ;
- Peart, Claire ;
- Przybylak, Katarzyna ;
- Sawicka, Magdalena ;
- Wilson, Katy ;
- Wolton, Kathryn