We recently published a blog describing four new publications.1 Since this post, we have received news that 2 more papers, submitted earlier this year to the Journal of Computational Toxicology, have been published:
- Evaluating Confidence in Toxicity Assessments Based on Experimental Data and In Silico Predictions2
Reliability, relevance, and confidence are defined within the context of in silico analyses. Practical examples show how to assess model results by applying the concepts, including how in silico and experimental data are combined in weight of evidence assessments.
- Implementation of in silico toxicology protocols within a visual and interactive hazard assessment platform3
This paper outlines an interactive and visual hazard assessment computational platform and describes how in silico models are developed and incorporated based on published in silico toxicology protocols. The use of the platform is illustrated with four case studies.
These publications2-3 will appear in a special issue of the journal on the in silico toxicology protocol initiative.
We would like to thank all co-authors for their contribution to these manuscripts.
We also presented three posters at the recent American College of Toxicology meeting:
- Assessing Abuse Liability using Read-across and Structural Alerts
- Development of a Structure-Activity Relationship Profiler to Predict Mechanism-Based Inhibition of a Metabolite on CYP Enzymes
- Using Metabolically Similar Analogs in Read-Across to Establish Dialkyl-N-Nitrosamine Potency
If you would like a copy of these posters or would like to discuss the findings in these manuscripts, please contact me (Glenn Myatt, email@example.com)
- Johnson C. et al., (2021), Evaluating Confidence in Toxicity Assessments Based on Experimental Data and In Silico Predictions, Computational Toxicology, 100204 https://doi.org/10.1016/j.comtox.2021.100204
- Myatt G.J. et al., (2021), Implementation of in silico toxicology protocols within a visual and interactive hazard assessment platform, Computational Toxicology, 100201 https://doi.org/10.1016/j.comtox.2021.100201