Richard S. Judson

TL;DR: Tuning expectations of NAM performance to an understanding of the reproducibility and variability, both of traditional approaches and NAM approaches, provides a path for the adopted NAMs as alternatives in screening chemicals for risk.

TL;DR: Enrichment analysis to evaluate the accuracy of PODQSAR showed that 80% of the 5% most potent chemicals were found in the top 20%" of the most potent chemical predictions, suggesting that the repeat dose POD QSAR models presented here may help inform screening level human health risk assessments in the absence of other data.

TL;DR: The findings support the idea that the absence of short-term bioactivity may provide useful information for prioritizing chemicals based on potential carcinogenic risk, and additional data streams are needed to further refine these models.

TL;DR: A predictive model for cytotoxicity based on U.S. EPA Toxicity ForeCaster data tested up to 100 μM is built and probabilistic design rules are provided to help synthetic chemists minimize the chance that a newly synthesized chemical will be cytotoxic.

TL;DR: High-throughput screening data from the ToxCast program is utilized, coupled with chemical structural information, to generate chemical clusters using three similarity methods pertaining to nine MIEs within an AOP network for hepatic steatosis to illustrate how the AOP framework can support an iterative process whereby in vitro toxicity testing and chemical structure can be combined to improve toxicity predictions.