You are here:
Quantitative Prediction of Repeat Dose Toxicity Values using GenRA
Citation:
Helman, G., G. Patlewicz, AND I. Shah. Quantitative Prediction of Repeat Dose Toxicity Values using GenRA. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, 109:104480, (2019). https://doi.org/10.1016/j.yrtph.2019.104480
Impact/Purpose:
There is an increasing demand for hazard, exposure, and dose-response information to evaluate the safety of thousands of data-poor chemicals in commerce. International chemical management laws including the U.S. Toxic Substances Control Act (TSCA) (EPA, 2008), the European Union’s Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH)(EC, 2006), and the Canadian Chemicals Management Plan (CMP) (ECCC/HC, 2016) are considering the use of new approach methodologies (NAMs) to fill data gaps. This manuscript describes a new approach methodology (NAM) based on the generalised read-across (GenRA) method to predict point of departure (POD) values for chemicals using structural analogues. Using this NAM, it is feasible to predict PODs for thousands on untested chemicals.
Description:
Computational approaches have recently gained popularity in the field of read-across to automatically fill data-gaps for untested chemicals. Previously, we developed the generalized read-across (GenRA) tool, which utilizes in vitro bioactivity data in conjunction with chemical descriptor information to derive local validity domains to predict hazards observed in in vivo toxicity studies. Here, we modified GenRA to quantitatively predict point of departure (POD) values obtained from US EPA’s Toxicity Reference Database (ToxRefDB) version 2.0. To evaluate GenRA predictions, we first aggregated oral Lowest Observed Adverse Effect Levels (LOAEL) for 1,014 chemicals by systemic, developmental, reproductive, and cholinesterase effects. The mean LOAEL values for each chemical were converted to log molar equivalents. Applying GenRA to all chemicals with a minimum Jaccard similarity threshold of 0.5 for Morgan fingerprints and a maximum of 10 nearest neighbors predicted systemic, developmental, reproductive, and cholinesterase inhibition LOAEL values with R2 values of 0.26, 0.22, 0.14, and 0.43, respectively. However, when evaluating GenRA locally to clusters of structurally-similar chemicals (containing 2 to 362 chemicals), average R2 values for systemic, developmental, reproductive, and cholinesterase LOAEL predictions improved to 0.73, 0.66, 0.60 and 0.79, respectively. Our findings highlight the complexity of the chemical-toxicity landscape and the importance of identifying local domains where GenRA can be used most effectively for predicting PODs.
URLs/Downloads:
DOI: Quantitative Prediction of Repeat Dose Toxicity Values using GenRA
https://pubmed.ncbi.nlm.nih.gov/31550520/