Transitioning the Generalised Read-Across approach (GenRA) to quantitative predictions: A case study using acute oral toxicity data.

Read-across approaches continue to evolve as does their utility in the field of risk assessment. Previously we presented our generalised read-across (GenRA) approach (Shah et al., 2016), which utilises chemical descriptor and/or bioactivity data to make read-across predictions on the basis of the similarity weighted average of nearest neighbours. The current public version of GenRA predicts 574 apical outcomes as a binary call from repeat dose toxicity studies available in ToxRefDB (Helman et al., 2019). Here we investigated the application of GenRA to quantitative values, specifically using a large dataset of rat oral acute LD50 toxicity data (LD50 values for 7011 discrete chemicals) that had been collected under the auspices of the ICCVAM acute toxicity workgroup (ATWG). GenRA LD50 predictions were made based on the following criteria - chemicals were characterised by Morgan chemical fingerprints with a minimum similarity threshold of 0.5 and a maximum of 10 nearest neighbours over the entire dataset. An R value of 0.61 and RMSE of 0.58 was achieved based on these parameters. Monte Carlo cross validation was then used to estimate confidence in the R. Cross validated R values were found to fall in the range of 0.47 to 0.62. However, when evaluating GenRA locally to clusters of mechanistically or structurally-similar chemicals, average R values improved up to 0.91. GenRA can be extended to make reasonable quantitative predictions of acute oral rodent toxicity with improved performance exhibited for specific local domains.