Quantifying Analogue Suitability for SAR-Based Read-Across Toxicological Assessment

Structure activity relationship (SAR)-based read-across often is an integral part of toxicological safety assessment, and justification of the prediction presents the most challenging aspect of the approach. It has been established that structural consideration alone is inadequate for selecting analogues and justifying their use, and biological relevance must be incorporated. Here we introduce an approach for considering biological and toxicological related features quantitatively to compute a similarity score that is concordant with suitability for a read-across prediction for systemic toxicity. Fingerprint keys for comparing metabolism, reactivity, and physical chemical properties are presented and used to compare these attributes for 14 case study chemicals each with a list of potential analogues. Within each case study, the sum of these nonstructural similarity scores is consistent with suitability for read-across established using an approach based on expert judgment. Machine learning is applied to determine the contributions from each of the similarity attributes revealing their importance for each structure class. This approach is used to quantify and communicate the differences between a target and a potential analogue as well as rank analogue quality when more than one is relevant. A numerical score with easily interpreted fingerprints increases transparency and consistency among experts, facilitates implementation by others, and ultimately increases chances for regulatory acceptance.

Automated summary

Structure activity relationship (SAR)-based read-across is important for toxicological safety assessment, but justifying the prediction is challenging. A quantitative approach was introduced to consider biological and toxicological features and calculate a similarity score for systemic toxicity prediction. Fingerprint keys were used to compare attributes for 14 case study chemicals and their potential analogues. Machine learning determined the importance of each similarity attribute for different structure classes. This approach improves transparency and consistency, facilitating regulatory acceptance.