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.