In Silico Prediction of Eye Irritation Using Hansen Solubility Parameters and Predicted pKa Values. Issue 3 (May 2023)

• Record Type: Journal Article
• Authors: Andersson, Martin; Norinder, Ulf; Chavan, Swapnil; Cotgreave, Ian
• Is Part Of: ATLA. Volume 51:Issue 3(2023)
• Page Start: 204
• Page End: 209
• Publication Date: 2023-05
• Subjects: computational toxicology -- eye irritation -- genetic algorithm optimisation -- Hansen Solubility Parameters -- in silico prediction; Laboratory animals -- Periodicals; 616.0273
• DOI: 10.1177/02611929231175676
• Languages: English
• ISSNs: 0261-1929

Abstract

An in silico method has been developed that permits the binary differentiation between pure liquids causing serious eye damage or eye irritation, and pure liquids with no need for such classification, according to the UN GHS system. The method is based on the finding that the Hansen Solubility Parameters (HSP) of a liquid are collectively important predictors for eye irritation. Thus, by applying a two-tier approach in which in silico -predicted pKa values (firstly) and a trained model based solely on in silico -predicted HSP data (secondly) were used, we have developed, and validated, a fully in silico approach for predicting the outcome of a Draize test (in terms of UN GHS Cat. 1/Cat. 2A/Cat. 2B or UN GHS No Cat.) with high validation set performance (sensitivity = 0.846, specificity = 0.818, balanced accuracy = 0.832) using SMILES only. The method is applicable to pure non-ionic liquids with molecular weight below 500 g/mol, fewer than six hydrogen bond donors (e.g. nitrogen–hydrogen or oxygen–hydrogen bonds) and fewer than eleven hydrogen bond acceptors (e.g. nitrogen or oxygen atoms). Due to its fully in silico characteristics, this method can be applied to pure liquids that are still at the desktop design stage and not yet in production.