Evaluation of in silico models for the identification of respiratory sensitizers

Abstract

Low molecular weight (LMW) respiratory sensitizers can cause occupational asthma but due to a lack of adequate test methods, prospective identification of respiratory sensitizers is currently not possible. This article presents the evaluation of structure-activity relationship (SAR) models as potential methods to prospectively conclude on the sensitization potential of LMW chemicals. The predictive performance of the SARs calculated from their training sets was compared to their performance on a dataset of newly identified respiratory sensitizers and nonsensitizers, derived from literature. The predictivity of the available SARs for new substances was markedly lower than their published predictive performance. For that reason, no single SAR model can be considered sufficiently reliable to conclude on potential LMW respiratory sensitization properties of a substance. The individual applicability domains (ADs) of the models were analyzed for adequacies and deficiencies. Based on these findings, a tiered prediction approach is subsequently proposed. This approach combines the two SARs with the highest positive and negative predictivity taking into account model specific chemical AD issues. The tiered approach provided reliable predictions for one-third of the respiratory sensitizers and nonsensitizers of the external validation set compiled by us. For these chemicals, a positive predictive value of 96% and a negative predictive value of 89% were obtained. The tiered approach was not able to predict the other two-thirds of the chemicals, meaning that additional information is required and that there is an urgent need for other test methods, e.g., in chemico or in vitro, to reach a reliable conclusion.

Keywords: (Q)SAR; alternatives to animal testing; chemical allergy; predictive toxicology; respiratory sensitization; tiered approach.