Respiratory irritation is an important human health endpoint in chemical risk assessment. There are two established modes of action of respiratory irritation, 1) sensory irritation mediated by the interaction with sensory neurons, potentially stimulating trigeminal nerve, and 2) direct tissue irritation. The aim of our research was to, develop a QSAR method to predict human respiratory irritants, and to potentially reduce the reliance on animal testing for the identification of respiratory irritants. Compounds are classified as irritating based on combined evidence from different types of toxicological data, including inhalation studies with acute and repeated exposure. The curated project database comprised 1997 organic substances, 1553 being classified as irritating and 444 as non-irritating. A comparison of machine learning approaches, including Logistic Regression (LR), Random Forests (RFs), and Gradient Boosted Decision Trees (GBTs), showed, the best classification was obtained by GBTs. The LR model resulted in an area under the curve (AUC) of 0.65, while the optimal performance for both RFs and GBTs gives an AUC of 0.71. In addition to the classification and the information on the applicability domain, the web-based tool provides a list of structurally similar analogues together with their experimental data to facilitate expert review for read-across purposes.
Bibliographical noteFunding Information:
The research has been funded by the National Centre for the Replacement Refinement & Reduction of Animal in Research - NC3Rs CRACK-IT program ( https://nc3rs.org.uk/crackit/respiratox ) with grant reference NC/C017S01/1 . Dr. Andreas Karwath was partially supported by the Medical Research Council grant MR/S003991/1 . The authors like to thank Professor Dr. Yves C. Alarie for providing threshold limit value datasets and for his helpful discussions on the topic.
- Human respiratory irritation
- In silico
- Machine learning
- Web application
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