RespiraTox – Development of a QSAR model to predict human respiratory irritants

Matthias M. Wehr*, Satinder S. Sarang, Martijn Rooseboom, Peter J. Boogaard, Andreas Karwath, Sylvia E. Escher

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number105089
JournalRegulatory Toxicology and Pharmacology
Volume128
DOIs
Publication statusPublished - Feb 2022

Keywords

  • Human respiratory irritation
  • In silico
  • Machine learning
  • QSAR
  • Read-across
  • Web application

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