Classification of industrial chemicals for respiratory chemosensory irritation using the TRPV1-expressing neuronal SH-SY5Y cell model and machine learning

Respiratory sensory irritation is the basis for many occupational exposure limits. Irritation thresholds have hitherto mainly been identified in humans by questionnaires and in mice by measuring the inhaled concentration causing 50% respiratory depression (RD50). Both methods are ethically questionable. We investigated an alternative New Approach Methodology (NAM) approach, namely the neuronal SH-SY5Y cell model expressing the sensory receptor TRPV1 in combination with random forest-based machine learning. The intracellular Ca2+ concentration was monitored during acute exposure to different concentrations of 34 organic chemicals. Potency and efficacy were determined with and without the TRPV1 antagonist capsazepine (CZ). Fifteen of the chemicals induced TRPV1 activation at some concentrations, however only phenol appeared as a true TRPV1 agonist. Using machine learning, the parameters EC20, Emax, concentration at Emax, the two first components from principal component analyses, and pH were analysed against previously published RD50 data to classify each chemical as non-irritant/irritant or as non-irritant/ intermediate/irritant. The best 2-class model (accuracy 0.90, outlier frequency 4.8%) was the one using experiments with CZ present, suggesting that the irritancy was not mediated by TRPV1 activation. The best 3-class model (accuracy 0.77, outlier frequency 5.7%) was the one using data without CZ, indicating that TRPV1 activation may play a role for intermediate irritation. The three false negative chemicals, as predicted by the NAMs, were the most irritating chemicals according to RD50 determined in vivo, indicating that other processes may also be important.