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Biogenic alkenes, which are among the most abundant volatile organic compounds in the atmosphere, are readily oxidized by ozone. Characterizing the reactivity and kinetics of the first-generation products of these reactions, carbonyl oxides (often named Criegee intermediates), is essential in defining the oxidation pathways of organic compounds in the atmosphere but is highly challenging due to the short lifetime of these zwitterions. Here, we report the development of a novel online method to quantify atmospherically relevant Criegee intermediates (CIs) in the gas phase by stabilization with spin traps and analysis with proton-transfer reaction mass spectrometry. Ozonolysis of α-pinene has been chosen as a proof-of-principle model system. To determine unambiguously the structure of the spin trap adducts with α-pinene CIs, the reaction was tested in solution, and reaction products were characterized with high-resolution mass spectrometry, electron paramagnetic resonance, and nuclear magnetic resonance spectroscopy. DFT calculations show that addition of the Criegee intermediate to the DMPO spin trap, leading to the formation of a six-membered ring adduct, occurs through a very favorable pathway and that the product is significantly more stable than the reactants, supporting the experimental characterization. A flow tube set up has been used to generate spin trap adducts with α-pinene CIs in the gas phase. We demonstrate that spin trap adducts with α-pinene CIs also form in the gas phase and that they are stable enough to be detected with online mass spectrometry. This new technique offers for the first time a method to characterize highly reactive and atmospherically relevant radical intermediates in situ.

A molecular initiating event (MIE) is the gateway to an adverse outcome pathway (AOP), a sequence of events ending in an adverse effect. In silico predictions of MIEs are a vital tool in a modern, mechanism-focused approach to chemical risk assessment. For 90 biological targets representing important human MIEs, structural alert-based models have been constructed with an automated procedure that uses Bayesian statistics to iteratively select substructures. These models give impressive average performance statistics (an average of 92% correct predictions across targets), significantly improving on previous models. Random Forest models have been constructed from physicochemical features for the same targets, giving similarly impressive performance statistics (93% correct predictions). A key difference between the models is interpretation of predictionsthe structural alert models are transparent and easy to interpret, while Random Forest models can only identify the most important physicochemical features for making predictions. The two complementary models have been combined in a consensus model, improving performance compared to each individual model (94% correct predictions) and increasing confidence in predictions. Variation in model performance has been explained by calculating a modelability index (MODI), using Tanimoto coefficient between Morgan fingerprints to identify nearest neighbor chemicals. This work is an important step toward building confidence in the use of in silico tools for assessment of toxicity.

Deep learning neural networks, constructed for the prediction of chemical binding at 79 pharmacologically important human biological targets, show extremely high performance on test data (accuracy 92.2 ± 4.2%, MCC 0.814 ± 0.093, ROC-AUC 0.96 ± 0.04).

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