106 LungCHEM-Py: a computational model to predict asthma exacerbation risk from indoor air pollutants

Abstract Indoor environments contain a variety of air pollutants from a wide range of sources. These include particulates, as well as gas-phase volatile organic compounds (VOCs), from sources such as personal care products, cooking, and cleaning. In recent years, evidence that VOCs can worsen asthma symptoms has been increasing, although links between individual VOCs and specific biological effects are still lacking. In this interdisciplinary project, we aim to link room VOC composition with asthma-exacerbation potential, through the development of a new computational model—The Lung Chemistry Model in Python (LungCHEM-Py). Here, we present an overview of LungCHEM-Py, alongside the experimental studies which underpin it. Firstly, to inform modelled chemical kinetics of inhaled VOCs, a novel VOC-liquid interaction experimental system has been developed, to determine the chemical fates of VOCs in a lung-like environment. Preliminary experiments show that some species are likely to deposit in the proximal airways (eg ethanol), while others are likely to travel deeper into the respiratory tree (eg limonene), indicating the possibility of different biological targets. The kinetic data is then informing toxicology experiments, where lung cells are exposed to individual VOCs using a Vitrocell Continuous Flow system. The aim is to attribute concentration-dependent, species-specific asthma hazard scores to individual VOCs, so that the overall risk from different occupant activities indoors (eg cooking, cleaning) can be simulated. LungCHEM-Py provides a new mechanism for facilitating healthier indoor environments with respect to respiratory health, by informing choices around product use and domestic activities, based on their VOC emissions.