Quantitative adverse outcome pathway modeling for cigarette smoke-inducible airway mucus hypersecretion. Part 2: Bayesian network modeling for probabilistic risk estimation

The development of <i>in vitro</i> tests that reproduce real-world situations is crucial for toxicity- and disease-risk assessment without animal testing. Because signs and symptoms of health concerns can be complex, it is helpful to create a simplified representation of such manifestations using a conceptual framework such as an adverse outcome pathway (AOP). Combining an AOP with computational models could be a potential tool for the extrapolation of <i>in vitro</i> results to real-world scenarios. Here, we applied Bayesian network-based probabilistic quantitative models for disease-related risk estimation using an <i>in vitro</i> dataset on the AOP of mucus hypersecretion-a known representative symptom of chronic airway disease-obtained by repeated exposure of human bronchial epithelial cells to whole cigarette smoke. We also used a computational aerosol dosimetry model to account for differences between <i>in vitro</i> exposure concentrations and human exposure scenarios. The results revealed dose- and exposure repetition-dependent increases in adverse outcome probability, suggesting that the model reflects the risk continuum of cigarette smoking. Furthermore, under certain assumptions, dosimetry modeling indicated that our <i>in vitro</i> exposure concentrations were similar to actual smoking scenarios. As an exercise, we also calculated <i>in vitro</i> odds ratios for chronic bronchitis that were comparable to the range of real-world odds ratios for chronic bronchitis due to cigarette smoking. Our combinatory risk-assessment approach could be a valuable tool for estimating the chronic inhalation effects of inhalable products and chemicals.