Computational models for hazard identification of flavor compounds in tobacco products
Flavor chemicals contribute to the appeal and toxicity of tobacco products, and the assortment of flavor chemicals available for use in tobacco products is extensive. However, potential harms from inhaling these substances and their byproducts have not been extensively studied. To help address this data gap, FDA scientists (Goel et al. 2022) used a chemistry-driven computational approach to evaluate flavor chemicals based on intrinsic hazardous structures and reactivity of chemicals. A library of 3,012 unique flavor chemicals was compiled from publicly available information, and a structure-based analysis was done to characterize their (1) physicochemical properties, (2) GHS health hazard classification, (3) structural alerts linked to the chemical’s reactivity, instability, or toxicity, and (4) substructures shared with chemicals characterized by FDA as respiratory toxicants. Computational analysis of the constructed flavor library flagged 638 chemicals with GHS classified respiratory health hazards, 1,079 chemicals with at least one structural alert, and 2297 chemicals with substructural similarity to chemicals on the respiratory toxicant list. A subsequent analysis was performed on a subset of 173 chemicals in the flavor library, from which four general structures with an increased potential for respiratory toxicity were identified. This study indicated that computational methods are efficient tools for hazard identification and understanding structure-toxicity relationship. With appropriate context of use and interpretation, in silico methods may provide scientific evidence to support toxicological evaluations of chemicals in or emitted from tobacco products.