A Cheminformatics Workflow to Select Representative TSCA Chemicals for New Approach Methodology (NAM) Screening

The Toxic Substances Control Act (TSCA) requires the US EPA to evaluate the hazard and exposure of new and existing chemicals. New chemical notifications are typically data-poor and EPA has historically relied upon approaches including chemical categories to fill data gaps. As part of a multi-year Research Program, opportunities are being explored to leverage New Approach Methods (NAMs) in hazard and exposure assessments. Data from a battery of in vitro NAMs will be generated to form a case study for an adaptable approach to inform new chemical assessments. Herein, a cheminformatics workflow was developed to identify a set of ∼300 representative candidate chemicals for in vitro screening from the TSCA non-confidential active inventory. The freely available web application ClassyFire was used to categorize all discrete organic structures from the TSCA inventory into one of 68 primary structural categories. Large primary categories were subcategorized into smaller categories using hierarchical agglomerative clustering, ultimately yielding 180 structural terminal categories. The inventory was filtered to substances that lacked previous ToxCast bioactivity screening, were associated with physicochemical property predictions indicating non-volatile solids or liquids, and had a higher chance of procurement. Amenability predictions for liquid chromatography-mass spectrometry were also generated to provide an indication of which chemicals lent themselves to aqueous-based screening and analytical verification in solvated samples. Structures associated with transformation in solvent, potentially explosive or highly reactive, were excluded. Potential candidate substances were selected on the basis of being structurally representative of the terminal category and meeting other screenability conditions. A final set of 318 candidate chemicals were proposed to undergo analytical quality control and screening in a range of broad and targeted biological technologies for human health-relevant end points. Finally, in silico tools were applied to explore predicted hazard profiles of these candidate substances relative to the full inventory.