Human vs. cells vs. machine: A comparative analysis of toxicological points of departure derived from quantitative read-across (qRAx), in vitro data, and In silico predictions.

Read-across is an expert-driven new approach methodology (NAM) used to fill gaps in chemical toxicity data. While qualitative read-across is widely used, quantitative read-across (qRAx) for deriving points of departure (PODs) has received limited attention. We compared the applicability domain, consistency, and conservatism of PODs derived from qRAx, in vitro data, and in silico predictions. Specifically, we first identified 41 substances evaluated by the U.S. EPA's Provisional Peer-Reviewed Toxicity Value (PPRTV) program for qRAx-derived oral chronic PODs. For these same substances, we generated PODs using: (i) in vitro-to-in vivo extrapolation from ToxCast bioactivity; (ii) database-calibrated in silico assessment from ToxValDB; and (iii) three quantitative structure-activity relationship (QSAR) models. Success rates for generating PODs varied considerably: qRAx 83% (34/41), ToxCast 22% (9/41), ToxValDB 66% (27/41), and QSAR 46-100% (19-41/41). qRAx yielded the most conservative PODs in the largest number of cases (44-54%). Combining multiple NAMs including at least one of ToxValDB or a QSAR model results in coverage exceeding 90% and including both in a tiered approach produces PODs that are on average within one order of magnitude of qRAx-derived values. We conclude that well-calibrated in silico methods can rapidly derive PODs with defined uncertainty, supporting time-sensitive health risk decisions.