February 21, 2018
Dr. James Bus, Senior Managing Scientist in Ä¢¹½tv's Health Sciences practice, recently co-authored the article, "How Well Can Carcinogenicity be Predicted by High Throughput 'Characteristics Of Carcinogens' Mechanistic Data?" The article was published in the journal Regulatory Toxicology and Pharmacology.
The article was judged by the Society of Toxicology (SOT) Risk Assessment Specialty Section to be the Best Published Paper Demonstrating an Application of Risk Assessment for the year 2017.
IARC has begun using ToxCast/Tox21 data in efforts to represent key characteristics of carcinogens to organize and weigh mechanistic evidence in cancer hazard determinations and this implicit inference approach also is being considered by USEPA. To determine how well ToxCast/Tox21 data can explicitly predict cancer hazard, this approach was evaluated with statistical analyses and machine learning prediction algorithms. Substances USEPA previously classified as having cancer hazard potential were designated as positives and substances not posing a carcinogenic hazard were designated as negatives. Then ToxCast/Tox21 data were analyzed both with and without adjusting for the cytotoxicity burst effect commonly observed in such assays. Using the same assignments as IARC of ToxCast/Tox21 assays to the seven key characteristics of carcinogens, the ability to predict cancer hazard for each key characteristic, alone or in combination, was found to be no better than chance. Hence, we have little scientific confidence in IARC's inference models derived from current ToxCast/Tox21 assays for key characteristics to predict cancer. This finding supports the need for a more rigorous mode-of-action pathway-based framework to organize, evaluate, and integrate mechanistic evidence with animal toxicity, epidemiological investigations, and knowledge of exposure and dosimetry to evaluate potential carcinogenic hazards and risks to humans.
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