How Well Can Carcinogenicity Be Predicted By High Throughput "Characteristics Of Carcinogens" Mechanistic Data?

Exponent Senior Managing Scientists Ms. Mary Ko Manibusan and Dr. James Bus recently co-authored the article, "How Well Can Carcinogenicity be Predicted by High Throughput 'Characteristics of Carcinogens' Mechanistic Data?" The article is featured in the journal Regulatory Toxicology and Pharmacology.

The International Agency for Research on Cancer (IARC) has begun using ToxCast/Tox21 data in an effort to represent key characteristics of carcinogens to organize and weigh mechanistic evidence in cancer hazard determinations. The USEPA is also considering using this same approach.

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 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.

Read the full article here.