Thresholds Derived From Common Measures in Rat Studies Are Predictive of Liver Tumorigenic Chemicals

Abstract

We hypothesized that typical tissue and clinical chemistry (ClinChem) end points measured in rat toxicity studies exhibit chemical-independent biological thresholds beyond which cancer occurs. Using the rat in vivo TG-GATES study, 75 chemicals were examined across chemical-dose-time comparisons that could be linked to liver tumor outcomes. Thresholds for liver weight to body weight (LW/BW) and 21 serum ClinChem end points were defined as the maximum and minimum values for those exposures that did not lead to liver tumors in rats. Upper thresholds were identified for LW/BW (117%), aspartate aminotransferase (195%), alanine aminotransferase (141%), alkaline phosphatase (152%), and total bilirubin (115%), and lower thresholds were identified for phospholipids (82%), relative albumin (93%), total cholesterol (82%), and total protein (94%). Thresholds derived from the TG-GATES data set were consistent across other acute and subchronic rat studies. A training set of ClinChem and LW/BW thresholds derived from a 38 chemical training set from TG-GATES was predictive of liver tumor outcomes for a test set of 37 independent TG-GATES chemicals (91%). The thresholds were most predictive when applied to 7d treatments (98%). These findings provide support that biological thresholds for common end points in rodent studies can be used to predict chemical tumorigenic potential.

A Collaborative Initiative to Establish Genomic Biomarkers for Assessing Tumorigenic Potential to Reduce Reliance on Conventional Rodent Carcinogenicity Studies

“…The 12 individual activation levels when used collectively resulted in high predictive accuracies for 77 chemicals (TG-GATES) or 86 chemicals (DrugMatrix) analyzed (up to 94%) ( Hill et al , 2020 ). In addition to transcriptional biomarkers, the concept of tumorigenic activation levels can be applied to liver: body weight and clinical chemistry markers that have been used to accurately identify chemical-dose pairs that would lead to a tumor outcome ( Corton et al , 2020b ). Thus, these studies provide a high degree of confidence of the feasibility of using gene expression biomarkers to not only identify the AOP responsible for liver tumor induction but also the dose levels that would or would not lead to liver tumor induction.…”