Toxicogenomics (TGx) combines toxicology with genomics or other high throughput molecular profiling technologies to gain an enhanced understanding of toxicity at the molecular level. Previously, we developed a pair ranking (PRank) method to assess in vitro to in vivo extrapolation (IVIVE) for complex toxicogenomic datasets. Using this approach, we demonstrated that toxicogenomic data generated in hepatocytes in vitro can predict the ranking of a drug’s potential to cause hepatotoxicity in vivo. To further understand the capabilities of preclinical models of hepatotoxicity, we applied the PRank methodology to toxicogenomic data generated in three different rat assay systems, primary hepatocytes 24 hours, in vivo single dose 24 hours and repeated dose 28 days and contained within the Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System (TG-GATEs) database. A high similarity between the two in vivo assay systems was noted (PRank score = 0.90), indicating the potential utility of shorter term in vivo studies to predict outcome in longer term, more expensive in vivo test systems. There was a moderate similarity between rat primary hepatocytes (24 hours) and in vivo repeat-dose studies (PRank score = 0.77) but a low similarity (PRank score = 0.56) between rat primary hepatocytes and in vivo single dose studies suggesting potential differences in response of the test systems. When the comparison was limiting to gene sets relevant to specific toxicogenomic pathways, we found pathways such as lipid metabolism were consistently over-represented in all three assay systems. Similarly, all three assay systems could distinguish compounds from different therapeutic categories. This suggests that any noted differences in assay systems was biological process-dependent and furthermore that all three systems have utiltiy in assessing drug responses within a certain drug class. In conclusion, this comparison of three commonly used rat TGx systems provides useful information in utiltiy and application of TGx assays.
- preclinical models
- gene expression