Assessing Hepatoxicants based on High-throughput Quantitative SILAC Proteomics and Causal Biological Networks

Abstract

Drug-induced liver injury can result in the termination of compounds in preclinical development, as well as withdrawal of marketed drugs. Identification of the signaling pathways and proteins involved in injury is an important step towards establishing assays that could be utilized to identify potential hepatotoxicants early in the drug discovery process. In this study we used high throughput quantitative mass spectrometry proteomics involving Stable Isotope Labeling with Amino acids in Cell culture (SILAC) leveraged by a recently developed systems biology approach (Causal Reasoning Engine, CRE) to investigate the effects of hepatotoxic compounds on the cellular proteome of HepG2 cells. Cells were treated with various concentrations of nefazadone, nimesulide, nomifensine, or glafenine, all of which cause hepatotoxicity in humans. Buspirone and rosiglitazone were used as comparator compounds not associated with hepatotoxicity. In comparison to more traditional proteomic analysis tools CRE results provided detailed molecular hypotheses that condense into biological networks and collectively explain a significant number of the measured protein changes. The CRE hypotheses demonstrate that magnitude of response is not necessarily the differentiating factor between DILI and non-DILI compounds but rather the biological processes implicated. Differentiating CRE molecular hypotheses implicate lipid and glucose metabolism, inflammation, oxidative stress and DNA damage as consistent major in vitro discriminating factors. Overall, this paper provides evidence that SILAC data coupled with the CRE analysis method can provide context and new insight into variation of stress responses in hepatotoxic versus non-hepatotoxic compounds even within the same therapeutic class.