Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis

Drug-induced liver
injury (DILI) is a significant concern
in drug
development, often leading to drug withdrawal. Although many studies
aim to identify biomarkers and gene/pathway signatures related to
liver toxicity and aim to predict DILI compounds, this remains a challenge
in drug discovery. With a strong development of high-content screening/imaging
(HCS/HCI) for phenotypic screening, we explored the morphological
cell perturbations induced by DILI compounds. In the first step, cell
morphological signatures were associated with two datasets of DILI
chemicals (DILIRank and eTox). The mechanisms of action were then
analyzed for chemicals having transcriptomics data and sharing similar
morphological perturbations. Signaling pathways associated with liver
toxicity (cell cycle, cell growth, apoptosis, ...) were then captured,
and a hypothetical relation between cell morphological perturbations
and gene deregulation was illustrated within our analysis. Finally,
using the cell morphological signatures, machine learning approaches
were developed to predict chemicals with a potential risk of DILI.
Some models showed relevant performance with validation set balanced
accuracies between 0.645 and 0.739. Overall, our findings demonstrate
the utility of combining HCI with transcriptomics data to identify
the morphological and gene expression signatures related to DILI chemicals.
Moreover, our protocol could be extended to other toxicity end points,
offering a promising avenue for comprehensive toxicity assessment
in drug discovery.