Gentamicin is an aminoglycoside antibiotic whose clinical use is limited by nephrotoxicity, while the underlying mechanistic drivers remain incompletely understood. In this study, we reanalyze previously published transcriptomics data where RPTEC/TERT1 cells were exposed to gentamicin. We use a transcriptomics-driven coregulated gene network approach to identify key events (KEs) relevant to gentamicin-induced nephrotoxicity from a broad adverse outcome pathway (AOP) network. We subsequently employ concentration–response modeling combined with bootstrapping to generate artificial response–response data, which we use to define and calibrate a quantitative AOP (qAOP) network model specific to gentamicin exposure. This approach enables the generation of biologically meaningful hypotheses despite limited experimental data availability, for example, quantifying the contribution of different network pathways to cytotoxicity. Specifically, our analysis suggests that cytotoxicity depends more strongly on mitochondrial than on lysosomal disruption. This analysis can be validated by studying how experimental manipulation of these KEs affects cytotoxicity, and by including additional gentamicin concentrations. The qAOP model resulting from our analysis can be directly applied to other compounds sharing a similar mode of action, while the overall methodology is widely applicable to diverse biological case studies for which transcriptomics data and a broad AOP network are available. Notably, this framework does not rely on extensive experimental conditions, highlighting its potential utility for mechanistic understanding and predictive toxicology.
Tillio et al. (Mon,) studied this question.