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Background and Aims: Drug‐induced liver injury (DILI) is one of the most frequent reasons for failure of drugs in clinical trials or market withdrawal. Early assessment of DILI risk remains a major challenge during drug development. Here, we present a mechanism‐based weight‐of‐evidence approach able to identify certain candidate compounds with DILI liabilities due to mitochondrial toxicity. Methods: A total of 1587 FDA‐approved drugs and 378 kinase inhibitors were screened for cellular stress response activation associated with DILI using an imaging‐based HepG2 BAC‐GFP reporter platform including the integrated stress response (CHOP), DNA damage response (P21) and oxidative stress response (SRXN1). Results: In total 389, 219 and 104 drugs were able to induce CHOP‐GFP, P21‐GFP and SRXN1‐GFP expression at 50 μM respectively. Concentration response analysis identified 154 FDA‐approved drugs as critical CHOP‐GFP inducers. Based on predicted and observed (pre‐)clinical DILI liabilities of these drugs, nine antimycotic drugs (e.g. butoconazole, miconazole, tioconazole) and 13 central nervous system (CNS) agents (e.g. duloxetine, fluoxetine) were selected for transcriptomic evaluation using whole‐genome RNA‐sequencing of primary human hepatocytes. Gene network analysis uncovered mitochondrial processes, NRF2 signalling and xenobiotic metabolism as most affected by the antimycotic drugs and CNS agents. Both the selected antimycotics and CNS agents caused impairment of mitochondrial oxygen consumption in both HepG2 and primary human hepatocytes. Conclusions: Together, the results suggest that early pre‐clinical screening for CHOP expression could indicate liability of mitochondrial toxicity in the context of DILI, and, therefore, could serve as an important warning signal to consider during decision‐making in drug development. [ABSTRACT FROM AUTHOR]

The scope of physiologically based pharmacokinetic (PBPK) modeling can be expanded by assimilation of the mechanistic models of intracellular processes from systems biology field. The genome scale metabolic networks (GSMNs) represent a whole set of metabolic enzymes expressed in human tissues. Dynamic models of the gene regulation of key drug metabolism enzymes are available. Here, we introduce GSMNs and review ongoing work on integration of PBPK, GSMNs, and metabolic gene regulation. We demonstrate example models. [ABSTRACT FROM AUTHOR]