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IL-2-mediated hepatotoxicity: knowledge gap identification based on the irAOP concept.

Luise A RoserChristina SakellariouMalin LindstedtVanessa NeuhausSusann DehmelCharline SommerMartin RaaschThierry FlandreSigrid RoesenerPhilip HewittMichael J ParnhamKatherina SewaldSusanne Schiffmann
Published in: Journal of immunotoxicology (2024)
Drug-induced hepatotoxicity constitutes a major reason for non-approval and post-marketing withdrawal of pharmaceuticals. In many cases, preclinical models lack predictive capacity for hepatic damage in humans. A vital concern is the integration of immune system effects in preclinical safety assessment. The immune-related Adverse Outcome Pathway (irAOP) approach, which is applied within the Immune Safety Avatar (imSAVAR) consortium, presents a novel method to understand and predict immune-mediated adverse events elicited by pharmaceuticals and thus targets this issue. It aims to dissect the molecular mechanisms involved and identify key players in drug-induced side effects. As irAOPs are still in their infancy, there is a need for a model irAOP to validate the suitability of this tool. For this purpose, we developed a hepatotoxicity-based model irAOP for recombinant human IL-2 (aldesleukin). Besides producing durable therapeutic responses against renal cell carcinoma and metastatic melanoma, the boosted immune activation upon IL-2 treatment elicits liver damage. The availability of extensive data regarding IL-2 allows both the generation of a comprehensive putative irAOP and to validate the predictability of the irAOP with clinical data. Moreover, IL-2, as one of the first cancer immunotherapeutics on the market, is a blueprint for various biological and novel treatment regimens that are under investigation today. This review provides a guideline for further irAOP-directed research in immune-mediated hepatotoxicity.
Keyphrases
  • drug induced
  • liver injury
  • adverse drug
  • renal cell carcinoma
  • oxidative stress
  • recombinant human
  • healthcare
  • electronic health record
  • squamous cell carcinoma
  • stem cells
  • young adults
  • deep learning