| Abstrakt: |
Drugs are prescribed worldwide to treat diseases but with the risk of idiosyncratic drug-induced liver injury (iDILI). The most important difficulty is how best to establish causality. Based on strong evidence and principles of artificial intelligence (AI) to solve complex processes through quantitative algorithms using scored elements, progress was achieved with the Roussel Uclaf Causality Assessment Method (RUCAM) in its original and updated versions, often viewed now as the gold standard. As a highly appreciated diagnostic algorithm, the RUCAM is in global use with around 100,000 iDILI cases published worldwide using RUCAM to assess causality, largely outperforming any other specific causality assessment tool in terms of case numbers. Consequently, the RUCAM helps to establish a list of top-ranking drugs worldwide implicated in iDILI and to describe clinical and mechanistic features of iDILI caused by various drugs. In addition, the RUCAM was recently applied in iDILI cases of patients treated for coronavirus disease 2019 (COVID-19) infections or cancer patients treated with immune checkpoint inhibitors (ICIs), as well as in the search for new treatment options with conventional drugs in iDILI. Analyses of RUCAM-based iDILI cases are helpful to support pathogenetic steps like immune reactions, genetic predisposition as evidenced by human leucocyte antigens (HLA) genotypes for selected drugs, and the role of the gut microbiome. To achieve consistency in data collection, analysis, and specific clinical and pathogenetic presentation, researchers, regulatory agencies, and pharmaceutical firms should place iDILI and the updated RUCAM as the causality tool under one and the same hat in review articles and clinical guidelines for the diagnosis and treatment of iDILI. [ABSTRACT FROM AUTHOR] |
