Drug-induced liver injury (DILI) is a frequent and dangerous adverse effect faced during preclinical and clinical drug therapy. DILI is a leading cause of candidate drug attrition, withdrawal and in clinic, is the primary cause of acute liver failure. Traditional diagnostic markers for DILI include alanine aminotransferase (ALT), aspartate aminotransferase (AST) and alkaline phosphatase (ALP). Yet, these routinely used diagnostic markers have several noteworthy limitations, restricting their sensitivity, specificity and accuracy in diagnosing DILI. Consequently, new biomarkers for DILI need to be identified.A potential biomarker for DILI is cytokeratin-18 (CK18), an intermediate filament protein highly abundant in hepatocytes and cholangiocytes. Extensively researched in a variety of clinical settings, both full length and cleaved forms of CK18 can diagnose early-stage DILI and provide insight into the mechanism of hepatocellular injury compared to traditionally used diagnostic markers. However, relatively little research has been conducted on CK18 in preclinical models of DILI. In particular, CK18 and its relationship with DILI is yet to be characterised in an in vivo rat model. Such characterization of CK18 and ccCK18 responses may enable their use as translational biomarkers for hepatotoxicity and facilitate management of clinical DILI risk in drug development. The aim of this review is to discuss the application of CK18 as a biomarker for DILI. Specifically, this review will highlight the properties of CK18, summarise clinical research that utilised CK18 to diagnose DILI and examine the current challenges preventing the characterisation of CK18 in an in vivo rat model of DILI.

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Purpose Adverse effects following fluoropyrimidine-based chemotherapy regimens are common. However, there are no current accepted diagnostic markers for prediction prior to treatment, and the underlying mechanisms remain unclear. This study aimed to determine genetic and non-genetic predictors of adverse effects. Methods Genomic DNA was analyzed for 25 single nucleotide polymorphisms (SNPs). Demographics, comorbidities, cancer and fluoropyrimidine-based chemotherapy regimen types, and adverse effect data were obtained from clinical records for 155 Australian White participants. Associations were determined by bivariate analysis, logistic regression modeling and Bayesian network analysis. Results Twelve different adverse effects were observed in the participants, the most common severe adverse effect was diarrhea (12.9%). Bivariate analysis revealed associations between all adverse effects except neutropenia, between genetic and non-genetic predictors, and between 8 genetic and 12 non-genetic predictors with more than 1 adverse effect. Logistic regression modeling of adverse effects revealed a greater/sole role for six genetic predictors in overall gastrointestinal toxicity, nausea and/or vomiting, constipation, and neutropenia, and for nine non-genetic predictors in diarrhea, mucositis, neuropathy, generalized pain, hand–foot syndrome, skin toxicity, cardiotoxicity and fatigue. The Bayesian network analysis revealed less directly associated predictors (one genetic and six non-genetic) with adverse effects and confirmed associations between six adverse effects, eight genetic predictors and nine non-genetic predictors. Conclusion This study is the first to link both genetic and non-genetic predictors with adverse effects following fluoropyrimidine-based chemotherapy. Collectively, we report a wealth of information that warrants further investigation to elucidate the clinical significance, especially associations with genetic predictors and adverse effects.

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