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Aims: Detection and characterization of idiosyncratic drug‐induced liver injury (DILI) currently rely on standard liver tests, which are suboptimal in terms of specificity, sensitivity and prognosis. Therefore, DILI diagnosis can be delayed, with important consequences for the patient. In this study, we aimed to evaluate the potential of osteopontin, cytokeratin‐18 (caspase‐cleaved: ccK18 and total: K18), α‐glutathione‐S‐transferase and microRNA‐122 as new DILI biomarkers. Methods: Serial blood samples were collected from 32 DILI and 34 non‐DILI acute liver injury (ALI) cases and a single sample from 43 population controls without liver injury (HLC) and analysed using enzyme‐linked immunosorbent assay (ELISA) or single‐molecule arrays. Results: All biomarkers differentiated DILI and ALI from HLC with an area under receiver operator characteristic curve (AUC) value of >0.75 but were less efficient in distinguishing DILI from ALI, with ccK18 (0.79) and K18 (0.76) demonstrating highest potential. However, the AUC improved considerably (0.98) for ccK18 when comparing DILI and a subgroup of autoimmune hepatitis cases. Cytokeratin‐18, microRNA‐122 and α‐glutathione‐S‐transferase correlated well with traditional transaminases, while osteopontin correlated most strongly with the international normalized ratio (INR). Conclusions: ccK18 appears promising in distinguishing DILI from autoimmune hepatitis but less so from other forms of acute liver injury. Osteopontin demonstrates prognostic potential with higher levels detected in more severe cases regardless of aetiology. [ABSTRACT FROM AUTHOR]

Background & Aims: Most patients with drug‐induced liver injury (DILI) manifest clinical symptoms while on therapy, while some patients manifest days or weeks after drug cessation (delayed onset). This challenges DILI causality assessment and diagnosis. Factors contributing to the delayed onset phenotype are currently unknown. We explored factors contributing to delayed onset of DILI by analysing culprit drug properties, host factors and their interactions in a large patient population from the Spanish DILI Registry. Methods: Clinical information from 388 patients (69 presented delayed onset) and drug properties of 43 causative drugs (45 active ingredients) were analysed. A two‐tier regression‐based model was used to assess host/drug interactions affecting the probability of delayed onset. Results: Antibacterial and anti‐inflammatory drugs accounted for the delayed onset cases. Drug property of <50% hepatic metabolism (odds ratio [OR] 11.06, 95% confidence interval [95% CI]: 4.4‐32.2, P = 0.0003), daily dose ≥1000 mg (OR: 2.77, 95% CI: 1.3‐6.1, P = 0.0063) and the absence of pre‐existing conditions in a patient (OR: 2.55, 95% CI: 1.3‐4.9, P = 0.0043) were independently associated with delayed onset. The findings were consistent when externally validated using Latin American DILI Network cases (N = 131). Likewise, drug properties of mitochondrial liability and Pauling electronegativity were associated with delayed onset, but dependent on specific host factors such as age, sex and pre‐existing cardiac diseases. Conclusions: This study demonstrated that delayed onset, a specific DILI phenotype, is explained by complex interactions among drug properties and host factors and provided mechanistic hypotheses for future studies. These findings can help improve the diagnostic capability and causality assessment. [ABSTRACT FROM AUTHOR]

Despite being relatively rare, drug-induced liver injury (DILI) is a serious condition, both for the individual patient due to the risk of acute liver failure, and for the drug development industry and regulatory agencies due to associations with drug development attritions, black box warnings, and postmarketing withdrawals. A major limitation in DILI diagnosis and prediction is the current lack of specific biomarkers. Despite refined usage of traditional liver biomarkers in DILI, reliable disease outcome predictions are still difficult to make. These limitations have driven the growing interest in developing new more sensitive and specific DILI biomarkers, which can improve early DILI prediction, diagnosis, and course of action. Several promising DILI biomarker candidates have been discovered to date, including mechanistic-based biomarker candidates such as glutamate dehydrogenase, high-mobility group box 1 protein and keratin-18, which can also provide information on the injury mechanism of different causative agents. Furthermore, microRNAs have received much attention lately as potential non-invasive DILI biomarker candidates, in particular miR-122. Advances in "omics" technologies offer a new approach for biomarker exploration studies. The ability to screen a large number of molecules (e.g., metabolites, proteins, or DNA) simultaneously enables the identification of 'toxicity signatures,' which may be used to enhance preclinical safety assessments and disease diagnostics. Omics-based studies can also provide information on the underlying mechanisms of distinct forms of DILI that may further facilitate the identification of early diagnostic biomarkers and safer implementation of personalized medicine. In this review, we summarize recent advances in the area of DILI biomarker studies. [ABSTRACT FROM AUTHOR]

Background & Aims Several pharmaceutical compounds have been shown to exert inhibitory effects on the bile salt export pump ( BSEP) encoded by the ABCB11 gene. We analysed the combined effect on drug-induced liver injury ( DILI) development of the ABCB11 1331T>C polymorphism and the presence of specific chemical moieties, with known BSEP inhibiting properties, in the causative drug. Methods Genotyping using a TaqMan 5′ allelic discrimination assay was performed in 188 Spanish DILI patients, 219 healthy controls and 91 sex-, age- and drug-matched controls. A chemical structure analysis was performed for each individual causative drug. Results The CC genotype was significantly associated with hepatocellular damage [odds ratio ( OR) = 2.1, P = 0.001], particularly in NSAID DILI cases ( OR = 3.4, P = 0.007). In addition, the CC genotype was found to be significantly linked to DILI development from drugs causing <50% BSEP inhibition (OR = 1.8, Pc = 0.011). Of the BSEP inhibitory chemical moieties, 59% of the causative drugs contained a carbocyclic system with at least one aromatic ring, corresponding to 61% of the total cases. The C allele was significantly more frequent in DILI cases containing this chemical moiety, which appear to be conditioned on the ABCB11 1331T>C polymorphism in the absence of other BSEP inhibitory structures. Conclusion Patients carrying the C allele in the ABCB11 1331T>C polymorphism are at increased risk of developing hepatocellular type of DILI, when taking drugs containing a carbocyclic system with aromatic rings. [ABSTRACT FROM AUTHOR]

Summary: Diagnosis of drug-induced liver injury (DILI) remains a challenge and eagerly awaits the development of reliable hepatotoxicity biomarkers. Several methods have been developed in order to facilitate hepatotoxicity causality assessments. These methods can be divided into three categories: (1) expert judgement, (2) probabilistic approaches, and (3) algorithms or scales. The last category is further divided into general and liver-specific scales. The Council for International Organizations of Medical Sciences (CIOMS) scale, also referred to as the Roussel Uclaf Causality Assessment Method (RUCAM), although cumbersome and difficult to apply by physicians not acquainted with DILI, is used by many expert hepatologists, researchers, and regulatory authorities to assess the probability of suspected causal agents. However, several limitations of this scale have been brought to light, indicating that a number of adjustments are needed. This review is a detailed timely criticism to alert the readers of the limitations and give insight into what would be needed to improve the scale. Instructions on how to approach DILI diagnosis in practice are provided, using CIOMS as an aid to emphasize the topics to be addressed when assessing DILI cases. Amendments of the CIOMS scale in the form of applying authoritative evidence-based criteria, a simplified scoring system and appropriate weighting given to individual parameters based on statistical evaluations with large databases will provide wider applicability in the clinical setting. [ABSTRACT FROM AUTHOR]

Background & Aims: Drug-induced liver injury (DILI), especially from antimicrobial agents, is an important cause of serious liver disease. Amoxicillin-clavulanate (AC) is a leading cause of idiosyncratic DILI, but little is understood about genetic susceptibility to this adverse reaction. Methods: We performed a genome-wide association study using 822,927 single nucleotide polymorphism (SNP) markers from 201 White European and US cases of DILI following AC administration (AC-DILI) and 532 population controls, matched for genetic background. Results: AC-DILI was associated with many loci in the major histocompatibility complex. The strongest effect was with an HLA class II SNP (rs9274407, P = 4.8 × 10−14), which correlated with rs3135388, a tag SNP of HLA-DRB1*1501-DQB1*0602 that was previously associated with AC-DILI. Conditioned on rs3135388, rs9274407 is still significant (P = 1.1 × 10−4). An independent association was observed in the class I region (rs2523822, P = 1.8 × 10−10), related to HLA-A*0201. The most significant class I and II SNPs showed statistical interaction (P = .0015). High-resolution HLA genotyping (177 cases and 219 controls) confirmed associations of HLA-A*0201 (P = 2 × 10−6) and HLA-DQB1*0602 (P = 5 × 10−10) and their interaction (P = .005). Additional, population-dependent effects were observed in HLA alleles with nominal significance. In an analysis of autoimmune-related genes, rs2476601 in the gene PTPN22 was associated (P = 1.3 × 10−4). Conclusions: Class I and II HLA genotypes affect susceptibility to AC-DILI, indicating the importance of the adaptive immune response in pathogenesis. The HLA genotypes identified will be useful in studies of the pathogenesis of AC-DILI but have limited utility as predictive or diagnostic biomarkers because of the low positive predictive values. [ABSTRACT FROM AUTHOR]