Exploring Individual Variability in Drug-Induced Liver Injury (DILI) Responses through Metabolomic Analysis.

Drug-induced liver injury (DILI) is a serious adverse hepatic event presenting diagnostic and prognostic challenges. The clinical categorization of DILI into hepatocellular, cholestatic, or mixed phenotype is based on serum alanine aminotransferase (ALT) and alkaline phosphatase (ALP) values; however, this classification may not capture the full spectrum of DILI subtypes. With this aim, we explored the utility of assessing changes in the plasma metabolomic profiles of 79 DILI patients assessed by the RUCAM (Roussel Uclaf Causality Assessment Method) score to better characterize this condition and compare results obtained with the standard clinical characterization. Through the identification of various metabolites in the plasma (including free and conjugated bile acids and glycerophospholipids), and the integration of this information into predictive models, we were able to evaluate the extent of the hepatocellular or cholestatic phenotype and to assign a numeric value with the contribution of each specific DILI sub-phenotype into the patient's general condition. Additionally, our results showed that metabolomic analysis enabled the monitoring of DILI variability responses to the same drug, the transitions between sub-phenotypes during disease progression, and identified a spectrum of residual DILI metabolic features, which can be overlooked using standard clinical diagnosis during patient follow-up.

Drug-Induced Fatty Liver Disease (DIFLD): A Comprehensive Analysis of Clinical, Biochemical, and Histopathological Data for Mechanisms Identification and Consistency with Current Adverse Outcome Pathways.

Drug induced fatty liver disease (DIFLD) is a form of drug-induced liver injury (DILI), which can also be included in the more general metabolic dysfunction-associated steatotic liver disease (MASLD), which specifically refers to the accumulation of fat in the liver unrelated to alcohol intake. A bi-directional relationship between DILI and MASLD is likely to exist: while certain drugs can cause MASLD by acting as pro-steatogenic factors, MASLD may make hepatocytes more vulnerable to drugs. Having a pre-existing MASLD significantly heightens the likelihood of experiencing DILI from certain medications. Thus, the prevalence of steatosis within DILI may be biased by pre-existing MASLD, and it can be concluded that the genuine true incidence of DIFLD in the general population remains unknown. In certain individuals, drug-induced steatosis is often accompanied by concomitant injury mechanisms such as oxidative stress, cell death, and inflammation, which leads to the development of drug-induced steatohepatitis (DISH). DISH is much more severe from the clinical point of view, has worse prognosis and outcome, and resembles MASH (metabolic-associated steatohepatitis), as it is associated with inflammation and sometimes with fibrosis. A literature review of clinical case reports allowed us to examine and evaluate the clinical features of DIFLD and their association with specific drugs, enabling us to propose a classification of DIFLD drugs based on clinical outcomes and pathological severity: Group 1, drugs with low intrinsic toxicity (e.g., ibuprofen, naproxen, acetaminophen, irinotecan, methotrexate, and tamoxifen), but expected to promote/aggravate steatosis in patients with pre-existing MASLD; Group 2, drugs associated with steatosis and only occasionally with steatohepatitis (e.g., amiodarone, valproic acid, and tetracycline); and Group 3, drugs with a great tendency to transit to steatohepatitis and further to fibrosis. Different mechanisms may be in play when identifying drug mode of action: (1) inhibition of mitochondrial fatty acid ß-oxidation; (2) inhibition of fatty acid transport across mitochondrial membranes; (3) increased de novo lipid synthesis; (4) reduction in lipid export by the inhibition of microsomal triglyceride transfer protein; (5) induction of mitochondrial permeability transition pore opening; (6) dissipation of the mitochondrial transmembrane potential; (7) impairment of the mitochondrial respiratory chain/oxidative phosphorylation; (8) mitochondrial DNA damage, degradation and depletion; and (9) nuclear receptors (NRs)/transcriptomic alterations. Currently, the majority of, if not all, adverse outcome pathways (AOPs) for steatosis in AOP-Wiki highlight the interaction with NRs or transcription factors as the key molecular initiating event (MIE). This perspective suggests that chemical-induced steatosis typically results from the interplay between a chemical and a NR or transcription factors, implying that this interaction represents the primary and pivotal MIE. However, upon conducting this exhaustive literature review, it became evident that the current AOPs tend to overly emphasize this interaction as the sole MIE. Some studies indeed support the involvement of NRs in steatosis, but others demonstrate that such NR interactions alone do not necessarily lead to steatosis. This view, ignoring other mitochondrial-related injury mechanisms, falls short in encapsulating the intricate biological mechanisms involved in chemically induced liver steatosis, necessitating their consideration as part of the AOP's map road as well.

Idiosyncratic Drug-Induced Liver Injury and Amoxicillin-Clavulanate: Spotlight on Gut Microbiota, Fecal Metabolome and Bile Acid Profile in Patients.

Several hepatic disorders are influenced by gut microbiota, but its role in idiosyncratic drug-induced liver injury (iDILI), whose main causative agent is amoxicillin-clavulanate, remains unknown. This pioneering study aims to unravel particular patterns of gut microbiota composition and associated metabolites in iDILI and iDILI patients by amoxicillin-clavulanate (iDILI-AC). Thus, serum and fecal samples from 46 patients were divided into three study groups: healthy controls (n = 10), non-iDILI acute hepatitis (n = 12) and iDILI patients (n = 24). To evaluate the amoxicillin-clavulanate effect, iDILI patients were separated into two subgroups: iDILI non-caused by amoxicillin-clavulanate (iDILI-nonAC) (n = 18) and iDILI-AC patients (n = 6). Gut microbiota composition and fecal metabolome plus serum and fecal bile acid (BA) analyses were performed, along with correlation analyses. iDILI patients presented a particular microbiome profile associated with reduced fecal secondary BAs and fecal metabolites linked to lower inflammation, such as dodecanedioic acid and pyridoxamine. Moreover, certain taxa like Barnesiella, Clostridia UCG-014 and Eubacterium spp. correlated with significant metabolites and BAs. Additionally, comparisons between iDILI-nonAC and iDILI-AC groups unraveled unique features associated with iDILI when caused by amoxicillin-clavulanate. In conclusion, specific gut microbiota profiles in iDILI and iDILI-AC patients were associated with particular metabolic and BA status, which could affect disease onset and progression.

Novel clinical phenotypes, drug categorization, and outcome prediction in drug-induced cholestasis: Analysis of a database of 432 patients developed by literature review and machine learning support.

BACKGROUND: Serum transaminases, alkaline phosphatase and bilirubin are common parameters used for DILI diagnosis, classification, and prognosis. However, the relevance of clinical examination, histopathology and drug chemical properties have not been fully investigated. As cholestasis is a frequent and complex DILI manifestation, our goal was to investigate the relevance of clinical features and drug properties to stratify drug-induced cholestasis (DIC) patients, and to develop a prognosis model to identify patients at risk and high-concern drugs. METHODS: DIC-related articles were searched by keywords and Boolean operators in seven databases. Relevant articles were uploaded onto Sysrev, a machine-learning based platform for article review and data extraction. Demographic, clinical, biochemical, and liver histopathological data were collected. Drug properties were obtained from databases or QSAR modelling. Statistical analyses and logistic regressions were performed. RESULTS: Data from 432 DIC patients associated with 52 drugs were collected. Fibrosis strongly associated with fatality, whereas canalicular paucity and ALP associated with chronicity. Drugs causing cholestasis clustered in three major groups. The pure cholestatic pattern divided into two subphenotypes with differences in prognosis, canalicular paucity, fibrosis, ALP and bilirubin. A predictive model of DIC outcome based on non-invasive parameters and drug properties was developed. Results demonstrate that physicochemical (pKa-a) and pharmacokinetic (bioavailability, CYP2C9) attributes impinged on the DIC phenotype and allowed the identification of high-concern drugs. CONCLUSIONS: We identified novel associations among DIC manifestations and disclosed novel DIC subphenotypes with specific clinical and chemical traits. The developed predictive DIC outcome model could facilitate DIC prognosis in clinical practice and drug categorization.

A quantitative weight-of-evidence method for confidence assessment of adverse outcome pathway networks: A case study on chemical-induced liver steatosis.

The field of chemical toxicity testing is undergoing a transition to overcome the limitations of in vivo experiments. This evolution involves implementing innovative non-animal approaches to improve predictability and provide a more precise understanding of toxicity mechanisms. Adverse outcome pathway (AOP) networks are pivotal in organizing existing mechanistic knowledge related to toxicological processes. However, these AOP networks are dynamic and require regular updates to incorporate the latest data. Regulatory challenges also persist due to concerns about the reliability of the information they offer. This study introduces a generic Weight-of-Evidence (WoE) scoring method, aligned with the tailored Bradford-Hill criteria, to quantitatively assess the confidence levels in key event relationships (KERs) within AOP networks. We use the previously published AOP network on chemical-induced liver steatosis, a prevalent form of human liver injury, as a case study. Initially, the existing AOP network is optimized with the latest scientific information extracted from PubMed using the free SysRev platform for artificial intelligence (AI)-based abstract inclusion and standardized data collection. The resulting optimized AOP network, constructed using Cytoscape, visually represents confidence levels through node size (key event, KE) and edge thickness (KERs). Additionally, a Shiny application is developed to facilitate user interaction with the dataset, promoting future updates. Our analysis of 173 research papers yielded 100 unique KEs and 221 KERs among which 72 KEs and 170 KERs, respectively, have not been previously documented in the prior AOP network or AOP-wiki. Notably, modifications in de novo lipogenesis, fatty acid uptake and mitochondrial beta-oxidation, leading to lipid accumulation and liver steatosis, garnered the highest KER confidence scores. In conclusion, our study delivers a generic methodology for developing and assessing AOP networks. The quantitative WoE scoring method facilitates in determining the level of support for KERs within the optimized AOP network, offering valuable insights into its utility in both scientific research and regulatory contexts. KERs supported by robust evidence represent promising candidates for inclusion in an in vitro test battery for reliably predicting chemical-induced liver steatosis within regulatory frameworks.

Anti-miR-873-5p improves alcohol-related liver disease by enhancing hepatic deacetylation via SIRT1.

Background & Aims: Current therapies for the treatment of alcohol-related liver disease (ALD) have proven largely ineffective. Patients relapse and the disease progresses even after liver transplantation. Altered epigenetic mechanisms are characteristic of alcohol metabolism given excessive acetate and NAD depletion and play an important role in liver injury. In this regard, novel therapeutic approaches based on epigenetic modulators are increasingly proposed. MicroRNAs, epigenetic modulators acting at the post-transcriptional level, appear to be promising new targets for the treatment of ALD. Methods: MiR-873-5p levels were measured in 23 liver tissue from Patients with ALD, and GNMT levels during ALD were confirmed using expression databases (transcriptome n = 62, proteome n = 68). High-resolution proteomics and metabolomics in mice following the Gao-binge model were used to investigate miR-873-5p expression in ALD. Hepatocytes exposed to 50 mM alcohol for 12 h were used to study toxicity. The effect of anti-miR-873-5p in the treatment outcomes of ALD was investigated. Results: The analysis of human and preclinical ALD samples revealed increased expression of miR-873-5p in the liver. Interestingly, there was an inverse correlation with NNMT, suggesting a novel mechanism for NAD depletion and aberrant acetylation during ALD progression. High-resolution proteomics and metabolomics identified miR-873-5p as a key regulator of NAD metabolism and SIRT1 deacetylase activity. Anti-miR-873-5p reduced NNMT activity, fuelled the NAD salvage pathway, restored the acetylome, and modulated the levels of NF-κB and FXR, two known SIRT1 substrates, thereby protecting the liver from apoptotic and inflammatory processes, and improving bile acid homeostasis. Conclusions: These data indicate that targeting miR-873-5p, a repressor of GNMT previously associated with NAFLD and acetaminophen-induced liver failure. is a novel and attractive approach to treating alcohol-induced hepatoxicity. Impact and implications: The role of miR-873-5p has not been explicitly examined in the progression of ALD, a pathology with no therapeutic options. In this study, inhibiting miR-873-5p exerted hepatoprotective effects against ALD through rescued SIRT1 activity and consequently restored bile acid homeostasis and attenuated the inflammatory response. Targeting hepatic miR-873-5p may represent a novel therapeutic approach for the treatment of ALD.