Plasma Lipid Profiling of Three Types of Drug-Induced Liver Injury in Japanese Patients: A Preliminary Study

Advances in drug-induced liver injury research: in vitro models, mechanisms, omics and gene modulation techniques

Drug-induced liver injury (DILI) refers to drug-mediated damage to the structure and function of the liver, ranging from mild elevation of liver enzymes to severe hepatic insufficiency, and in some cases, progressing to liver failure. The mechanisms and clinical symptoms of DILI are diverse due to the varying combination of drugs, making clinical treatment and prevention complex. DILI has significant public health implications and is the primary reason for post-marketing drug withdrawals. The search for reliable preclinical models and validated biomarkers to predict and investigate DILI can contribute to a more comprehensive understanding of adverse effects and drug safety. In this review, we examine the progress of research on DILI, enumerate in vitro models with potential benefits, and highlight cellular molecular perturbations that may serve as biomarkers. Additionally, we discuss omics approaches frequently used to gather comprehensive datasets on molecular events in response to drug exposure. Finally, three commonly used gene modulation techniques are described, highlighting their application in identifying causal relationships in DILI. Altogether, this review provides a thorough overview of ongoing work and approaches in the field of DILI.

Nutritional management for acute liver failure

Acute liver failure (ALF) induces increased energy expenditure and disrupts the metabolism of essential nutrients. Hepatic encephalopathy is a complication of ALF with a poor prognosis and mainly involves the metabolic disturbance of amino acids in its pathogenesis. In this review, we discuss the nutritional management for ALF in consideration of the pathophysiology of ALF with respect to the impairment of hepatocyte function. It is known that enteral nutrition is recommended for patients with ALF, while parenteral nutrition is recommended for patients who cannot tolerate enteral nutrition. As ALF leads to a hypermetabolic state, the energy intake is recommended to cover 1.3 times the resting energy expenditure. Because of the high risk of hypoglycemia associated with disturbances in glucose metabolism, substantial glucose intake is recommended. Along with the deterioration of glucose metabolism, protein metabolism is also disrupted. As patients with ALF have increased systemic protein catabolism together with decreased protein synthesis, appropriate amounts of amino acids or protein under monitoring serum ammonia levels are recommended. In conclusion, nutritional management based on the understanding of nutritional pathophysiology is a pivotal therapeutic approach for patients with ALF. The approach should be individualized in the acute phase, the recovery phase, and the pretransplant phase.

Unique metabolomics characteristics for distinguishing cirrhosis related to different liver diseases: A systematic review and meta-analysis

BACKGROUND AND AIM

Clinical evidence for early identification and diagnosis of liver cirrhosis (LC) caused by different types of liver disease is limited. We investigated this topic through a meta-analysis of quantitative metabolomics.

METHODS

Four databases were searched until October 31, 2022 for studies comparing metabolite levels between patients with different types of liver disease and control individuals. A random-effects model was applied for the meta-analysis.

RESULTS

This study included 55 studies with 8266 clinical participants, covering 348 metabolites. In LC related to drug-induced liver injury (DILI), hepatitis B virus (HBV) infection, and non-alcoholic fatty liver disease (NAFLD), the primary bile acid biosynthesis (taurocholic acid: SMD, 1.08[0.81, 1.35]; P < 0.00001; glycocholic acid: SMD, 1.35[1.07, 1.62]; P < 0.00001; taurochenodeoxycholic acid: SMD, 1.36[0.94, 1.78]; P < 0.00001; glycochenodeoxycholic acid: SMD, 1.49[0.93, 2.06]; P < 0.00001), proline and arginine (l-proline: SMD, 1.06[0.53, 1.58]; P < 0.0001; hydroxyproline: SMD, 0.81[0.30, 1.33]; P = 0.002), and fatty acid biosynthesis (palmitic acid: SMD, 0.44[0.21, 0.67]; P = 0.0002; oleic acid: SMD, 0.46[0.19, 0.73]; P = 0.0008; stearic acid: SMD, 0.37[0.07, 0.68]; P = 0.02) metabolic pathways were significantly altered.

CONCLUSION

We identified key biomarkers and metabolic characteristics for distinguishing and identifying LC related to different types of liver disease, providing a new perspective for early diagnosis, disease monitoring, and precise treatment.

Plasma metabolomic and lipidomic alterations associated with anti-tuberculosis drug-induced liver injury

Background: Anti-tuberculosis drug-induced liver injury (ATB-DILI) is an adverse reaction with a high incidence and the greatest impact on tuberculosis treatment. However, there is a lack of effective biomarkers for the early prediction of ATB-DILI. Herein, this study uses UPLC‒MS/MS to reveal the plasma metabolic profile and lipid profile of ATB-DILI patients before drug administration and screen new biomarkers for predicting ATB-DILI. Methods: A total of 60 TB patients were enrolled, and plasma was collected before antituberculosis drug administration. The untargeted metabolomics and lipidomics analyses were performed using UPLC‒MS/MS, and the high-resolution mass spectrometer Q Exactive was used for data acquisition in both positive and negative ion modes. The random forest package of R software was used for data screening and model building. Results: A total of 60 TB patients, including 30 ATB-DILI patients and 30 non-ATB-DILI subjects, were enrolled. There were no significant differences between the ATB-DILI and control groups in age, sex, smoking, drinking or body mass index (p > 0.05). Twenty-two differential metabolites were selected. According to KEGG pathway analysis, 9 significantly enriched metabolic pathways were found, and both drug metabolism-other enzymes and niacin and nicotinamide metabolic pathways were found in both positive and negative ion models. A total of 7 differential lipid molecules were identified between the two groups. Ferroptosis and biosynthesis of unsaturated fatty acids were involved in the occurrence of ATB-DILI. Random forest analysis showed that the model built with the top 30 important variables had an area under the ROC curve of 0.79 (0.65-0.93) for the training set and 0.79 (0.55-1.00) for the validation set. Conclusion: This study demonstrated that potential markers for the early prediction of ATB-DILI can be found through plasma metabolomics and lipidomics. The random forest model showed good clinical predictive value for ATB-DILI.

A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies

BACKGROUND

Multilocus analysis on a set of single nucleotide polymorphisms (SNPs) pre-assigned within a gene constitutes a valuable complement to single-marker analysis by aggregating data on complex traits in a biologically meaningful way. However, despite the existence of a wide variety of SNP-set methods, few comprehensive comparison studies have been previously performed to evaluate the effectiveness of these methods.

RESULTS

We herein sought to fill this knowledge gap by conducting a comprehensive empirical comparison for 22 commonly-used summary-statistics based SNP-set methods. We showed that only seven methods could effectively control the type I error, and that these well-calibrated approaches had varying power performance under the simulation scenarios. Overall, we confirmed that the burden test was generally underpowered and score-based variance component tests (e.g., sequence kernel association test) were much powerful under the polygenic genetic architecture in both common and rare variant association analyses. We further revealed that two linkage-disequilibrium-free P value combination methods (e.g., harmonic mean P value method and aggregated Cauchy association test) behaved very well under the sparse genetic architecture in simulations and real-data applications to common and rare variant association analyses as well as in expression quantitative trait loci weighted integrative analysis. We also assessed the scalability of these approaches by recording computational time and found that all these methods can be scalable to biobank-scale data although some might be relatively slow.

CONCLUSION

In conclusion, we hope that our findings can offer an important guidance on how to choose appropriate multilocus association analysis methods in post-GWAS era. All the SNP-set methods are implemented in the R package called MCA, which is freely available at https://github.com/biostatpzeng/ .

The Potential Role of Metabolomics in Drug-Induced Liver Injury (DILI) Assessment

Drug-induced liver injury (DILI) is one of the most frequent adverse clinical reactions and a relevant cause of morbidity and mortality. Hepatotoxicity is among the major reasons for drug withdrawal during post-market and late development stages, representing a major concern to the pharmaceutical industry. The current biochemical parameters for the detection of DILI are based on enzymes (alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transpeptidase (GGT), alkaline phosphatase (ALP)) and bilirubin serum levels that are not specific of DILI and therefore there is an increasing interest on novel, specific, DILI biomarkers discovery. Metabolomics has emerged as a tool with a great potential for biomarker discovery, especially in disease diagnosis, and assessment of drug toxicity or efficacy. This review summarizes the multistep approaches in DILI biomarker research and discovery based on metabolomics and the principal outcomes from the research performed in this field. For that purpose, we have reviewed the recent scientific literature from PubMed, Web of Science, EMBASE, and PubTator using the terms “metabolomics”, “DILI”, and “humans”. Despite the undoubted contribution of metabolomics to our understanding of the underlying mechanisms of DILI and the identification of promising novel metabolite biomarkers, there are still some inconsistencies and limitations that hinder the translation of these research findings into general clinical practice, probably due to the variability of the methods used as well to the different mechanisms elicited by the DILI causing agent.

Metabolomic Profiling for Histologically Fibrotic Stage in Chronic Drug-Induced Liver Injury

Background and aims: Chronic drug-induced liver injury (DILI) is a rare but under-researched adverse drug reaction-related disease, which is highly likely to progress into liver fibrosis and even cirrhosis. In this study, metabolomics was used to screen out characteristic metabolites related to the histological progression of fibrosis in chronic DILI and analyze the metabolic changes during the development of fibrosis to explain the underlying mechanism. Methods: Chronic DILI patients who underwent liver biopsy were divided into different fibrosis grades. Serum was analyzed by untargeted metabolomics to find serological characteristic metabolite fingerprints. The screened fingerprints were validated by the validation group patients, and the identification ability of fingerprints was compared using FibroScan. Results: A total of 31 metabolites associated with fibrosis and 11 metabolites associated with advanced fibrosis were identified. The validation group confirmed the accuracy of the two metabolite fingerprints [area under the curve (AUC) value 0.753 and 0.944]. In addition, the fingerprints showed the ability to distinguish the grades of fibrosis by comparing using FibroScan. The metabolite fingerprint pathway showed that bile acid synthesis is disturbed while lipid metabolism is extremely active, resulting in an overload of lipid metabolites in the occurrence and development of chronic DILI-associated fibrosis. Conclusions: Our metabolomic analysis reveals the unique metabolomic fingerprints associated with chronic DILI fibrosis, which have potential clinical diagnostic and prognostic significances. The metabolomic fingerprints suggest the disturbance of the lipid metabolites as the most important factor in the development of DILI fibrosis.

Combinatory Effects of Cerium Dioxide Nanoparticles and Acetaminophen on the Liver-A Case Study of Low-Dose Interactions in Human HuH-7 Cells

The interactions between pharmaceuticals and nanomaterials and its potentially resulting toxicological effects in living systems are only insufficiently investigated. In this study, two model compounds, acetaminophen, a pharmaceutical, and cerium dioxide, a manufactured nanomaterial, were investigated in combination and individually. Upon inhalation, cerium dioxide nanomaterials were shown to systemically translocate into other organs, such as the liver. Therefore we picked the human liver cell line HuH-7 cells as an in vitro system to investigate liver toxicity. Possible synergistic or antagonistic metabolic changes after co-exposure scenarios were investigated. Toxicological data of the water soluble tetrazolium (WST-1) assay for cell proliferation and genotoxicity assessment using the Comet assay were combined with an untargeted as well as a targeted lipidomics approach. We found an attenuated cytotoxicity and an altered metabolic profile in co-exposure experiments with cerium dioxide, indicating an interaction of both compounds at these endpoints. Single exposure against cerium dioxide showed a genotoxic effect in the Comet assay. Conversely, acetaminophen exhibited no genotoxic effect. Comet assay data do not indicate an enhancement of genotoxicity after co-exposure. The results obtained in this study highlight the advantage of investigating co-exposure scenarios, especially for bioactive substances.

Serum Metabolomic Analysis of Chronic Drug-Induced Liver Injury With or Without Cirrhosis

Background: Chronic drug-induced liver injury (DILI) occurs in up to 20% of all DILI patients. It presents a chronic pattern with persistent or relapsed episodes and may even progress to cirrhosis. However, its underlying development mechanism is poorly understood. Aims: To find serum metabolite signatures of chronic DILI with or without cirrhosis, and to elucidate the underlying mechanism. Methods: Untargeted metabolomics coupled with pattern recognition approaches were used to profile and extract metabolite signatures from 83 chronic DILI patients, including 58 non-cirrhosis (NC) cases, 14 compensated cirrhosis (CC) cases, and 11 decompensated cirrhosis (DC) cases. Results: Of the 269 annotated metabolites associated with chronic DILI, metabolic fingerprints associated with cirrhosis (including 30 metabolites) and decompensation (including 25 metabolites), were identified. There was a significantly positive correlation between cirrhosis-associated fingerprint (eigenmetabolite) and the aspartate aminotransferase-to-platelet ratio index (APRI) (r = 0.315, P = 0.003). The efficacy of cirrhosis-associated eigenmetabolite coupled with APRI to identify cirrhosis from non-cirrhosis patients was significantly better than APRI alone [area under the curve (AUC) value 0.914 vs. 0.573]. The decompensation-associated fingerprint (eigenmetabolite) can effectively identify the compensation and decompensation periods (AUC value 0.954). The results of the metabolic fingerprint pathway analysis suggest that the blocked tricarboxylic acid cycle (TCA cycle) and intermediary metabolism, excessive accumulation of bile acids, and perturbed amino acid metabolism are potential mechanisms in the occurrence and development of chronic DILI-associated cirrhosis. Conclusions: The metabolomic fingerprints characterize different stages of chronic DILI progression and deepen the understanding of the metabolic reprogramming mechanism of chronic DILI progression to cirrhosis.

Application of comprehensive lipidomics to biomarker research on adverse drug reactions

Lipidomics is a relatively new field of omics that focuses on lipids, one of the major categories of metabolites. Owing to their various functions, lipids are considered suitable targets for biomarker development; in addition, lipidomics analysis of adverse drug reactions (ADRs) has been conducted recently. In this review, I have summarized information on comprehensive lipidomics, which involves the analysis of global lipids in a non-targeted manner. Mass spectrometry-based platforms are currently the dominant lipidomics platform owing to their versatile features. I have also summarized the application of lipidomics in biomarker research on ADRs caused by therapeutic drugs in humans and rodents. Additionally, general concerns in and emerging approaches of lipidomics research on ADR have been highlighted. Although biomarkers identified using the lipidomics analysis of ADRs have not been qualified, reported candidates will be evaluated for clinical application. In addition, novel biomarker candidates will be developed via classical and new approaches exemplified in this review.