Gap junction inhibition prevents drug-induced liver toxicity and fulminant hepatic failure

Drug-induced liver injury (DILI) limits the development and application of many therapeutic compounds and presents major challenges to the pharmaceutical industry and clinical medicine. Acetaminophen-containing compounds are among the most frequently prescribed drugs and are also the most common cause of DILI. Here we describe a pharmacological strategy that targets gap junction communication to prevent amplification of fulminant hepatic failure and acetaminophen-induced hepatotoxicity. We demonstrate that connexin 32 (Cx32), a key hepatic gap junction protein, is an essential mediator of DILI by showing that mice deficient in Cx32 are protected against liver damage, acute inflammation and death caused by liver-toxic drugs. We identify a small-molecule inhibitor of Cx32 that protects against liver failure and death in wild-type mice when co-administered with known hepatotoxic drugs. These findings indicate that gap junction inhibition could provide a pharmaceutical strategy to limit DILI and improve drug safety.     

Translating clinical findings into knowledge in drug safety evaluation--drug induced liver injury prediction system (DILIps)

Drug-induced liver injury (DILI) is a significant concern in drug development due to the poor concordance between preclinical and clinical findings of liver toxicity. We hypothesized that the DILI types (hepatotoxic side effects) seen in the clinic can be translated into the development of predictive in silico models for use in the drug discovery phase. We identified 13 hepatotoxic side effects with high accuracy for classifying marketed drugs for their DILI potential. We then developed in silico predictive models for each of these 13 side effects, which were further combined to construct a DILI prediction system (DILIps). The DILIps yielded 60-70% prediction accuracy for three independent validation sets. To enhance the confidence for identification of drugs that cause severe DILI in humans, the "Rule of Three" was developed in DILIps by using a consensus strategy based on 13 models. This gave high positive predictive value (91%) when applied to an external dataset containing 206 drugs from three independent literature datasets. Using the DILIps, we screened all the drugs in DrugBank and investigated their DILI potential in terms of protein targets and therapeutic categories through network modeling. We demonstrated that two therapeutic categories, anti-infectives for systemic use and musculoskeletal system drugs, were enriched for DILI, which is consistent with current knowledge. We also identified protein targets and pathways that are related to drugs that cause DILI by using pathway analysis and co-occurrence text mining. While marketed drugs were the focus of this study, the DILIps has a potential as an evaluation tool to screen and prioritize new drug candidates or chemicals, such as environmental chemicals, to avoid those that might cause liver toxicity. We expect that the methodology can be also applied to other drug safety endpoints, such as renal or cardiovascular toxicity.     

The HepaRG cell line,a superior in vitro model to L-02, HepG2 and hiHeps cell lines for assessing drug-induced liver injury

Drug-induced liver injury (DILI) is a leading cause of discontinuation of new drug approval or withdrawal of marketed medicine based on safety due to organ vulnerability. The aim of this research is to investigate the potential abilities of four different in vitro cell models (L-02, HepG2, HepaRG, and hiHeps cell lines) in assessing marketed drugs labeled with apparently different types of liver injury. A total of 17 drugs with versatile pharmacological profiles were chosen, of which, 14 drugs are recognized as DILI agents and 3 drugs are DILI irrelevant. Preliminary cellular screening assays indicated that the HepaRG cell line had an advantage over other cell lines in predicting drugs associated with DILI in vitro as it had the highest Youden’s index (71.4 %). A multi-parametric screening assay showed that oxidative stress, mitochondrial damage, and disorders of neutral lipid metabolism were changed notably in the HepaRG cell line after DILI-related drugs exposure, accounting for its high sensitivity in comparison with other three cell lines. In addition, aspartate aminotransferase (AST), lactate dehydrogenase (LDH), and malate dehydrogenase (MDH) all correlated with the cytotoxic effects of diclofenac sodium (p?<?0.05), buspirone hydrochloride (p?<?0.01), and danazol (p?<?0.01) in the HepaRG cell line. We conclude that the HepaRG cell line is a superior in vitro cell model to other three cell lines for evaluating drugs with DILI potential.     

A review of the common properties of drugs with idiosyncratic hepatotoxicity and the "multiple determinant hypothesis" for the manifestation of idiosyncratic drug toxicity

Idiosyncratic drug toxicity is generally believed to be a phenomenon that cannot be readily evaluated experimentally. Reasons for this difficulty include the following: 1. It is a rare event (<1/5,000) and therefore impossible to be studied in clinical trials; 2. It is a human-specific event not detectable in experimental animals. To aid the understanding of idiosyncratic toxicity and to develop an experimental strategy for this phenomenon, a hypothesis is proposed. The hypothesis states that the low frequency of idiosyncratic drug toxicity is due to the requirements for the occurrence of multiple critical and discrete events, with the probability for the occurrence of idiosyncratic drug toxicity as a product of the probabilities of each event. The key determinants of these critical events are proposed to be: 1. Chemical properties; 2. exposure; 3. environmental factors; and 4. genetic factors. Based on this hypothesis, idiosyncratic drug toxicity can be evaluated experimentally via studying these key determinants. The chemical properties critical to idiosyncratic drug toxicity are identified via a review of the common properties of drugs that cause idiosyncratic liver toxicity. These properties include: 1. Formation of reactive metabolites. 2. Metabolism by P450 isoforms. 3. Preponderance of P450 inducers, and 4. Occurrence of clinically significant pharmacokinetic interactions with co-administered drugs. Based on this review, it is proposed that these common properties may be useful experimental endpoints for the prediction and therefore avoidance of the selection of drug candidates with idiosyncratic drug toxicity for further development.     

Profiles of Serum Cytokines in Acute Drug-Induced Liver Injury and Their Prognostic Significance

Drug-induced liver injury (DILI) is the most common cause of acute liver failure in the United-States. The aim of the study was to describe serum immune profiles associated with acute DILI, to investigate whether there are profiles associated with clinical features or types of DILI and/or with prognosis, and to assess temporal changes in levels. Twenty-seven immune analytes were measured in the sera of 78 DILI subjects in the Drug-Induced Liver Injury Network (DILIN) and compared with 40 healthy controls. Immune analytes (14 cytokines, 7 chemokines and 6 growth factors) were measured by BioPlex multiplex ELISA at DILI onset and after 6 months. A modeling process utilizing immune principles was used to select a final set of variables among 27 immune analytes and several additional clinical lab values for prediction of early death (within 6 months of DILI onset). Nineteen of the 27 immune analytes were differentially expressed among healthy control, DILI onset and 6-month cohorts. Disparate patterns of immune responses, especially innate and adaptive cellular (mostly TH17) immunity were evident. Low values of four immune analytes (IL-9, IL-17, PDGF-bb and RANTES) and serum albumin are predictive of early death [PPV = 88% (95% CI, 65%-100%), NPV = 97% (95% CI, 93%-100%), accuracy = 96% (95% CI, 92%-100%)].

Conclusions

Acute DILI is associated with robust and varying immune responses. High levels of expression of cytokines associated with innate immunity are associated with a poor prognosis, whereas high levels of expression of adaptive cytokines are associated with good long-term prognosis and eventual recovery. Serum immune analyte profiles at DILI onset appear to be of prognostic, and perhaps, diagnostic significance.     

Prediction models for drug-induced hepatotoxicity by using weighted molecular fingerprints

Background

Drug-induced liver injury (DILI) is a critical issue in drug development because DILI causes failures in clinical trials and the withdrawal of approved drugs from the market. There have been many attempts to predict the risk of DILI based on in vivo and in silico identification of hepatotoxic compounds. In the current study, we propose the in silico prediction model predicting DILI using weighted molecular fingerprints.

Results

In this study, we used 881 bits of molecular fingerprint and used as features describing presence or absence of each substructure of compounds. Then, the Bayesian probability of each substructure was calculated and labeled (positive or negative for DILI), and a weighted fingerprint was determined from the ratio of DILI-positive to DILI-negative probability values. Using weighted fingerprint features, the prediction models were trained and evaluated with the Random Forest (RF) and Support Vector Machine (SVM) algorithms. The constructed models yielded accuracies of 73.8% and 72.6%, AUCs of 0.791 and 0.768 in cross-validation. In independent tests, models achieved accuracies of 60.1% and 61.1% for RF and SVM, respectively. The results validated that weighted features helped increase overall performance of prediction models. The constructed models were further applied to the prediction of natural compounds in herbs to identify DILI potential, and 13,996 unique herbal compounds were predicted as DILI-positive with the SVM model.

Conclusions

The prediction models with weighted features increased the performance compared to non-weighted models. Moreover, we predicted the DILI potential of herbs with the best performed model, and the prediction results suggest that many herbal compounds could have potential to be DILI. We can thus infer that taking natural products without detailed references about the relevant pathways may be dangerous. Considering the frequency of use of compounds in natural herbs and their increased application in drug development, DILI labeling would be very important.

     

Animal models of idiosyncratic drug reactions

Idiosyncratic drug reactions represent a major problem. In most cases the mechanisms of these reactions are unknown, but circumstantial evidence points to the involvement of reactive metabolites and the characteristics of the reactions suggest involvement of the immune system. If progress is to be made in dealing with these adverse reactions it is essential that we have a better understanding of their mechanisms, and it is hard to imagine testing mechanistic hypotheses without good animal models. Unfortunately, idiosyncratic reactions are also idiosyncratic in animals so few good models exist. The best models, in which a rodent develops a clinical syndrome similar to that which occurs in humans, appear to be penicillamine-induced autoimmunity in Brown Norway rats and nevirapine-induced skin rash in rats. Sulfamethoxazole-induced hypersensitivity in dogs and propylthiouracil-induced autoimmunity in cats are also similar to adverse reactions that occur in people, but they have practical limitations. Halothane-induced liver toxicity in guinea pigs and amodiaquine-induced bone marrow and liver toxicity in rats represent models in which there is an immune response and mild, reversible toxicity. It is possible that the development of immune tolerance is what limits the toxicity in these models, and if this is true, interventions that prevent tolerance might lead to good models. Although the history of developing animal models of idiosyncratic drug reactions is mostly one of failure, such models are essential. A better understanding of immune tolerance may greatly facilitate the development of better models; transgenic technology may also provide an important tool.     

Hospital Admissions for Drug-Induced Liver Injury: Clinical Features, Therapy, and Outcomes

We investigated clinical features, therapy, and outcomes of patients hospitalized for drug-induced liver injury (DILI). DILI resolution was defined as liver biochemistry values back to normal or lower than CIOMS laboratory criteria; Chronicity was defined as persistent biochemical abnormality for >6?months after drugs?? withdrawal. Three-hundred cases were reviewed retrospectively; mean age 51 (13?C86) years, and 204 (68?%) were females. It included 267 (89?%) hepatocellular injury, 16 (5.3?%) cholestatic injury, and 17 (5.7?%) mixed injury cases. In hepatocellular injury group, 197 (73.8?%) patients with TBIL?<?10× ULN included 142 (72.1?%) females and 70 (26.2?%) patients with TBIL????10× ULN included 39 (55.7?%) females (P?=?0.012). Of 70 patients (TBIL????10× ULN), 20 were treated with steroid step-down therapy (79?±?26?days) and others with non-steroid therapy. The steroid therapy group showed higher DILI resolution rate (P?=?0.029) and shorter recovery time (P?=?0.012). Notably, 274/300 (91.3?%) patients resolved, 18/300 (6?%) developed chronic liver injury, 7/300 (2.3?%) died, and one patient received liver transplantation. In death group, TBIL, ALB, PT, and PTA revealed more severe abnormality than in recovery group. In 121/300 (40.3?%) patients, use of herbal medicines was the leading cause of liver injury, followed by antibiotics, cardiovascular drugs, and endocrine drugs. We concluded that step-down steroid therapy for DILI improved curative effect, shortened disease course, and was safe.     

Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort

Drug-drug interactions account for up to 30% of adverse drug reactions. Increasing prevalence of electronic health records (EHRs) offers a unique opportunity to build machine learning algorithms to identify drug-drug interactions that drive adverse events. In this study, we investigated hospitalizations’ data to study drug interactions with non-steroidal anti-inflammatory drugs (NSAIDS) that result in drug-induced liver injury (DILI). We propose a logistic regression based machine learning algorithm that unearths several known interactions from an EHR dataset of about 400,000 hospitalization. Our proposed modeling framework is successful in detecting 87.5% of the positive controls, which are defined by drugs known to interact with diclofenac causing an increased risk of DILI, and correctly ranks aggregate risk of DILI for eight commonly prescribed NSAIDs. We found that our modeling framework is particularly successful in inferring associations of drug-drug interactions from relatively small EHR datasets. Furthermore, we have identified a novel and potentially hepatotoxic interaction that might occur during concomitant use of meloxicam and esomeprazole, which are commonly prescribed together to allay NSAID-induced gastrointestinal (GI) bleeding. Empirically, we validate our approach against prior methods for signal detection on EHR datasets, in which our proposed approach outperforms all the compared methods across most metrics, such as area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC).     

Modeling and predicting clinical efficacy for drugs targeting the tumor milieu

Disappointing results from most late-stage clinical trials of cancer therapeutics indicate a need for improved and more-predictive animal tumor models. This insufficiency of models, combined with the advent of a class of drugs that target the tumor microenvironment rather than the tumor cell, presents new challenges for designing and interpreting preclinical efficacy studies. A comparison of the clinical efficacy of anti-angiogenic drugs with their corresponding preclinical studies over the past two decades offers many lessons that can inform and improve the design of experiments in existing mouse models. In addition, technological and logistical advances in mouse models of human cancer over the past five years have the potential to increase the clinical translatability of animal studies.     

Mesenchymal stromal cell-dependent immunoregulation in chemically-induced acute liver failure

Drug-induced liver injury(DILI),which refers to liver damage caused by a drug or its metabolites,has emerged as an important cause of acute liver failure(ALF)in recent years.Chemically-induced ALF in animal models mimics the pathology of DILI in humans;thus,these models are used to study the mechanism of potentially effective treatment strategies.Mesenchymal stromal cells(MSCs)possess immunomodulatory properties,and they alleviate acute liver injury and decrease the mortality of animals with chemically-induced ALF.Here,we summarize some of the existing research on the interaction between MSCs and immune cells,and discuss the possible mechanisms underlying the immunomodulatory activity of MSCs in chemically-induced ALF.We conclude that MSCs can impact the phenotype and function of macrophages,as well as the differentiation and maturation of dendritic cells,and inhibit the proliferation and activation of T lymphocytes or B lymphocytes.MSCs also have immunomodulatory effects on the production of cytokines,such as prostaglandin E2 and tumor necrosis factor-alpha-stimulated gene 6,in animal models.Thus,MSCs have significant benefits in the treatment of chemically-induced ALF by interacting with immune cells and they may be applied to DILI in humans in the near future.     

Reassessing Models of Hepatic Extraction

The aim of this investigation is to compare different mathematical models of the liver in the context of in vitro-in vivo correlation. We reanalyze drugs from the Houston reviews [1, 2], and compare the mathematical models. For the well-stirred model, a particular form of the distributed tubes model, and the dispersion model, fits are done to in vitro and in vivo intrinsic clearance data from microsomal and hepatocyte experiments. The distributed and dispersion models have decreased residuals as compared to the well-stirred model, but neither is to be clearly preferred over theother. It seems likely that drug-specific factors have a major impact on the quality of IVIVC correlations. While new experiments are needed to validate IVIVC models, our results indicate that improved correlation of in vitroand in vivo data is possible for high clearance drugs by using either a dispersion or distributed tube model rather than a well-stirred model.     

Protecting mitochondria via inhibiting VDAC1 oligomerization alleviates ferroptosis in acetaminophen-induced acute liver injury

Cell Biology and Toxicology - Acetaminophen (APAP) overdose is a common cause of drug-induced liver injury (DILI). Ferroptosis has been recently implicated in APAP-induced liver injury (AILI)....     

Mitochondrial stress response in drug-induced liver injury

Drug-induced liver injury (DILI) caused by the ingestion of medications, herbs, chemicals or dietary supplements, is a clinically widespread health problem. The underlying mechanism of DILI is the formation of reactive metabolites, which trigger mitochondrial oxidative stress and the opening of mitochondrial permeability transition (MPT) pores through direct toxicity or immune response, leading to cell inflammation, apoptosis, and necrosis. Traditionally, mitochondria play an indispensable role in maintaining the physiological and biochemical functions of cells by producing ATP and mediating intracellular signal transduction; drugs can typically stimulate the mitochondria and, in the case of sustained stress, can eventually cause impairment of mitochondrial function and metabolic activity. Meanwhile, the mitochondrial stress response, as an adaptive protective mechanism, occurs when mitochondrial homeostasis is threatened. In this review, we summarize the relevant frontier researches of the protective effects of mitochondrial stress response in DILI as well as the potential related mechanisms, thus providing some thoughts for the clinical treatment of DILI.

     

非酒精性脂肪肝与脂肪性肝炎动物模型研究进展

非酒精性脂肪肝是无酒精滥用的包括单纯性脂肪肝、脂肪性肝炎、脂肪性肝纤维化和肝硬化的肝病综合征,目前已成为广受关注的肝病医学难题。随着抗脂肪肝药物的深入研究,动物模型制作得到很好发展。近年来,在大鼠、沙鼠、小鼠、兔和小猪等动物种属成功地建立了食物、胃肠外营养与蛋氨酸胆碱缺乏等诱导的单纯性脂肪肝和脂肪性肝炎动物模型,这些模型为研究脂肪肝和脂肪性肝炎的发病机理与治疗提供了机会。每种动物模型各有优缺点,合理应用动物模型能更好地开展脂肪肝病的实验和临床研究。本文综述了非酒精性脂肪肝及脂肪性肝炎动物模型制作方法的若干研究进展。     

Idiosyncratic NSAID drug induced oxidative stress

Many idiosyncratic non-steroidal anti-inflammatory drugs (NSAIDs) cause GI, liver and bone marrow toxicity in some patients which results in GI bleeding/ulceration/fulminant hepatic failure/hepatitis or agranulocytosis/aplastic anemia. The toxic mechanisms proposed have been reviewed. Evidence is presented showing that idiosyncratic NSAID drugs form prooxidant radicals when metabolised by peroxidases known to be present in these tissues. Thus GSH, NADH and/or ascorbate were cooxidised by catalytic amounts of NSAIDs and hydrogen peroxide in the presence of peroxidase. During GSH and NADH cooxidation, oxygen uptake and activation occurred. Furthermore the formation of NSAID oxidation products was prevented during the cooxidation indicating that the cooxidation involved redox cycling of the first formed NSAID radical product. The order of prooxidant catalytic effectiveness of fenamate and arylacetic acid NSAIDs was mefenamic acid>tolfenamic acid>flufenamic acid, meclofenamic acid or diclofenac. Diphenylamine, a common moiety to all of these NSAIDs was a more active prooxidant for NADH and ascorbate cooxidation than these NSAIDs which suggests that oxidation of the NSAID diphenylamine moiety to a cation and/or nitroxide radical was responsible for the NSAID prooxidant activity. The order of catalytic effectiveness found for sulfonamide derivatives was sulfaphenazole>sulfisoxazolez.Gt;dapsone>sulfanilic acid>procainamide>sulfamethoxazole>sulfadiazine>sulfadimethoxine whereas sulfanilamide, sulfapyridine or nimesulide had no prooxidant activity. Although indomethacin had little prooxidant activity, its major in vivo metabolite, N-deschlorobenzoyl indomethacin had significant prooxidant activity. Aminoantipyrine the major in vivo metabolite of aminopyrine or dipyrone was also more prooxidant than the parent drugs. It is hypothesized that the NSAID radicals and/or the resulting oxidative stress initiates the cytotoxic processes leading to idiosyncratic toxicity.     

Animal models of portal hypertension

Chronic liver diseases ultimately lead to cirrhosis and portal hypertension (PHT). Indeed, PHT is a major cause of severe complications, while medical treatment is limited to non-selective beta blockers. Sophisticated animal models are needed to investigate novel treatment options for different etiologies of liver disease, effective anti-fibrotic agents as well as vasoactive drugs against PHT. In this review, we present some of the most common animal models of liver disease and PHT – including pre-hepatic, intra-hepatic and post-hepatic PHT in rodents. Methodology for induction, considerations for disease etiology, advantages and limitations and practical issues of these animal models are discussed. The appropriate and sensible use of animal models in preclinical research supporting the 3R concept of replacement, reduction and refinement is highlighted.     

Bile salt export pump inhibitors are associated with bile acid-dependent drug-induced toxicity in sandwich-cultured hepatocytes

Drug-induced liver injury (DILI) is a major reason for the dropout of candidate compounds from drug testing and the withdrawal of pharmaceuticals from clinical use. Among the various mechanisms of liver injury, the accumulation of bile acids (BAs) within hepatocytes is thought to be a primary mechanism for the development of DILI. Although bile salt export pump (BSEP) dysfunction is considered a susceptibility factor for DILI, little is known about the relationship between drug-induced BSEP dysfunction and BA-dependent hepatotoxicity. Furthermore, few methods are at hand for the systematic and quantitative evaluation of BA-dependent DILI. This study aimed to construct a model of DILI by employing sandwich-cultured hepatocytes (SCHs). SCHs can be used to assess functions of canalicular transporters such as BSEP and the activity of metabolic enzymes. Here, the impact of 26 test compounds (ritonavir, troglitazone, etc.) was investigated on BA-dependent cytotoxicity in SCHs. SCHs were exposed to each compound for 24h with or without BAs (glycochenodeoxycholic acid, deoxycholic acid, etc.). As a result, BA-dependent toxicity was observed for 11 test compounds in SCHs treated in the presence of BAs, while no signs of toxicity were observed for SCHs treated in the absence of BAs. Of the 11 compounds, nine were known BSEP inhibitors. Moreover, for some compounds, an increase in the severity of BA-dependent toxicity was observed in SCHs that were co-treated with 1-aminobenzotriazole, a non-selective inhibitor of cytochrome P450 (CYP450)-mediated drug metabolism. These results indicate that the SCH-based model is likely to prove useful for the evaluation of BA-dependent DILI, including the effects of drug metabolism and BSEP inhibition on liver injury.