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.     

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.     

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.

     

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.

     

化合物ADMET性质预测平台的构建

在药物研发早期阶段对化合物成药性和安全性进行评估,对于提高药物研发成功率、降低研发成本具有十分重要的意义。为了能够帮助药物研究工作者快速准确地判断候选化合物的成药性与安全性,开发了一个基于计算机方法的化合物ADMET性质预测平台。首先,通过文本挖掘的方法收集了化合物药代动力学性质和毒性(ADMET)的高质量实验数据。然后,根据原始文献复原了13个预测模型,同时采用支持向量机方法自建了15个具有较高预测能力的计算模型。最后,基于分布式架构,结合高性能计算集群优势,开发了化合物ADMET性质预测平台(http://www.vslead.com/?r=admet/index),用于预测28种重要的化合物ADMET性质。研究者可以使用这一平台快捷方便地对药物研究中比较重要的ADMET性质进行预测,在药物研发早期对候选化合物进行成药性评价和风险评估,有助于提高药物研的成功率,节省研发时间和经费的投入。     

Applications of cytotoxicity assays and pre-lethal mechanistic assays for assessment of human hepatotoxicity potential

While drug toxicity (especially hepatotoxicity) is the most frequent reason cited for withdrawal of an approved drug, no simple solution exists to adequately predict such adverse events. Simple cytotoxicity assays in HepG2 cells are relatively insensitive to human hepatotoxic drugs in a retrospective analysis of marketed pharmaceuticals. In comparison, a panel of pre-lethal mechanistic cellular assays hold the promise to deliver a more sensitive approach to detect endpoint-specific drug toxicities. The panel of assays covered by this review includes steatosis, cholestasis, phospholipidosis, reactive intermediates, mitochondria membrane function, oxidative stress, and drug interactions. In addition, the use of metabolically competent cells or the introduction of major human hepatocytes in these in vitro studies allow a more complete picture of potential drug side effect. Since inter-individual therapeutic index (TI) may differ from patient to patient, the rational use of one or more of these cellular assay and targeted in vivo exposure data may allow pharmaceutical scientists to select drug candidates with a higher TI potential in the drug discovery phase.     

Development and validation of a robust QSAR model for prediction of carcinogenicity of drugs

Carcinogenicity is one of the toxicological endpoints causing the highest concern. Also, the standard bioassays in rodents used to assess the carcinogenic potential of chemicals and drugs are extremely long, costly and require the sacrifice of large numbers of animals. For these reasons, we have attempted development of a global quantitative structure-activity relationship (QSAR) model using a data set of 1464 compounds (the Galvez data set available from http://www.uv.es/-galvez/tablevi.pdf), including many marketed drugs for their carcinogenesis potential. Though experimental toxicity testing using animal models is unavoidable for new drug candidates at an advanced stage of drug development, yet the developed global QSAR model can in silico predict the carcinogenicity of new drug compounds to provide a tool for initial screening of new drug candidate molecules with reduced number of animal testing, money and time. Considering large number of data points with diverse structural features used for model development (n(training) = 732) and model validation (n(test) = 732), the model developed in this study has an encouraging statistical quality (leave-one-out Q2 = 0.731, R2pred = 0.716). Our developed model suggests that higher lipophilicity values and conjugated ring systems, thioketo and nitro groups contribute positively towards drug carcinogenicity. On the contrary, tertiary and secondary nitrogens, phenolic, enolic and carboxylic OH fragments and presence of three-membered rings reduce the carcinogenicity. Branching, size and shape are found to be crucial factors for drug-induced carcinogenicity. One may consider all these points to reduce carcinogenic potential of the molecules.     

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).     

Sex Differences in Liver Toxicity—Do Female and Male Human Primary Hepatocytes React Differently to Toxicants In Vitro?

There is increasing amount of evidence for sex variation in drug efficiency and toxicity profiles. Women are more susceptible than men to acute liver injury from xenobiotics. In general, this is attributed to sex differences at a physiological level as well as differences in pharmacokinetics and pharmacodynamics, but neither of these can give a sufficient explanation for the diverse responses to xenobiotics. Existing data are mainly based on animal models and limited data exist on in vitro sex differences relevant to humans. To date, male and female human hepatocytes have not yet been compared in terms of their responses to hepatotoxic drugs. We investigated whether sex-specific differences in acute hepatotoxicity can be observed in vitro by comparing hepatotoxic drug effects in male and female primary human hepatocytes. Significant sex-related differences were found for certain parameters and individual drugs, showing an overall higher sensitivity of female primary hepatocytes to hepatotoxicants. Moreover, our work demonstrated that high content screening is feasible with pooled primary human hepatocytes in suspension.     

Developmental toxicology - an integral part of safety evaluation of new drugs

The thalidomide tragedy stimulated an intense research in the etiology, prevention and treatment of congenital malformations. The Government requires that drugs and food additives be evaluated pre-clinically for toxicity, including developmental toxicity, before being marketed. The number of compounds which must be tested has increased dramatically with the continuous development of therapeutic, cosmetic and food additive chemicals. Such tests include: in vitro studies which can serve as efficient pre-screens to rank chemicals for further batteries of in vivo tests on pregnant animals. However, the safety of any drug would be determined only by a post-marketing epidemiological survey. Taking into account the altered susceptibility to different drugs in a pregnant individual, it could be said that administration of any drug during the first trimester is an experiment in human teratology.     

Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates

MOTIVATION: To gather information about available databases and chemoinformatics methods for prediction of properties relevant to the drug discovery and optimization process. RESULTS: We present an overview of the most important databases with 2-dimensional and 3-dimensional structural information about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability of chemical compounds as potential drugs, as well as for predicting their physico-chemical and ADMET properties have been proposed in recent years. These methods are discussed, and some possible future directions in this rapidly developing field are described.     

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.     

Risk assessment and mitigation strategies for reactive metabolites in drug discovery and development

Drug toxicity is a leading cause of attrition of candidate drugs during drug development as well as of withdrawal of drugs post-licensing due to adverse drug reactions in man. These adverse drug reactions cause a broad range of clinically severe conditions including both highly reproducible and dose dependent toxicities as well as relatively infrequent and idiosyncratic adverse events. The underlying risk factors can be split into two groups: (1) drug-related and (2) patient-related. The drug-related risk factors include metabolic factors that determine the propensity of a molecule to form toxic reactive metabolites (RMs), and the RM and non-RM mediated mechanisms which cause cell and tissue injury. Patient related risk factors may vary markedly between individuals, and encompass genetic and non-genetic processes, e.g. environmental, that influence the disposition of drugs and their metabolites, the nature of the adverse responses elicited and the resulting biological consequences. We describe a new strategy, which builds upon the strategies used currently within numerous pharmaceutical companies to avoid and minimize RM formation during drug discovery, and that is intended to reduce the likelihood that candidate drugs will cause toxicity in the human population. The new strategy addresses drug-related safety hazards, but not patient-related risk factors. A common target organ of toxicity is the liver and to decrease the likelihood that candidate drugs will cause liver toxicity (both non-idiosyncratic and idiosyncratic), we propose use of an in vitro Hepatic Liability Panel alongside in vitro methods for the detection of RMs. This will enable design and selection of compounds in discovery that have reduced propensity to cause liver toxicity. In vitro Hepatic Liability is assessed using toxicity assays that quantify: CYP 450 dependent and CYP 450 independent cell toxicity; mitochondrial impairment; and inhibition of the Bile Salt Export Pump. Prior to progression into development, a Hepatotoxicity Hazard Matrix combines data from the Hepatic Liability Panel with the Estimated RM Body Burden. The latter is defined as the level of covalent binding of radiolabelled drug to human hepatocyte proteins in vitro adjusted for the predicted human dose. We exemplify the potential value of this approach by consideration of the thiazolidinedione class of drugs.     

IDMap: facilitating the detection of potential leads with therapeutic targets

Pharmaceutical industry has been striving to reduce the costs of drug development and increase productivity. Among the many different attempts, drug repositioning (retargeting existing drugs) comes into the spotlight because of its financial efficiency. We introduce IDMap which predicts novel relationships between targets and chemicals and thus is capable of repositioning the marketed drugs by using text mining and chemical structure information. Also capable of mapping commercial chemicals to possible drug targets and vice versa, IDMap creates convenient environments for identifying the potential lead and its targets, especially in the field of drug repositioning. AVAILABILITY: IDMap executable and its user manual including color images are freely available to non-commercial users at http://www.equispharm.com/idmap     

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.     

Multiple Drug Targeting Potential of Novel Ligands Against Virulent Proteins of Candida albicans

Systemic candidiasis is one of the most common nosocomial systemic infections causing high mortality and morbidity. Although infections caused by other Candida species are increasing the majority of candidiasis is commonly caused by Candida albicans which accounts for 40–60% of the reported cases. Current antifungal treatment regime is feeble due to persistent multiple drug resistance. Moreover, antifungal agents are mostly synthetic drugs that cause toxic side effects on immune-compromised patients. So, not only finding novel drug candidates from natural sources is necessary but to identify such compounds that have multiple targeting potential is essential. Hence, in this study, we propose to screen and identify bioactive natural compounds based on flavonoids, terpenes, and alkaloids for their potential antifungal activity. This in silico study was carried out by targeting 16 major virulent protein targets of C. albicans with 91 ligands. We have reported few ligands having multi-targeting potential and further analyzed theirs in silico pharmacokinetic profile. In silico ADMET analysis and protein–protein interaction can not only help in refining better drug candidates but also shortlist ideal protein for drug targeting.

     

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.