digoxin and Chemical-and-Drug-Induced-Liver-Injury

Efficacy and safety of dronedarone was shown in the ATHENA trial for paroxysmal or persistent atrial fibrillation (AF) patients. Further trials revealed safety concerns in patients with heart failure and permanent AF. This review summarizes insights from recent real-world studies and meta-analyses, including reports on efficacy, with focus on liver safety, mortality risk in patients with paroxysmal/persistent AF, and interactions of dronedarone with direct oral anticoagulants. Reports of rapidly progressing liver failure in dronedarone-prescribed patients in 2011 led to regulatory cautions about potential liver toxicity. Recent real-world evidence suggests dronedarone liver safety profile is similar to other antiarrhythmics and liver toxicity could be equally common with many Class III antiarrhythmics. Dronedarone safety concerns (increased mortality in patients with permanent AF) were raised based on randomized controlled trials (RCT) (ANDROMEDA and PALLAS), but comedication with digoxin may have increased the mortality rates in PALLAS, considering the dronedarone-digoxin pharmacokinetic (PK) interaction. Real-world data on apixaban-dronedarone interactions and edoxaban RCT observations suggest no significant safety risks for these drug combinations. Median trough plasma concentrations of dabigatran 110 mg during concomitant use with dronedarone are at acceptable levels, while PK data on the rivaroxaban-dronedarone interaction are unavailable. In RCTs and real-world studies, dronedarone significantly reduces AF burden and cardiovascular hospitalizations, and demonstrates a low risk for proarrhythmia in patients with paroxysmal or persistent AF. The concerns on liver safety must be balanced against the significant reduction in hospitalizations in patients with non-permanent AF and low risk for proarrhythmias following dronedarone treatment.

Topics: Anti-Arrhythmia Agents; Antithrombins; Atrial Fibrillation; Chemical and Drug Induced Liver Injury; Dabigatran; Digoxin; Dronedarone; Drug Interactions; Factor Xa Inhibitors; Heart Failure; Hospitalization; Humans; Mortality; Pyridines; Randomized Controlled Trials as Topic; Thiazoles

Topics: Chemical and Drug Induced Liver Injury; Databases, Factual; Drug Labeling; Humans; Pharmaceutical Preparations; Risk

Topics: Acetaminophen; Acetylcysteine; Antidotes; Charcoal; Chemical and Drug Induced Liver Injury; Child Abuse; Child, Preschool; Digitoxin; Digoxin; Emetics; Female; Gastric Lavage; Humans; Hydrocarbons; Immunoglobulin Fab Fragments; Infant; Male; Naloxone; Narcotics; Poisoning

Topics: Anemia, Macrocytic; Anti-Arrhythmia Agents; Anticoagulants; Azathioprine; Chemical and Drug Induced Liver Injury; Cholelithiasis; Clofibrate; Colitis, Ulcerative; Dextrans; Digoxin; Drug Therapy; Fibrinolytic Agents; Halothane; Heparin; Hepatitis B Antigens; Humans; Hyperthyroidism; Iodine Isotopes; Leukemia; Myocardial Infarction; Platelet Adhesiveness; Pulmonary Embolism; Thrombosis; Venoms

Although the mechanism of action is not well known, intravenous lipid emulsion (ILE) has been shown to be effective in the treatment of lipophilic drug intoxications. It is thought that, ILE probably separates the lipophilic drugs from target tissue by creating a lipid-rich compartment in the plasma. The second theory is that ILE provides energy to myocardium with high-dose free fatty acids activating the voltage-gated calcium channels in the myocytes. In this study, effects of ILE treatment on digoxin overdose were searched in an animal model in terms of cardiac side effects and survival. Forty Sprague-Dawley rats were divided into five groups. As the pre-treatment, the groups were administered saline, ILE, DigiFab and DigiFab and ILE. Following that, digoxin was infused to all groups until death except the control group. First arrhythmia and cardiac arrest observation times were recorded. According to the results, there was no statistically significant difference among the group in terms of first arrhythmia time and cardiac arrest times. However, when the saline group compared with ILE-treated group separately, significant difference was observed. DigiFab, ILE or ILE-DigiFab treatment make no significant difference in terms of the first arrhythmia and cardiac arrest duration in digoxin-intoxicated rats. However, it is not possible to say that at the given doses, ILE treatment might be successful at least as a known antidote. The fact that the statistical significance between the two groups is not observed in the subgroup analysis, the study should be repeated with larger groups.

Topics: Animals; Antidotes; Arrhythmias, Cardiac; Cardiotoxicity; Chemical and Drug Induced Liver Injury; Cytoprotection; Digoxin; Disease Models, Animal; Fat Emulsions, Intravenous; Fatty Liver; Heart Arrest; Immunoglobulin Fab Fragments; Kidney; Liver; Rats, Sprague-Dawley

Triptolide (TP) is the major active principle of Tripterygium wilfordii Hook f. and very effective in treatment of autoimmune diseases. However, TP induced hepatotoxicity limited its clinical applications. Our previous study found that TP was a substrate of P-glycoprotein and its hepatobiliary clearance was markedly affected by P-gp modulation in sandwich-cultured rat hepatocytes. In this study, small interfering RNA (siRNA) and specific inhibitor tariquidar were used to investigate the impact of P-gp down regulation on TP-induced hepatotoxicity. The results showed that when the function of P-gp was inhibited by mdr1a-1 siRNA or tariquidar, the systemic and hepatic exposures of TP were significantly increased. The aggravated hepatotoxicity was evidenced with the remarkably lifted levels of serum biomarkers (ALT and AST) and pathological changes in liver. The other toxicological indicators (MDA, SOD and Bcl-2/Bax) were also significantly changed by P-gp inhibition. The data analysis showed that the increase of TP exposure in mice was quantitatively correlated to the enhanced hepatotoxicity, and the hepatic exposure was more relevant to the toxicity. P-gp mediated clearance played a significant role in TP detoxification. The risk of herb-drug interaction likely occurs when TP is concomitant with P-gp inhibitors or substrates in clinic.

Topics: Alanine Transaminase; Animals; Apoptosis Regulatory Proteins; Aspartate Aminotransferases; ATP Binding Cassette Transporter, Subfamily B; Cell Line, Tumor; Chemical and Drug Induced Liver Injury; Digoxin; Diterpenes; Epoxy Compounds; Gene Knockdown Techniques; Immunologic Factors; Male; Mice, Inbred BALB C; Molecular Docking Simulation; Oxidative Stress; Phenanthrenes; Protein Binding; Quinolines; RNA, Small Interfering

Drug-induced liver injury is the most common cause of market withdrawal of pharmaceuticals, and thus, there is considerable need for better prediction models for DILI early in drug discovery. We present a study involving 223 marketed drugs (51% associated with clinical hepatotoxicity; 49% non-hepatotoxic) to assess the concordance of in vitro bioactivation data with clinical hepatotoxicity and have used these data to develop a decision tree to help reduce late-stage candidate attrition. Data to assess P450 metabolism-dependent inhibition (MDI) for all common drug-metabolizing P450 enzymes were generated for 179 of these compounds, GSH adduct data generated for 190 compounds, covalent binding data obtained for 53 compounds, and clinical dose data obtained for all compounds. Individual data for all 223 compounds are presented here and interrogated to determine what level of an alert to consider termination of a compound. The analysis showed that 76% of drugs with a daily dose of <100 mg were non-hepatotoxic (p < 0.0001). Drugs with a daily dose of ≥100 mg or with GSH adduct formation, marked P450 MDI, or covalent binding ≥200 pmol eq/mg protein tended to be hepatotoxic (∼ 65% in each case). Combining dose with each bioactivation assay increased this association significantly (80-100%, p < 0.0001). These analyses were then used to develop the decision tree and the tree tested using 196 of the compounds with sufficient data (49% hepatotoxic; 51% non-hepatotoxic). The results of these outcome analyses demonstrated the utility of the tree in selectively terminating hepatotoxic compounds early; 45% of the hepatotoxic compounds evaluated using the tree were recommended for termination before candidate selection, whereas only 10% of the non-hepatotoxic compounds were recommended for termination. An independent set of 10 GSK compounds with known clinical hepatotoxicity status were also assessed using the tree, with similar results.

Topics: Chemical and Drug Induced Liver Injury; Cytochrome P-450 Enzyme Inhibitors; Cytochrome P-450 Enzyme System; Decision Trees; Drug Evaluation, Preclinical; Drug-Related Side Effects and Adverse Reactions; Glutathione; Humans; Liver; Pharmaceutical Preparations; Protein Binding

The human bile salt export pump (BSEP) is a membrane protein expressed on the canalicular plasma membrane domain of hepatocytes, which mediates active transport of unconjugated and conjugated bile salts from liver cells into bile. BSEP activity therefore plays an important role in bile flow. In humans, genetically inherited defects in BSEP expression or activity cause cholestatic liver injury, and many drugs that cause cholestatic drug-induced liver injury (DILI) in humans have been shown to inhibit BSEP activity in vitro and in vivo. These findings suggest that inhibition of BSEP activity by drugs could be one of the mechanisms that initiate human DILI. To gain insight into the chemical features responsible for BSEP inhibition, we have used a recently described in vitro membrane vesicle BSEP inhibition assay to quantify transporter inhibition for a set of 624 compounds. The relationship between BSEP inhibition and molecular physicochemical properties was investigated, and our results show that lipophilicity and molecular size are significantly correlated with BSEP inhibition. This data set was further used to build predictive BSEP classification models through multiple quantitative structure-activity relationship modeling approaches. The highest level of predictive accuracy was provided by a support vector machine model (accuracy = 0.87, κ = 0.74). These analyses highlight the potential value that can be gained by combining computational methods with experimental efforts in early stages of drug discovery projects to minimize the propensity of drug candidates to inhibit BSEP.

Topics: Animals; ATP Binding Cassette Transporter, Subfamily B, Member 11; ATP-Binding Cassette Transporters; Bile Acids and Salts; Cell Line; Chemical and Drug Induced Liver Injury; Humans; Quantitative Structure-Activity Relationship

Drug-induced liver injury (DILI) is a leading cause of drugs failing during clinical trials and being withdrawn from the market. Comparative analysis of drugs based on their DILI potential is an effective approach to discover key DILI mechanisms and risk factors. However, assessing the DILI potential of a drug is a challenge with no existing consensus methods. We proposed a systematic classification scheme using FDA-approved drug labeling to assess the DILI potential of drugs, which yielded a benchmark dataset with 287 drugs representing a wide range of therapeutic categories and daily dosage amounts. The method is transparent and reproducible with a potential to serve as a common practice to study the DILI of marketed drugs for supporting drug discovery and biomarker development.

Topics: Animals; Benchmarking; Biomarkers, Pharmacological; Chemical and Drug Induced Liver Injury; Drug Design; Drug Labeling; Drug-Related Side Effects and Adverse Reactions; Humans; Pharmaceutical Preparations; Reproducibility of Results; United States; United States Food and Drug Administration

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.

Topics: Animals; Anti-Infective Agents; Anti-Inflammatory Agents; Chemical and Drug Induced Liver Injury; Databases, Factual; Drug-Related Side Effects and Adverse Reactions; Humans; Liver; Models, Biological; Predictive Value of Tests

Drug-induced liver injury is one of the main causes of drug attrition. The ability to predict the liver effects of drug candidates from their chemical structures is critical to help guide experimental drug discovery projects toward safer medicines. In this study, we have compiled a data set of 951 compounds reported to produce a wide range of effects in the liver in different species, comprising humans, rodents, and nonrodents. The liver effects for this data set were obtained as assertional metadata, generated from MEDLINE abstracts using a unique combination of lexical and linguistic methods and ontological rules. We have analyzed this data set using conventional cheminformatics approaches and addressed several questions pertaining to cross-species concordance of liver effects, chemical determinants of liver effects in humans, and the prediction of whether a given compound is likely to cause a liver effect in humans. We found that the concordance of liver effects was relatively low (ca. 39-44%) between different species, raising the possibility that species specificity could depend on specific features of chemical structure. Compounds were clustered by their chemical similarity, and similar compounds were examined for the expected similarity of their species-dependent liver effect profiles. In most cases, similar profiles were observed for members of the same cluster, but some compounds appeared as outliers. The outliers were the subject of focused assertion regeneration from MEDLINE as well as other data sources. In some cases, additional biological assertions were identified, which were in line with expectations based on compounds' chemical similarities. The assertions were further converted to binary annotations of underlying chemicals (i.e., liver effect vs no liver effect), and binary quantitative structure-activity relationship (QSAR) models were generated to predict whether a compound would be expected to produce liver effects in humans. Despite the apparent heterogeneity of data, models have shown good predictive power assessed by external 5-fold cross-validation procedures. The external predictive power of binary QSAR models was further confirmed by their application to compounds that were retrieved or studied after the model was developed. To the best of our knowledge, this is the first study for chemical toxicity prediction that applied QSAR modeling and other cheminformatics techniques to observational data generated by the means of automate

Topics: Animals; Chemical and Drug Induced Liver Injury; Cluster Analysis; Databases, Factual; Humans; MEDLINE; Mice; Models, Chemical; Molecular Conformation; Quantitative Structure-Activity Relationship

Drug-induced liver injury is a major issue of concern and has led to the withdrawal of a significant number of marketed drugs. An understanding of structure-activity relationships (SARs) of chemicals can make a significant contribution to the identification of potential toxic effects early in the drug development process and aid in avoiding such problems. This process can be supported by the use of existing toxicity data and mechanistic understanding of the biological processes for related compounds. In the published literature, this information is often spread across diverse sources and can be varied and unstructured in quality and content. The current work has explored whether it is feasible to collect and use such data for the development of new SARs for the hepatotoxicity endpoint and expand upon the limited information currently available in this area. Reviews of hepatotoxicity data were used to build a structure-searchable database, which was analyzed to identify chemical classes associated with an adverse effect on the liver. Searches of the published literature were then undertaken to identify additional supporting evidence, and the resulting information was incorporated into the database. This collated information was evaluated and used to determine the scope of the SARs for each class identified. Data for over 1266 chemicals were collected, and SARs for 38 classes were developed. The SARs have been implemented as structural alerts using Derek for Windows (DfW), a knowledge-based expert system, to allow clearly supported and transparent predictions. An evaluation exercise performed using a customized DfW version 10 knowledge base demonstrated an overall concordance of 56% and specificity and sensitivity values of 73% and 46%, respectively. The approach taken demonstrates that SARs for complex endpoints can be derived from the published data for use in the in silico toxicity assessment of new compounds.

Topics: Chemical and Drug Induced Liver Injury; Databases, Factual; Humans; Structure-Activity Relationship; Tetracyclines; Thiophenes

Drug-induced liver injury (DILI) is one of the most important reasons for drug development failure at both preapproval and postapproval stages. There has been increased interest in developing predictive in vivo, in vitro, and in silico models to identify compounds that cause idiosyncratic hepatotoxicity. In the current study, we applied machine learning, a Bayesian modeling method with extended connectivity fingerprints and other interpretable descriptors. The model that was developed and internally validated (using a training set of 295 compounds) was then applied to a large test set relative to the training set (237 compounds) for external validation. The resulting concordance of 60%, sensitivity of 56%, and specificity of 67% were comparable to results for internal validation. The Bayesian model with extended connectivity functional class fingerprints of maximum diameter 6 (ECFC_6) and interpretable descriptors suggested several substructures that are chemically reactive and may also be important for DILI-causing compounds, e.g., ketones, diols, and α-methyl styrene type structures. Using Smiles Arbitrary Target Specification (SMARTS) filters published by several pharmaceutical companies, we evaluated whether such reactive substructures could be readily detected by any of the published filters. It was apparent that the most stringent filters used in this study, such as the Abbott alerts, which captures thiol traps and other compounds, may be of use in identifying DILI-causing compounds (sensitivity 67%). A significant outcome of the present study is that we provide predictions for many compounds that cause DILI by using the knowledge we have available from previous studies. These computational models may represent cost-effective selection criteria before in vitro or in vivo experimental studies.

Topics: Bayes Theorem; Chemical and Drug Induced Liver Injury; Humans; Ligands

In order to investigate a new possibility for artificial blood with oxygen-carrying capability to be applied to other than mere supplementation, normothermic whole body rinse-out in which artificial blood deriving from perfluorochemical emulsion, Fluosol-DA 20% (Green Cross Co., Ltd., Osaka, Japan) or stabilized hemoglobin solution, (pyridoxylated hemoglobin)-(polyoxyethylene) conjugate solution (Ajinomoto Co., Ltd., Tokyo, Japan) were used as rinsing fluid for a blood purification experiment. Replacement either with approximately 150 ml/kg of Fluosol-DA or stabilized hemoglobin solution showed effective removal of digoxin at a reduction rate of 96.3% or 92.2%, respectively. However, when Fluosol-DA was used, a certain amount of perfluorochemical should be retrieved by centrifugation to avoid a possible toxic effect on the reticulo-endothelial system. Even though 3 out of 6, and 3 out of 8 dogs, respectively, survived for a long period after the procedure, the experimental dogs were very susceptible to infection.

Topics: Animals; Blood Coagulation Disorders; Blood Substitutes; Chemical and Drug Induced Liver Injury; Digoxin; Dogs; Drug Combinations; Exchange Transfusion, Whole Blood; Fluorocarbons; Hematocrit; Hemoglobins; Hydroxyethyl Starch Derivatives; Kidney Diseases; Polyethylene Glycols; Solutions

In this report, we describe a case of acute atypical hepatitis during chronic treatment with amiodarone in a patient in which symptoms of digitalis toxicity also developed. The sudden onset of these side effects, whose mechanisms are herein discussed, stresses the importance of a careful monitoring of patients treated with amiodarone.

Topics: Aged; Amiodarone; Chemical and Drug Induced Liver Injury; Digoxin; Drug Interactions; Humans; Liver; Male

A case of serious intoxication with CS tear gas (o-chlorobenzylidene-malononitrile) is reported in a previously healthy male subject of 43 years involving pulmonary edema complicated by pneumonia, signs of heart failure and evidence of hepatocellular damage. Comparison with animal and human exposures supports the etiologic and pathogenetic role of CS tear gas in the present case. The pulmonary edema may have been the consequence of unusual conditions of exposure and/or increased individual susceptibility. The question of the "safety" of CS tear gas is discussed.

Topics: Adult; Chemical and Drug Induced Liver Injury; Digoxin; Furosemide; Heart Failure; Humans; Male; Nitriles; o-Chlorobenzylidenemalonitrile; Pneumonia; Pulmonary Edema; Tear Gases

Topics: Chemical and Drug Induced Liver Injury; Digitalis Glycosides; Digitoxin; Digoxin; Humans