lu-208075 and Chemical-and-Drug-Induced-Liver-Injury

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

Pulmonary arterial hypertension (PAH) is a condition that leads to progressive right heart failure and death unless recognized and treated early. Endothelin, a potent endogenous vasoconstrictor, has been identified as an important mediator of PAH. Endothelin receptor antagonists (ERAs) have been associated with an improvement in exercise capacity and time to clinical worsening in patients with Group 1 PAH, and three different ERAs are currently approved for use in this population: bosentan, ambrisentan, and macitentan. While all three ERAs are generally well-tolerated, they each have important adverse effects that need to be recognized and monitored. In particular, they may cause anemia, peripheral edema, and mild cardiac, respiratory, neurologic, and gastrointestinal adverse effects to varying degrees. Although bosentan increases a patient's risk of developing liver transaminitis, ambrisentan and macitentan do not appear to confer the same risk of hepatotoxicity at this time. Important drug-drug interactions, particularly involving other drugs metabolized via the cytochrome P450 pathway, are important to recognize when prescribing ERAs. In this review, we provide a brief overview of the current state of knowledge as it relates to the adverse effect profiles, tolerability, and drug-drug interactions of this class of medication as informed by the results of randomized clinical trials, drug surveillance programs, and regulatory agencies.

Topics: Animals; Antihypertensive Agents; Bosentan; Chemical and Drug Induced Liver Injury; Drug Interactions; Drug Monitoring; Endothelin Receptor Antagonists; Heart Failure; Humans; Hypertension, Pulmonary; Phenylpropionates; Pyridazines; Pyrimidines; Risk; Sulfonamides

We evaluated the risk of hepatotoxicity associated to endothelin receptor antagonists.. Systematic searches in PubMed/MEDLINE, the Cochrane Library as well as regulatory agencies websites were performed. Randomized controlled trials in patients receiving endothelin receptor antagonists (bosentan, sitaxentan or ambrisentan) in at least one treatment group were included. Prior to data extraction, definitions of hepatotoxicity were established. Effect sizes with 95% confidence intervals were calculated using random effects models. Heterogeneity was analysed using Cochran's Q and I(2) tests. Publication bias was assessed using Egger's method and funnel plots were generated.. Twenty-one trials met the inclusion criteria (3,644 patients). Bosentan was the evaluated drug in 1,689 (74%) patients who received endothelin receptor antagonists. Compared with controls, relative risk for any hepatic adverse reaction was 2.92 (1.85-4.62; I(2)=30.6%). When hepatotoxicity was defined as elevations of liver alanine or aspartate aminotransferases equal or greater than 3 times the upper limit of normal, relative risk was 2.98 (1.69-5.25; I(2) = 40.9%). No evidence of publication bias was found (Egger's method: p = 0.68).. Our results suggest an increased risk of hepatotoxicity in patients receiving endothelin receptor antagonists. Given the limited data available for endothelin receptor antagonists other than bosentan, it is not possible to draw firm conclusions about the individual risk associated for the remaining endothelin receptor antagonists.

Topics: Antihypertensive Agents; Biomarkers; Bosentan; Chemical and Drug Induced Liver Injury; Endothelin Receptor Antagonists; Humans; Hypertension; Isoxazoles; Phenylpropionates; Pyridazines; Randomized Controlled Trials as Topic; Safety-Based Drug Withdrawals; Sulfonamides; Thiophenes; Transaminases

Drug-induced liver injury has been observed in patients treated with the endothelin receptor antagonists sitaxentan and bosentan, but not following treatment with ambrisentan. The aim of our studies was to assess the possible role of multiple contributory mechanisms in this clinically relevant toxicity. Inhibition of the bile salt export pump (BSEP) and multidrug resistance-associated protein 2 was quantified using membrane vesicle assays. Inhibition of mitochondrial respiration in human liver-derived HuH-7 cells was determined using a Seahorse XF(e96) analyzer. Cytochrome P450 (P450)-independent and P450-mediated cell toxicity was assessed using transfected SV40-T-antigen-immortalized human liver epithelial (THLE) cell lines. Exposure-adjusted assay ratios were calculated by dividing the maximum human drug plasma concentrations by the IC50 or EC50 values obtained in vitro. Covalent binding (CVB) of radiolabeled drugs to human hepatocytes was quantified, and CVB body burdens were calculated by adjusting CVB values for fractional drug turnover in vitro and daily therapeutic dose. Sitaxentan exhibited positive exposure-adjusted signals in all five in vitro assays and a high CVB body burden. Bosentan exhibited a positive exposure-adjusted signal in one assay (BSEP inhibition) and a moderate CVB body burden. Ambrisentan exhibited no positive exposure-adjusted assay signals and a low CVB body burden. These data indicate that multiple mechanisms contribute to the rare, but potentially severe liver injury caused by sitaxentan in humans; provide a plausible rationale for the markedly lower propensity of bosentan to cause liver injury; and highlight the relative safety of ambrisentan.

Topics: ATP Binding Cassette Transporter, Subfamily B; ATP Binding Cassette Transporter, Subfamily B, Member 11; ATP-Binding Cassette Sub-Family B Member 4; ATP-Binding Cassette Transporters; Bosentan; Cell Line; Cell Survival; Chemical and Drug Induced Liver Injury; Cytochrome P-450 Enzyme System; Dose-Response Relationship, Drug; Endothelin Receptor Antagonists; Epithelial Cells; Hepatocytes; Humans; Isoxazoles; Mitochondria; Molecular Structure; Oxygen Consumption; Phenylpropionates; Pyridazines; Sulfonamides; Thiophenes

Topics: Adult; Aged; Alanine Transaminase; Antihypertensive Agents; Aspartate Aminotransferases; Chemical and Drug Induced Liver Injury; Drug Administration Schedule; Endothelin A Receptor Antagonists; Endothelins; Female; Humans; Hypertension, Pulmonary; Incidence; Liver; Liver Function Tests; Male; Middle Aged; Phenylpropionates; Pyridazines; Risk Factors; United States

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