novobiocin and Chemical-and-Drug-Induced-Liver-Injury

Topics: Butyrophenones; Chemical and Drug Induced Liver Injury; Cholestasis; Contraceptives, Oral, Hormonal; Humans; Novobiocin; Prognosis; Rifampin; Salicylates; Steroids

Topics: Anti-Bacterial Agents; Bilirubin; Chemical and Drug Induced Liver Injury; Enzyme Induction; Griseofulvin; Humans; Liver; Novobiocin; Rifampin

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

Topics: Adrenal Cortex Hormones; Analgesics; Chemical and Drug Induced Liver Injury; Cholestasis; Contraceptive Agents; Drug Hypersensitivity; Drug-Related Side Effects and Adverse Reactions; Fatty Liver; Halothane; Humans; Isoniazid; Jaundice; Liver; Liver Cirrhosis; Metabolism, Inborn Errors; Methotrexate; Methoxyflurane; Methyldopa; Necrosis; Novobiocin

Topics: Alcoholism; Bilirubin; Chemical and Drug Induced Liver Injury; Cholangitis; Drug-Related Side Effects and Adverse Reactions; Ethanol; Fatty Liver; Feeding Behavior; Humans; Kwashiorkor; Liver; Liver Diseases; Methyltestosterone; Mitochondria, Liver; Novobiocin; Nutrition Disorders; Phenothiazines; Rifamycins; Starvation

Topics: Animals; Chemical and Drug Induced Liver Injury; Glucosyltransferases; Humans; Hyperbilirubinemia; Iodobenzenes; Liver; Novobiocin; Rats; Rifampin

Topics: Animals; Cats; Chemical and Drug Induced Liver Injury; Dogs; Humans; Infant, Newborn; Injections, Subcutaneous; Liver; Liver Function Tests; Male; Novobiocin; Rabbits; Rats; Sodium

Topics: Adolescent; Adult; Aged; Chemical and Drug Induced Liver Injury; Chlorpromazine; Erythromycin; Female; Humans; Male; Middle Aged; Novobiocin; Phosphorus; Porphyrias

Topics: Bilirubin; Chemical and Drug Induced Liver Injury; Child; Hepatitis A; Humans; Hyperbilirubinemia; Jaundice; Novobiocin; Toxicology

Topics: Anti-Bacterial Agents; Candidiasis; Chemical and Drug Induced Liver Injury; Chloramphenicol; Diagnosis, Differential; Erythromycin; Halothane; Hepatitis; Hydrocortisone; Hypertension; Kidney Diseases; Metaraminol; Novobiocin; Pathology; Pyelonephritis; Surgical Procedures, Operative; Surgical Wound Infection; Tetracycline; Toxicology; Tracheotomy; Urinary Bladder Neoplasms; Urinary Diversion

Topics: Anaphylaxis; Anti-Bacterial Agents; Aspirin; Chemical and Drug Induced Liver Injury; Chloramphenicol; Colistin; Drug Eruptions; Drug Hypersensitivity; Hepatitis; Nitrofurantoin; Novobiocin; Penicillins; Polymyxins; Protein Synthesis Inhibitors; Serum Sickness; Streptomycin; Tetracycline; Toxicology