Hepatotoxicity

Hepatotoxicity

Articles recommended in the eTOX-LIBRARY and annotated with HEPATOTOXICITY keyword.
Note: this subsection exists from October 15th 2012 on.

Take advantage of these premade PubMed queries focused on Hepatotoxicity related terms:

2016

2015

2014

  • Age-related differences in reporting of drug-associated liver injury: Data-mining of WHO Safety Report Database. Hunt CM. et al. Regul Toxicol Pharmacol. (6086)
  • KEYWORDS: DATABASE – DATA MINING – DILI – DRUG SAFETY – HEPATOTOXICITY – SYSTEMS BIOLOGY

  • CARCINOscreen®: New short-term prediction method for hepatocarcinogenicity of chemicals based on hepatic transcript profiling in rats. Matsumoto H. et al. J Toxicol Sci. (6084)
  • KEYWORDS: ASSAY DATA – CARCINOGENICITY – HEPATOTOXICITY

  • Characterization of chemically induced liver injuries using gene co-expression modules. Tawa GJ. et al. PLoS One. (6080)
  • KEYWORDS: DATABASE – DILI – HEPATOTOXICITY – SYSTEMS BIOLOGY

  • Identification of metabolites, clinical chemistry markers and transcripts associated with hepatotoxicity. Buness A. et al. PLoS One. (6003)
  • KEYWORDS: ADME – BIOMARKERS – HEPATOTOXICITY – SYSTEMS BIOLOGY

    Analysis of data retrieved (see support material) from a panel of up to 23 clinical chemistry parameters for 274 samples has provided the identification of markers comprising metabolites such as taurocholic acid and putrescine (measured as sum parameter together with agmatine), classical clinical chemistry markers like AST (aspartate aminotransferase), ALT (alanine aminotransferase), and bilirubin, as well as gene transcripts like Igfbp1 (insulin-like growth factor-binding protein 1) and Egr1 (early growth response protein 1), as key markers for the assessment of hepatotoxicity risk. In Table 4, find an overview of samples/class (clinical chemistry, mebolites in liver, metabolites in serum and transcripts in liver) and parameter type.

  • Molecular mechanisms of liver injury: Apoptosis or necrosis. Wang K. et al. Exp Toxicol Pathol. (6000)
  • KEYWORDS: HEPATOTOXICITY – SYSTEMS BIOLOGY

    Review of several common forms of liver injury (chronic viral hepatitis (se Figure 1), cholestatic liver disease (see Figure 2) and acetamino-induced hepatotoxicity (see Figure 3)) and their potential mechanisms (comparison between apoptosis and necrosis).

  • Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat.. Römer M. et al.PLoS One. (5996)
  • KEYWORDS: DATABASES – HEPATOTOXICITY – SYSTEMS BIOLOGY

    This article shows how the classification performance of toxicogenomics models benefits from integrating heterogeneous omics data across multiple biological levels, toxicogenomics studies may benefit from data such metabolomics, relevant DNA mutations, and genome-wide promoter methylation. Authors encourage the maintainers of currently available databases specializing in toxicogenomics (e.g., TG-GATEs, DrugMatrix, etc.) to amend the existing repertoire of mRNA expression datasets by the addition of complementary omics data derived from the same biological samples.

  • A mechanistic model of drug-induced liver injury AIDS the interpretation of elevated liver transaminase levels in a phase I clinical trial. Howell BA. et al. CPT Pharmacometrics Syst Pharmacol. (5993)
  • KEYWORDS: DRUG SAFETY – HEPATOTOXICITY – RISK ASSESSMENT

    This article presents a new example of the DILIsym (a mechanistic model of drug-induced liver injury) application. In this case reporting how it can helps in the interpretation of the liver enzyme elevations observed in healthy volunteers receiving Entolimod.

  • Use of a systems model of drug-induced liver injury (DILIsym(®)) to elucidate the mechanistic differences between acetaminophen and its less-toxic isomer, AMAP, in mice. Howell BA. et al. Toxicol Lett. (5992)
  • KEYWORDS: ADME – CYP450 – DILI – HEPATOTOXICITY – PHARMACOKINETICS – RISK ASSESSMENT

    Use case application (acetaminophen) of the DILIsym model developed in the framework of MIP-DILI consortium. Four mechanistic hypothesis are discussed: 1) quantitative differences in drugmetabolism profiles as a result of different affinities for the relevant enzymes; 2) differences in theamount of reactive metabolites produced due to cytochrome P450 (CYP450) inhibition by the AMAP reactive metabolites; 3) differences in the rate of conjugation between the reactive metabolites and proteins; and 4) differences in the downstream effects or potencies of the reactive metabolites on vital components within hepatocytes.

  • Drug-induced liver injury: what was new in 2013?. Chalhoub WM. et al. Expert Opin Drug Metab Toxicol. (5984)
  • KEYWORDS: DILI – HEPATOTOXICITY

    Review on relevant information related with DILI toxicity appearing in 2013., see:
    – Table 1 for agents causing DILI in various epidemiologic series and registries
    – Table 2 for genetic risk factor list
    – Table 3 for genetic risk factors for Tuberculosis drugs causing DILI

    Section 7 provides miscellaneous information on: DILI databases, Drugs not initially approved by FDA, and miscellany DILI reports in Table 4.

  • Drug-Induced Hepatotoxicity: Metabolic, Genetic and Immunological Basis. Njoku DB. et al. Int J Mol Sci. (5983)
  • KEYWORDS: DILI – HEPATOTOXICITY – SYSTEMS BIOLOGY

    Review focused on studies surrounding drug hepatotoxicity where genetic (see seciton 3), metabolic (see section 1) and immune (see section 4) mechanisms were investigated.

  • Development of a cell-based assay system considering drug metabolism and immune- and inflammatory-related factors for the risk assessment of drug-induced liver injury. Yano A. et al. Toxicol Lett. (5982)
  • KEYWORDS: ASSAY DATA – DILI – HEPATOTOXICITY – RISK ASSESSMENT

    Analysis of metabolic, immune and inflammatory-related factors collected from a cell-based assay system lets to propose the use of the total sum score of gene expression (see Figure 7) for the risk assessment of DILI in preclinical drug development. Table 3 provides a list of inflammatory- and immune-related factors identified, and Table 4 gathers the safety profiles (drug name, concentration and daily dose) of 30 drugs regarding DILI in humans (

  • Construction and Analysis of a Human Hepatotoxicity Database Suitable for QSAR Modeling Using Post-Market Safety Data. Zhu X. et al. Toxicology. (5969)
  • KEYWORDS: DATABASE – HEPATOTOXICITY – QSAR MODELLING

    Based on a set of 2029 unique and modelable drug entities with 13,555 drug‐AdverseEffect combinations extracted from the FDA AERS database using 37 hepatotoxicity‐related query preferred terms (see Table III for a list of preferred terms selected to describe 4 liver injury endpoints ), authors generate datasets suitable to generate a battery of QSAR models for the assessment of drug-induced liver injury potential (see Table II for a list of drug therapeutic classes containing 10 or more drugs classified as positive for liver injury).

  • Toward predictive models for drug-induced liver injury in humans: are we there yet?. Chen M. et al. Biomark Med. (5938)
  • KEYWORDS: BIOMARKERS – DILI – HEPATOTOXICITY

    This review focuses on the current state of efforts in developing predictive models from diverse data sources ( a) homogeneous data: chemical structure-based in silico models (knowledge-based and QSAR-based models), in vitro assay-based models and toxicogenomics-based models); b) heterogeneous data: data integration, model integration) potential use in detecting human hepatotoxicity, and also provide perspectives on how to further improve DILI prediction. Table 1 reports published classifications of drugs for DILI risk in humans, and Table 2 reports predictive models for assessing DILI risk in humans.

  • Emerging efforts for discovering new biomarkers of liver disease and hepatotoxicity.. Hong H. et al. Biomark Med. (5937)
  • KEYWORDS: BIOMARKERS – HEPATOTOXICITY – TEXT MINING

    Discussion of new alternatives (DNA microarrays which allow monitoring of the expression levels of tens of thousands of genes in a single experiment (analyse data from CTD and ArrayExpress databases) or involved pathways evaluation) to identify potential biomakers to predict hepatotoxicity related events, in addition to the conventional ones (Serum ALT, AST and ALP activities, and total bilirubin concentration).

  • Evaluation of database-derived pathway development for enabling biomarker discovery for hepatotoxicity. Hebels DG. et al. Biomark Med. (5936)
  • KEYWORDS: BIOMARKERS – HEPATOTOXICITY – TEXT MINING

    This paper analyses text-mining (Comparative Toxicogenomics Database (CTD, GATACA Gene Explorer database, Library of Medical Associations (LoMA), MalaCards) and ‘omics-based (ArrayExpress, Gene Expression Omnibus (GEO)) databases to review how gene lists derived from text-mining databases compare with human liver tissue-derived gene lists and how they might be used with regards to hepatotoxicity biomarker discovery in nonanimal-based cellular system. Box 1 lists 17 hepatoxicity terms used in the search of information, Table 1 presents the number of genes identified for each text-mining database analysed, Box 2 provides the list of 64 hepatic compounds selected from the Project-Genomics Assisted Toxicity Evaluation system database based on overlap with the Liver Toxicity Knowledge Base from FDA.

  • Classification of hepatotoxicants using HepG2 cells: A proof of principle. Van den Hof WF. et al. Chem Res Toxicol. (5922)
  • KEYWORDS: HEPATOTOXICITY – RISK ASSESSMENT – SYSTEMS BIOLOGY

    Transcriptomic analyses of HepG2 cells can be used to assess hepatotoxicity risk. Analyses run for a small set of compounds evidence that classifiers selected for classification of hepatotoxicity and cholestasis indicate that endoplasmic reticulum stress and the unfolded protein response may be important cellular effects of drug-induced liver injury. Find in the support material section: the corresponding transcriptomics data accessible through GEO series accession number GSE51952, a file with the log2 fold change values of the 36 genes selected for classification of hepatotoxicity and another file with the 12 genes selected for classification of cholestasis.

  • Finding the rules for successful drug optimisation. Yusof I. et al. Drug Discov Today. (5919)
    Involved Partner: PFIZER
  • KEYWORDS: CARDIOTOXICITY – DRUG DISCOVERY – HEPATOTOXICITY – RISK ASSESSMENT

    This article proposes to define computational ‘rule indication’ approaches to enable an objective analysis of complex data to identify interpretable, multiparameter rules that distinguish compounds with the greatest likelihood of success for a project. A method to identify rules from complex and multidimensional data, the patient rule induction method (PRIM), is presented together with 2 examples of application:
    -Example 1: identifying drug-like compounds
    771 approved oral drugs, computed the quantitative estimate of drug-likeness (QED)
    -Example 2: toxicity classification
    Drugs extracted from CERP BioPrint assay panel (185 targets) with cardiotoxic and hepatotoxic potential in the clinic using (dataset 1: 408 cardiotoxic + 66 noncardiotoxic; and dataset 2: 302 hepatotoxic + 168 nonhepatotoxic).

  • Predictivity of dog co-culture model, primary human hepatocytes and HepG2 cells for the detection of hepatotoxic drugs in humans. Atienzar FA. et al. Toxicol Appl Pharmacol.. (5906)
  • KEYWORDS: ADME – ASSAY DATA – HEPATOTOXICITY

    This article reports on the development of a relevant dog co-culture model, equipped with adequate metabolic capacity for up 2 weeks, applied for a series of hepatotoxic and non-hepatotoxic compounds (see tables 3 (details of the drugs used in the studies: drug name, related analogs, therapeutic area, Cmax, DILI concern (score), DILI label and DILI description) and 7 (predictivity of the 3 cellular models to detect hepatotoxic and non-hepatotoxici drugs in human)).

  • The Role of Bile Salt Export Pump (BSEP) Gene Repression in Drug-Induced Cholestatic Liver Toxicity.
    Garzel B. et al.
    Drug Metab Dispos.
    (5904)
  • KEYWORDS: HEPATOTOXICITY – RISK ASSESSMENT – TRANSPORTERS

    .This present study in human primary hepatocytes reports the identification of a number of known BSEP (ACBC11) inhibitors also as potent repressors of this gene, so analysis of such features of a certain compound can help to assess on DILI occurrence since dual inhibitors and repressors of BSEP are often associated with severe clinically reported DILI.

  • Analysis of Pfizer Compounds in EPA’s ToxCast Chemicals-Assay Space. Shah F. et al. Chem Res Toxicol. (5903)
    Involved Partner: PFIZER
  • KEYWORDS: DATABASE – HEPATOTOXICITY – NUCLEAR RECEPTORS – RISK ASSESSMENT

    This article presents the analysis of 52 Pfizer compounds with preclinical and clinical data contributed to the ToxCast program initiative for their profiling across the multiple assay platform. Comparison within the ToxCast chemical space facilitated to explore common toxicity pathways. See support material, where the MOA and physicochemical properties of the Pfizer set, the AC50 values for the chemical and assay matrix, all assays are provided together with a brief assay description. Interestingly, part of the analysis allowed to identify novel interactions for Pfizer compounds in a series of nuclear receptors, which were not known till the date and can play some role in the understanding of hepatotoxicity occurrence.

  • Exploratory toxicology as an integrated part of drug discovery. Part II: screening strategies. Hornberg JJ. et al. Drug Discov Today. (5900)

    Involved Partner: LDB

  • KEYWORDS: CARDIOTOXICITY – DRUG DISCOVERY – GENOTOXICITY – HEPATOTOXICITY – IMMUNOTOXICITY – RISK ASSESSMENT

    In this review (part II) authours outline other aspects of the exploratory toxicology strategy defined by Lundbeck pharma. They present the timing of exploratory toxicology assays during the drug discovery project phases (see Figure 1), the endpoints exploratory in vivo toxicity study (see Figure 4), and their compound testing strategies with respect to cardiotoxicity, hepatotoxicity, genotoxicity and immunotoxicology.

2013

  • Development of predictive genetic tests for improving the safety of new medicines: the utilization of routinely collected electronic health records. Wing K. et al. Drug Discov Today. (5881)
  • KEYWORDS: DRUG SAFETY – DILI – HEPATOTOXICITY – RISK ASSESSMENT

    This article shows how an evaluation of routinely collected electronic health records information on genetic variation in the case of DILI reported cases could be used to develop predictive genetic tests for improving drug safety aspects (see Figure 1 for an example algorithm for identifying DILI using a database of electronic heath records).

  • Hepatocyte-based in vitro model for assessment of drug-induced cholestasis. Chatterjee S. et al. Toxicol Appl Pharmacol. (5873)
  • KEYWORDS: HEPATOTOXICITY – RISK ASSESSMENT

    This article presents an in vitro model based on sandwich-cultured hepatocytes to assess the potential of a compound to cause cholestasis by disturbing Bile Acids homeostasis, based on the analysis of data available for compounds with clinical reports of cholestasis (cyclosporin A, troglitazone, chlorpromazine, bosentan, ticlopidine, ritonavir, and midecamycin) enhanced toxicity in the presence of BAs, in comparison to data from compounds causing hepatotoxicity by other mechanisms (diclofenac, valproic acid, amiodarone and acetaminophen) which remained unchanged in the presence of BAs.

  • Safety biomarkers for drug-induced liver injury –current status and future perspectives. Antonie DJ. et al. Toxicol. Res. (5860)
  • KEYWORDS: BIOMARKERS – DILI – HEPATOTOXICITY

    This article discussed in the context of advancing fundamental mechanistic drug safety in the case of DILI event occurrence, how formal and novel biomarkers (see section Exploratory biomarkers for DILI under development, and figures 1 and 2) can help on the risk assessment aspects. Herein, it is highlighted the challenges of the Predictive Safety Testing Consortium (PSTC, US) and the IMI Safer and Faster Evidence based Translation (SAFE-T, Europe) project in this field.

  • Drug safety testing paradigm, current progress and future challenges: an overview. Varun A. et al. J Appl Toxicol. (5859)
  • KEYWORDS: BIOMARKERS – CARDIOTOXICITY – DRUG DISCOVERY – DRUG SAFETY – GENOTOXICITY – NEUROTOXICITY – PHARMACOLOGY – RESPIRATORY SENSITIZATION – SOFTWARE

    This review points out the fact that toxicologists should change their classical approach to a more investigative approach in order to allow them the anticipation of safety problems, they should collaborate with medicinal chemists, pharmacologists and clinicians. See the summarized contents in several tables:
    -Table 1. Routine exploratory safety studies (in silico, genetic toxicology, safety pharmacology and general toxicology
    -Table 2. Selected in silico systems available for safety prediction (i.e. DEREK)
    -Table 3. Standard genotoxicity testing battery
    -Table 4. Tests and parameters available to assess cardiovascular system, central nervous system and respiratory function in safety pharmacology studies
    -Table 5. Exploratory general toxicology studies
    -Table 6. Availability of in vitro assays for toxicity testing (grouped by target organ/system)
    -Table7. Selected references using ‘omics’ technologies in drug discovery for preclinical safety testing (metabolomics, toxicoproteomics and toxicogenomics)

  • Plasma biomarkers of liver injury and inflammation demonstrate a lack 2 of apoptosis during obstructive cholestasis in mice. Woolbright BL. et al. Toxicol Appl Pharmacol. (5858)
  • KEYWORDS: BIOMARKERS – HEPATOTOXICITY

    This articles reports the detection of early cholestatic liver injury in mice as an inflammatory event highlighted by analysis of a set of plasma biomarkers (cytokeratin-18 (FL-K18), microRNA-122 (miR-122) and high mobility group box-1 protein (HMGB1)) in comparison to Alanine aminotransferase (ALT).

  • Integrative Chemical-Biological Read-Across Approach for Chemical Hazard Classification. Low Y. et al. Chem Res Toxicol. (5792)
  • KEYWORDS: CARCINOGENICITY – HEPATOTOXICITY – MOLECULAR DESCRIPTORS – REGULATORY GUIDELINES – RISK ASSESSMENT – STRUCTURE-BASED PREDICTION

    The hybrid chemical-biological read-across (CBRA) method is presented as a encouraging tool for structure-based predictions, since it is predictive and interpretable thanks to the visualization methodology ensembled. Based on data from different sources (127 compounds TG-GATES database, 132 compounds DrugMatrix database, 240 compounds from Lock et . al ), authors apply their new methodology, and discuss results for 3 case studies (chloramphenicol, carbamazepine, and benzbromarone), list the advantages and limitations of CBRA method and make general recommendations for chemical-biological modeling and its application in hazard assessment.

  • Biological Networks for Predicting Chemical Hepatocarcinogenicity Using Gene Expression Data from Treated Mice and Relevance across Human and Rat Species. Thomas R. et al. PloS One. (5758)
  • KEYWORDS: ASSAY DATA – CARCINOGENICITY – HEPATOTOXICITY – NETWORKS

    A new molecular pathway-based prediction model using gene expression data obtained from the liver of mice treated with a range of 26 chemicals (see Table 1) for a period of 90 days is presented, and the results are encouraging to adequately predict the hepatocarcinogenicity potential of chemicals based on extrapolation for the case of human data (see supporting material.

  • Development of Biomarkers for Screening Hepatocellular Carcinoma Using Global Data Mining and Multiple Reaction Monitoring . Kim H. et al. PLoS One. (5753)
  • KEYWORDS: BIOMARKERS – CARCINOGENESIS – HEPATOTOXICITY

    This article presents an integrative strategy (see the workflow in Figure 1) that combines global data mining and multiple reaction monitoring based on data from 3 groups: a healthy control group, patients who have been diagnosed with hepatocellular carcinoma and hepatocellular carcinoma patients who underwert locoregional therapy, which identifies 4 potential biomarkers (the actin-binding protein anillin (ANLN), filamin-B (FLNB), complementary C4-A (C4A), and the alpha-fetoprotein (AFP)) for the diagnosis of hepatocellular carcinoma. Find the list of candidate proteins obtained from global data mining and the list of potential biomarkers verified in the suplementary tables S2 and S3.

  • The Metabolomic Window into Hepatobiliary Disease. Beyoğlu D. et al. J Hepatol. (5752)
  • KEYWORDS: ASSAY DATA – BIOMARKERS – HEPATOTOXICITY – METABOLISM – SYSTEMS BIOLOGY

    A brief overview of challenges of use metabolomics data to provide new insights into liver disease mechanisms and summary of metabolomic studies examining the development of nonalcoholic fatty liver disease (see Table 1), nonalcoholic steatohepatitis (see Table 2), hepatic fibrosis and cirrhosis (see Table 3), and hepatocellular carcinoma (see Table 4), with data on species, tissue, up/down genes (which could be considered as biomarkers) regulation and mechanism/pathways related.

  • Novel in vitro and mathematical models for the prediction of chemical toxicity. Williams DJ. et al. Toxicol Res. (5736)
  • KEYWORDS: DRUG SAFETY – HEPATOTOXICITY – RISK ASSESSMENT

    Focused in the case of hepatotoxicity prediction and drug safety concerns, authors present briefings on several requirements for improved models of this endpoint and how other disciplines can benefit. Specific cases are reported (ie. acetaminophen, paracetamol).

  • A multi-scale modeling framework for individualized, spatiotemporal prediction of drug effects and toxicological risk. Diaz Ochoa JG. et al. Front Pharmacol. (5717)
  • KEYWORDS: HEPATOTOXICITY – METABOLISM – PHARMACOKINETICS – PATHWAYS

    Based on literature data, authors report the metabolic network model for metabolism and toxicity of acetaminophen drug (see Figure 1 and Modeling of acetaminophen metabolism and toxicity section). The accompanying section discuss about this proposed model, its verification and its power to help on the prediction of drug effects and toxicological risk assessment.

  • Simultaneous determination of cytochrome P450 1A, 2A and 3A activities in porcine liver microsomes. Johansson M. et al. Interdiscip Toxicol. (5713)
  • KEYWORDS: ASSAY DATA – CYP450 – HEPATOTOXICITY – PHARMACOLOGY

    This experimental report presents a robust method to identify CYP450 1A, 2A and 3A activities, based on a study performed with selective CYP450 probe substrates (7-ethoxyresorufin (CYP1A), coumarin (CYP2A) and 7-benzyloxy-4-trifluoromethylcoumarin (BFC; CYP3A)) in porcine liver microsomes.

  • Nuclear Receptors as Drug Targets in Cholestatic Liver Diseases. Halilbasic E. et al. Clin Liver Dis. (5703)
  • KEYWORDS: HEPATOTOXICITY – NUCLEAR RECEPTORS – PATHWAYS

    A comprehensive discussion on the role of some Nuclear receptors (BA, FXR, PXR, CAR, VDR, PPARs, and GR) in the pathogenesis of various cholestatic disorders (see Figure 1 for an illustration of nuclear receptors maintenance of hepatobiliary homeostasis).

  • Assessment of Emerging Biomarkers of Liver Injury in Human Subjects. Schomaker S. et al. Toxicol Sci. (5689)
    Involved Partner: PFIZER
  • KEYWORDS: BIOMARKERS – DILI – ENZYMES – HEPATOTOXICITY

    Evaluation of alternative biomarkers to the wide used alanine aminotransferase (ALT) to better predict the potential for DILI, concretely this article reports about glutamate dehidrogenase (GLDH), purine nucleoside phosphorylase (PNP), malate dehydrogenase (MDH), and paraxonase 1 (PON1).

  • Blood transcriptomics: applications in toxicology. Joseph P. et al. J Appl Toxicol. (5688)
  • KEYWORDS: HEPATOTOXICITY – SYSTEMS BIOLOGY

    Brief review on challenges emerging from the blood transcriptomics analysis in the framework of toxicology. Since blood has several advantages over other surrogate tissues, and with small quantities one can yield adequate quantities of high-quality RNA required for gene expression profiling, authors discuss about the blood gene expression insights in two toxicity cases, hepatotoxicity and pulmonary toxicity, as potential examples for risk assessment.

  • Sandwich-cultured hepatocytes: utility for in vitro exploration of hepatobiliary drug disposition and drug-induced hepatotoxicity. De Bruyn T. et al. Expert Opin Drug Metab Toxicol. (5685)
  • KEYWORDS: ADME – CYP450 – ENZYMES – HEPATOTOXICITY – TRANSPORTERS

    A model of Sandwich-cultured hepatocyte is presented as an in vitro tool in order to explore the hepatic drug transport, metabolism biliary excretion and toxicity. Details are provided on the cel culture condition specifications depending on the species under study (see Table 1, 2, and 3), and the list of effects observed during the culture time on the expression and functionality of transport proteins (Table 4) and metabolizing enzymes (Table 5). In addition, some drug-drug interactions investigated are reported (see Table 6).

  • Comprehensive assessment of human pharmacokinetic prediction based on in vivo animal pharmacokinetic data, part 1: volume of distribution at steady state. Lombardo F. et al. J Clin Pharmacol. (5681)
  • KEYWORDS: DILI – HEPATOTOXICITY – PATHWAYS

    Review on the mechanisms involved in the idiosyncratic DILI (reactive metabolites and mithocondria disfunction), the signaling pathways involved in hepatotoxicity (adaptive signaling pathways, mitochondrial adaptation to drugs, sensitization of hepatocytes to the adaptive immune system and/or innate immune system, and activation of death signaling pathways by idiosyncratic toxicans).

  • Drug-Induced Liver Injury: The Role of Drug Metabolism and Transport. Corsini A. et al. J Clin Pharmacol. (5679)
  • KEYWORDS: DRUG SAFETY – CYP450 – DILI – ENZYMES – HEPATOTOXICITY – METABOLISM – TRANSPORTERS

    Briefings on the current knowledge about hepatic transporters proteins (influx transport and eflux transport, see Figure 1), and the different factors that can increase the susceptibility to DILI occurrence (genetic susceptibility in individuals (based on genetic expression of CYP450, UGTs, NAT2, GST, MnSOD, ABC transporters, and SCL transporters), adaptive immunological mechanisms, drug-drug interactions, and underlying diseases, see Figure 2).

  • Time series analysis of oxidative stress response patterns in HepG2: A toxicogenomics approach. et al.. (5672)
  • KEYWORDS: HEPATOTOXICITY – NETWORK – PATHWAYS – TOXICOGENOMICS

    136 genes (from a total of 3429) were identified as the common set of genes significantly modified by 3 oxidants evaluated (menadione, hydrogen peroxide and tert-butyl hydroperoxide) in HepG2 cells at seven time points (0.5, 1, 2, 4, 6, 8 and 24h). Table 1 provides the most significantly regulated pathways and transciption factor regulation networks annotated to the common 136 genes.

  • HepG2 cells simultaneously expressing five P450 enzymes for the screening of hepatotoxicity: identification of bioactivable drugs and the potential mechanism of toxicity involved. Tolosa L. et al. Arch Toxicol. (5671)
  • KEYWORDS: ASSAY DATA – CYP450 – HEPATOTOXICITY

    Experimental data is provided for a set of 12 bioactivable and 3 non-activable compounds regarding their activity against a set of 5 CYP450 (1A2, 2D6, 2C9, 2C19 and 3A4), see Table 1 for P450 involved and hepatotoxicity related mechanisms (apoptosis, calcium homeostasis, mithocondrial impairment and oxidative stress), Table 2 for the IC50 values incomponent (ADV) and non-component (HepG2) cells, Table 3 for the cytotoxic effects in metabolically competent (ADV) and non-component (HepG2) cells after 24 h of treatment.

  • A Unifying Ontology to Integrate Histological and Clinical Observations for Drug-Induced Liver Injury. Wang Y. et al. Am J Pathol. (5668)
  • KEYWORDS: DATABASE – HEPATOTOXICITY – TEXT MINING

    The details to develop the first ontology on histological and clinical observations focused on DILI event (based on SNOMED CT terminology, see Support Material) is presented as key tool to map histopathologic findings identified in the literature to annotate drugs onto a set of DILI related events. Based on retrieved information from querying PubMed with a set of 114 drugs from LTKB databse ([drug name] AND (hepatotoxicity OR liver injury) AND (histopathology OR liver biopsy), and the use of MetaMap, a UMLS tool, a new ontology is generated, Comparison of data from 3 databases (319 (Susuki et al), 626 (Greene et al) and 287 (LTKB) compounds) sorts out that 5 of the drugs evaluated are annotated to >10 terms reported in the description of histopathologic features (see Table 1).

  • 2011 Annual Meeting of the Safety Pharmacology Society: an overview. Kostadinova R. et al. Toxicol Appl Pharmacol. (5657)
    Involved Partner: ROCHE
  • KEYWORDS: DRUG SAFETY – HEPATOTOXICITY – RISK ASSESSMENT

    See Table 2 for expression of inflammatory factors and ECM components (10 interleukins, 3 protaglandins, 12 acute phase proteins, 2 transcription factors, 2 proteoglycans, 3 glycoproteins and 48 others) in LPS and LPS/Dex-treated human 3D liver co-cultures.

  • Bridging the gap between old and new concepts in drug-induced liver injury. Fromenty B. et al. Clin Res Hepatol Gastroenterol. (5648)
  • KEYWORDS: HEPATOTOXIXITY

    Commentary article that sums up new concepts related with DILI mechanism published recently in different papers, which underpin new kind of investigations to be addressed. Discussion is conducted in the case of the thiacetamide and acetomiphen drugs and the role of 2-aminoethoxyphenyl borate and connexin 32 activity.

2012

  • Liver Specificity of the Carcinogenicity of NOCs: A Chemical-Molecular Perspective. Yuan J. et al. Chem Res Toxicol. (5632)
  • KEYWORDS: ASSAY DATA – CARCINOGENICITY – HEPATOTOXICITY – QSAR MODELING

    In order to understand the liver-specific carcinogenicity of N-nitroso compounds, a set of 111 compounds (see Table 1 for CAS, SMILES and corresponding classification of the N-Nitroso types) were evaluated at structural level using a topological substructural molecular descriptor (TOPS-MODE) to identify molecular features associated with the liver specificity of the carcinogenicity. The results show that both chemical and biological factors contribute to their specificity, and the application of TOPS-MODE approach can help in the assessment from the chemical-molecular point of view..

  • Modeling drug- and chemical-induced hepatotoxicity with systems biology approaches. Bhattacharya S. et al. Front Physiol. (5629)
  • KEYWORDS: HEPATOTOXICITY – SYSTEMS BIOLOGY

    3 case studies (causal transcriptional network inference, agent-based model fo the human liver, ODE-based mechanistic multi-species model of the liver) exemplify the application of computational systems biology approaches, in the field of the drug- and chemical-induced hepatotoxicity.

  • A pre-marketing ALT signal predicts post-marketing liver safety. Moylan CA. et al. Regul Toxicol Pharmacol. (5596)
  • KEYWORDS: ASSAY DATA – DILI – ENZYME – HEPATOTOXICITY

    Based on data of 36 new drug approvals between 2001 and 2006 (see Table 1 for drug names, daily dose and relevant information from pre-marketing clinical trials), authors point out that a low and similar elevation of Serum Alanine Aminotransferase (ALT) over or equal to 3 x Upper limits normal (ULN) suggests a favorable post-marketing liver safety profile.

  • Preclinical strategy to reduce clinical hepatotoxicity using in vitro bioactivation data for >200 compounds. Sakatis MZ. et al. Chem Res Toxicol. (5595)
    Involved Partner: GSK
  • KEYWORDS: ASSAY DATA – DILI – HEPATOTOXICITY

    Analysis of data from a series of 223 marketed drugs (51% associated with clinical hepatotoxicity, and 49% non-hepatotoxic) shows that 76% of drugs with a daily dose of <100 mg are non-hepatotoxic (p < 0.0001). Drugs with a daily dose of ≥100 mg or with GSH adduct formation, marked P450 MDI (CYP450 (1A2, 2C9, 2C19, 2D6, 3A4)), or covalent binding ≥200 pmol eq/mg protein tended to be hepatotoxic (see Table 2 for drug names, dose, P450 MDI, GSH Adduct and Covalent Binding Data for Clinical Hepatotoxins and Non-hepatotoxins, and the progression decision determined using the Decision Tree presented in Figure 3, see also Table 3 for a summary of Decision-Making parameters used to develop the Decision Tree).

  • A Perspective on Efflux Transport Proteins in the Liver. Muller PY. et al. Clin Pharmacol Ther. (5591)
  • KEYWORDS: DILI – HEPATOTOXICITY – P-gp – PHARMACOKINETICS – TRANSPORTERS

    Detailed knowledge for a series of efflux transport proteins in liver depending on their location: Apical/canalicular (BSEP, P-gp, MRP2, BCRP, MATE1, MATE2), and Basolateral/sinusoidal (MRP3, MRP4, MRP5, OSTalpha/beta) transporters (see Figure 1). Based on some examples of drug-induced site effects, a brief discussion on hepatic efflux transport proteins as determinants of pharmacokinetics, efficacy and side effects of drugs is presented for the case of Drug-induced enteropathy diarrhea and DILI. Authors point out future directions and challenges in the use of cellular models, specific probes and inhibitors, in vivo imaging techniques or mass spectrometry imaging, and the successful evaluation of in vitro-in vivo data correlations.

  • Prediction of Liver Injury Induced by Chemicals in Human With a Multiparametric Assay on Isolated Mouse Liver Mitochondria. Porceddu M. et al. Toxicol Sci. (5584)
  • KEYWORDS: ASSAY DATA – DILI – HEPATOTOXICITY – MITOCHONDRIAL TOXICITY

    This article provides the experimental data of the effect on several parameters concerning mitochondrial toxicity for a set of 124 chemicals, mainly drugs, 87 with documented DILI and 37 without reported clinical hepatotoxicity (see Table 1 for names and therapeutic class of drugs). Authors present a multiparametric assay as a helpful tool to elucidate the mechanisms of action of marketed compounds focused on their mitochondrial toxicity profile.

  • Comparative analysis of eight cytotoxicity assays evaluated within the ACuteTox project. Clothier R. et al. Toxicol In Vitro. (5563)
  • KEYWORDS: ASSAY DATA – HEPATOTOXICITY – NEPRHOTOXICITY – NEUROTOXICITY – PROJECT

    A comparative clustering analysis of 8 cytotoxicity assays [the 3T3 and normal human keratinocytes neutral red uptake (NRU) assay, the primary rat hepatocytes, human HepG2 and 3T3 MTT assay, and the human A.704, SH-SY5Y and HepG2 cells propidium iodide (PI) assay] included in the EU Integrated Project ACuteTox, is presented as evaluation of possible differences of in vitro acute toxicity results between cells originating from liver, kidney and brain. Data for 57 chemicals evaluated is provided in Table 1.

  • Overtaking the DILI Model-T. Opar A. Nat Rev Drug Discov. (5554)
    eTOX cited
  • KEYWORDS: DILI – PROJECT

    A brief description of a new Innovative Medicines Initiative project, called MIP-DILI, which will contribute to the need of better understanding of what causes liver injury and who’s at risk. Other current toxicity-driven collaborations (SAFE-T (extension of InnoMed PredTox), Virtual Lvier, DILI-Sim, eTOX, DILIN) are mentioned as they are other initiatives and ongoing contributions to find better ways to predict patient outcome.

  • A pre-marketing ALT signal predicts post-marketing liver safety. Moylan CA. et al. Regul Toxicol Pharmacol. (5542)
    Involved Partner: GSK
  • KEYWORDS: ASSAY DATA – DILI – DRUG SAFETY – HEPATOTOXICITY – PHARMACOLOGY

    Table 1 reports information on ALT levels for 36 new drug approvals between 2001 and 2006 for daily doses smaller or bigger than 50mg.

  • Prediction of organ toxicity endpoints by QSAR modeling based on precise chemical-histopathology annotations. Myshkin E. et al. Chem Biol Drug Des. (5539)
  • KEYWORDS: DATABASE – HEPATOTOXICITY – NEPHROTOXICITY – QSAR MODELING – STRUCTURE-BASED PREDICTION – TEXT MINING

    This article presents 8 QSAR models developed (see tables 1-5 for models details) based on compound-toxicity annotations thanks to an organ focused ontology of toxic pathologies created (see Figure 1 and Database of chemical toxicity section for details of ontology generation). Authors built models to predict more defined subcategories of organ toxicity (liver necrosis, liver relative weight gain, liver lipid accumulation) and compare with models predicting more general organ toxicity as hepatotoxicity, and similarly in the case of nephrotoxicity and is subcategories (kidney necrosis, kidney relative weight gain, nephron injury). See Table 6 for full reference and models details of 16 published organ toxicity QSAR models.

  • Comparative analysis of perturbed molecular pathways identified in in vitro and in vivo toxicology studies. Wiesinger M. et al. Toxicol In Vitro. (5500)
  • KEYWORDS: HEPATOTOXICITY – NEPHROTOXICITY – NEUROTOXICITY – PATHWAYS – TEXT MINING

    Based on literature search for genes or proteins (see Table 1 for PubMed search terms) associated with in vitro and in vivo nephrotoxicity, hepatotoxicity or neurotoxicity, this study reports a list of 1221 unique human genes (see support material ) associated to these 3 toxicity endpoints, where heme oxygenase-1, nitric oxide synthetase 2, NFjB1 and p53 were found common for all tissue and experimental conditions. Comparison with KEGG pathways (see Table 5) outcomes with a set of 17 relevant pathways for kidney, 26 for liver, and 30 for central nervous system (see Table 4).

  • Identification of a novel set of biomarkers for evaluating phospholipidosis-inducing potential of compounds using rat liver microarray data measured 24-h after single dose administration. Yudate HT. et al. Toxicology. (5485)
  • KEYWORDS: ASSAY DATA – BIOMARKERS – HEPATOTOXICITY – PATHWAYS – PHOSPHOLIPIDOSIS – TOXICOGENOMICS

    This article presents a set of 25 biomarkers, validated by 11 independent compounds, for predicting the phospholipidosis-inducing potential based on transcriptomic data extracted from the TGP/TGP2 database. The training set and independent validation set (see Table 1 for names, dose, vehicle, route and phospholipidosis-inducing potential), the histopathological finding for 6 positive training set (see Table 2 for names, findings and topography), the list of 25 probe sets for evaluation of phospholipidosis-inducing potential at 24h following single dose oral administration (see Table 3), and the pathways analysis results based on Ingenuity pathways information, are provided as relevant information emerged from this study.

  • In vitro transcriptomic prediction of hepatotoxicity for early drug discovery. Cheng F. et al. J Theor Biol. (5460)
  • KEYWORDS: BIOMARKERS – DRUG DISCOVERY – ENZYMES – HEPATOTOXICITY

    Report of the development and application of a novel genomic prediction technique for screening hepatotoxic compounds based on in vitro human liver cell tests. This study compiles a set of 32 selected toxicity related genes and their correlations with ALT level in rat (see Table 3), and highlights 8 (MGMT, CCL5, RAN, CYP26B1, KDR, VTN, MASP1 and NR1H4) as directly related to liver toxicity.

  • Mixed learning algorithms and features ensemble in hepatotoxicity prediction. Liew CY. et al. J Comput Aided Mol Des. (5457)
  • KEYWORDS: HEPATOTOXICITY – MOLECULAR DESCRIPTORS – STUCTURE-BASED PREDICTION

    An ensemble model based on mixed learning algorithms and mixed features (see Figure 3) is presented as a new alternative to predict hepatic effects. 1685 compounds were collected from the FDA orange book and filtered using the information of Micromedex Healthcare Series reports about adverse hepatic effects. A cleaning process (compounds with unclear hepatic effects, duplicates, combination products, inorganic compounds, and compounds with molecular weight greater than 5000) resulted in a set of 1274 compounds, divided in 187 compounds as external validation sets and 1087 (654 positives and 433 negatives) were finally used to model building (see Figure 1). Authors provides a brief overview of different methodologies: Support vector machine, k-Nearest neighbor, Naive Bayes, … and evaluate the applicability domain of this ensemble method.

  • Proteomics in the search for mechanisms and biomarkers of drug-induced hepatotoxicity. Summeren A.V. et al. Toxicol in Vitro. (5455)
  • KEYWORDS: ASSAY DATA – BIOMARKERS – HEPATOTOXICITY

    This comprehensive review on proteomics applied to elucidate the drug-induced hepatotoxicity mechanism, provides, apart from a review of current proteomic techinques (gel-based proteomics: 2-dimensional gel electrophoresis, blue native gel electrophoresis; Liquid chromatography tandem mass spectrometry: isotope-code labeling shotgun technologies, label-free quantification; and Targeted proteomics), a summary of known hepatotoxic proteome studies in vivo and in vitro for mouse, rat and human (see Table 2 for chemical names, cells/organelles,applied technique,observations and reference details).

2011

  • Translating Clinical Findings into Knowledge in Drug Safety Evaluation – Drug Induced Liver Injury Prediction System (DILIps). Liu Z. et al. Plos Comput Biol. (5448)
    Data available at ChOXdb
  • KEYWORDS: ASSAY DATA – DATABASES – DILI – DRUG SAFETY – HEPATOTOXICITY – QSAR MODELING – TEXT-MINING

    A new predictive system devoted to drug induced liver injury prediction (DILIps) is presented as a recent example of challenges to bridge preclinical and clinical findings in the case of hepatotoxicity data (extracted from SIDER database (888 drugs), DrugBank database (6620 drugs), and three literature datasets: LTKB benchmark set (287 drugs), Pfizer hepatotoxicity set (626 compounds), and O’Brien set (135 drugs); see Support Material table S6). Based on 13 hepatotoxic side effects identified (bilirubinemia, cholecystitis, cholelithiasis, cirrhosis, elevated liver function tests, hepatic failure, hepatic necrosis, hepatitis, hepatomegaly, jaundice, liver disease, liver fatty, liver function tests abnormal) and the ‘Rule of three’ (consider drugs data when they are incriminated by 3 or more hepatotoxic side effects defined), the respective models were built in order to distinguish prediction between different findings described (see Table 1). The list of PMIDs as literature proof about the co-ocurrence between the 13 hepatotox side effects and protein targets are provided as result of text-mining approaches application.

  • Evaluation of Drugs with Specific Organ Toxicities in Organ Specific Cell Lines. Lin Z. et al. Toxicol Sci. (5443)
    Involved Partner: PFIZER
  • KEYWORDS: ASSAY DATA – CARDIOTOXICITY – HEPATOTOXICITY – NEPHROTOXICITY

    Based on data for 273 hepatotoxic, 191cardiotoxic, and 85 nephrotoxic compounds in HepG2 (hepatocellular carcinoma), H9c2 (embryonic myocardium), and NRK-52E (kidney proximal tubule) cells for evaluation of their cytotoxicity, the results of this study show how select organ toxicity potentially results form compound accumulation in a particular tissue, cell types within organs, metabolism and off-target effects, and highlights that consideration of Cmax values improves significantly the prediction of toxicities.

  • Mixed learning algorithms and features ensemble in hepatotoxicity prediction. Liew CY. et al. J Comput Aided Mol Des. (5427)
  • KEYWORDS: ASSAY DATA – HEPATOTOXICITY – STRUCTURE-BASED PREDICTION

    An ensemble model to predict hepatic effects based on a diverse set of 1807 compounds is presented as alternative to make predictions as an optimal solution for all modelling problems that separate models would show. Authors made available both the data set and the software based on their ensemble model for public use at http://padel.nus.edu.sg/software/padelddpredictor/ and provide a compiled list of other studies conducted for hepatotoxicity prediction case (see Table 4 for different enpoints addressed).

  • Development of an in vitro liver toxicity prediction model based on longer term primary rat hepatocyte culture. Hrach J. et al. Toxicol Lett. (5421)
  • KEYWORDS: ASSAY DATA – ENZYMES – HEPATOTOXICITY – TOXICOGENOMICS

    According to the genomic profile of compounds, authors shows how combination of hepatocyte sandwich culture and global gene expression analysis can help to define a model for hepatotoxicity prediction for unknown compounds. This study provides the top ‘hit’ genes with some role in hepatotoxicity events and their known function (see Table 2: Proteasome complex, cyclin-dependent kinase (Cdk7) or s-phase related proteins, Myc, Egf, Map kinase activated protein kinase 2 (Mapkapk2), Tgfβ2 and kappaB kinase, inhibitor (Ikbkb), Lactate dehydrogenase B (Lahb), triosephosphate isomerase (Tim) and enolase, ATP synthase C1 and subunits, cytochrome c reductases NADH dehydrogenases, Junctiona adhesion molecules 3 (Jan3) and claudin 10, CYP1A1, CYP2A1 and Microsomal Gst 2).

  • Prediction model of potential hepatocarcinogenicity of rat hepatocarcinogens using a large-scale toxicogenomics database. Uehara T. et al. Toxicol Appl Pharmacol. (5373)
  • KEYWORDS: ASSAY DATA – CARCINOGENICITY – HEPATOTOXICITY – TOXICOGENOMICS

    The set of 150 compounds gathered in the database TG-GATEs (Genomics-Assisted Toxicity Evaluation System) have been considered to develop a robust gene-based (Abcb1a/1b, Cd276, Ica1, Tmem184c, Acot9, Tes and Cdh13) model to predict genotoxic or non-genotoxic potential of hepatocarcinogens based on data of 28 day study in rats (see Table 1 for chemicals names, and Support Material Table 1 for detailed experimental conditions).

  • Predicting Drug-induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches. Low Y. et al. Chem Res Toxicol. (5371)
  • KEYWORDS: ASSAY DATA – DILI – HEPATOTOXICITY – MOLECULAR DESCRIPTORS – QSAR MODELING – TOXICOGENOMICS

    Based on data from a 28-day study on rats for a set of 127 drugs (chemical features plus toxicogenomic data; see support material for chemical names, CAS number, dosage, administration route and vehicle, histopathology , serum chemistry (ALT, AST, ALP, TBIL, DBIL and GGT enzymes), list of predictive gene biomarkers and the involving pathways, and list of compounds paired by chemical and transcriptional similarities information), the resulting model generated evidences the enrichment of using different descriptors and toxicogenomics profiles to tackle drug-induced hepatotoxicity prediction.

  • Lead detoxification activities and ADMET hepatotoxicities of a class of novel 5-(1-carbonyl-L-amino-acid)-2,2-dimethyl-[1,3]dithiolane-4-carboxylic acids. Xu Y. et al. Bioorg Med Chem Lett. (5368)
  • KEYWORDS: ADME – ASSAY DATA – HEPATOTOXICITY

    This article presents ADME data regarding a novel series of 12 compounds that thanks to their transmembrane ability are effective in lowering the organs’ lead level of the mice (femurs, brain and kidney) and reports in silico prediction of their non-hepatotoxin character (see Table 1 for values of lead in organs of the treated mice, tables 3 and 4 for permeability coefficients and ADME hepatotoxicity probability, respectively; and support material for experimental details and physical chemical constants).

  • Comparative Analysis of Predictive Models for Nongenotoxic Hepatocarcinogenicity Using Both Toxicogenomics and Quantitative Structure–Activity Relationships. Liu Z. et al. Chem Res Toxicol. (5361)
  • KEYWORDS: CARCINOGENESIS – HEPATOTOXICITY – QSAR MODELING – TOXICOGENOMICS

    Data of 5288 microarrays associated with animals treated with 344 chemicals from Gene Expression Omnibus database has been considered to compare results of predictivity taking into account toxicogenomics data (see information in Table 2) or applying QSAR methodology (see Table 1 for chemical names, CAS numbers, dosage and classification).

  • Phthalates: Toxicogenomics and inferred human diseases. Singh S. et al. Genomics. (5263)
  • KEYWORDS: ASSAY DATA – CARDIOTOXICITY – CYP450 – DATABASES – HEPATOTOXICITY – NEPHROTOXICITY – NUCLEAR RECEPTORS – PHARMACOLOGY – PATHWAYS – SYSTEMS BIOLOGY – TOXICOGENOMICS

    Focused on a group of 16 phthalates, the Comparative Toxicogenomics Database (CTD) is consulted to identify interactions between genes/proteins and these compounds (see Table 1 for a list of compounds evaluated, Table 2 for the list of the 34 most frequent genes/proteins connected to them (basically Nuclear Receptors, CYP450s, GPCRs and Ion channels). Analysis of these results suggests that Cardiotoxicity, Hepatotoxicity and Neprhotoxicity are expected to be the top toxicity categories for this series of compounds and points out the need to check this list of proteins as a potential biomarkers of phthalate toxicity.

  • FDA-approved drug labeling for the study of drug-induced liver injury. Chen M. et al. Drug Discov Today. (5347)
    Data available at ChOXdb
  • KEYWORDS: DILI – HEPATOTOXICITY – REGULATORY GUIDELINES – TEXT MINING
    Based on the FDA benchmark dataset of 287 drugs (see Support Material for drugs related information) which represents a diverse collection of therapeutic categories and a fully covered dosage range (see Figure 3), the authors propose a systematic classification scheme applying FDA-approved drug labeling to assess the Drug-induced liver injury.

2010

  • Non-steroidal anti-inflammatory drugs: What is the actual risk of liver damage?. Bessone F. World J Gastroenterol. (5242)
  • KEYWORDS: DRUG SAFETY – HEPATOTOXICITY

    Based on epidemiologic studies, this article reviews the current knowledge of induced liver damage concerning a series of non-steroidal anti-inflammatory drugs (aspirin, diclofenac, sulindac, ibuprofen, coxibs, oxicams, and nimesulide) prescription.

  • Risk assessment and mitigation strategies for reactive metabolites in drug discovery and development. Thompson RA. et al. Chem Biol Interact. (5230)
    Involved Partner: AZ
  • KEYWORDS: DRUG DISCOVERY – HEPATOTOXICITY – RISK ASSESSMENT

    Authors propose use of an in vitro Hepatic Liability Panel (THLE cell toxicity, HepG2 cytotoxicity in galactose vs glucose medium and inhibition of human BSEP) alongside in vitro methods for the detection of reactive metabolites (RM) distinguishing between drug-related and patient-related factors (see Figure 1). An interesting discussion is reported regarding strategies for RM avoidance and minimization and the key components of an integrated RM safety hazard assessment in case of hepatotoxicity drug safety. An application of this strategy is shown by means of an internal study about troglitazone, rosiglitazone and pioglitazone drugs prescribed for Type 2 diabetes.

  • The enhanced value of combining conventional and “omics” analyses in early assessment of drug-induced hepatobiliary injury. Ellinger-Ziegelbauer H. et al. Toxicol Appl Pharmacol. (5213)
    Involved Partners: BSP, SAD, ROCHE, BI
  • KEYWORDS: ASSAY DATA – HEPATOTOXICITY – RISK ASSESSMENT – TOXICOGENOMICS

    The InnoMed PredTox consortium presents a comprehensive example of combination of different ‘omics’ analyses applied to early assess of a hepatotoxicity event, drug-induced hepatobiliary injury, based in data from short-term toxicological studies in rats. Authors provide a list of 34 representative examples of genes found to be characteristically deregulated in response to drugs associated with hepatobiliary toxicity (see Table 1 and support material).

  • Application of toxicogenomics in hepatic systems toxicology for risk assessment: Acetaminophen as a case study. Kienhus AS. et al. Toxicol Appl Pharmacol. (5203)
  • KEYWORDS: HEPATOTOXICITY – RISK ASSESSMENT – TOXICOGENOMICS

    Based on acetaminophen case study, authors discuss about the application of the integration of toxicogenomics techniques (transcriptomics, proteomics, and metabolomics) with traditional toxicology measures (clinical chemistry, histopathology, etc) in hepatotoxicity concerns to take advantatge of its potential role in the risk assessment process. They provide an schematic overview of the risk assessment paradigm for hepatic systems toxicology (see Figure 2) and the toxicogenomics-based parallelogram (see Figure 3), together with a compilation of several toxicogenomics in vivo and in vitro studies (see tables 1 and 2, respectively). Finally they also shortly discuss about quantitative dose-response modeling in risk assessment for this compound.

  • An integrated reactive metabolite evaluation approach to assess and reduce safety risk during drug discovery and development.Reese M. et al. Chem Biol Interact. (5200)
  • KEYWORDS: CYP450 – DRUG DISCOVERY – ENZYMES – HEPATOTOXICITY – METABOLISM – RISK ASSESSMENT

    A set of 114 hepatotoxic and 111 non-hepatotoxic of marketed drugs with in vitro bioactivation data and clinical dose information (GSH adduct assay, CYP MDI experiments, Microsomal covalent binding analysis, Hepatic Nrf2 gene expression) is analyzed to check their concordance with clinical hepatotoxicity and to develop an early reactive metabolite strategy. The results highlight the significant association of clinical dose and GSH adduct formation with hepatotoxicity and also demonstrate that a high percentage of compounds with covalent binding >200 pmol/mg were hepatotoxic in man. Strategies towards attenuation of metabolic bioactivation risk should reduce compound attrition and provide safer drugs.

  • Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method. Rodgers AD. et al. Chem Res Toxicol. (5171)
  • KEYWORDS: ASSAY DATA – ENZYMES – HEPATOTOXICITY – MOLECULAR DESCRIPTORS – QSAR MODELING

    Based on data (see support material files) from the Human Liver Adverse Effects Database with approximately 500 compounds with five serum enzyme markers of toxicity information (alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), and γ-glutamyl transpeptidase (GGT)), the development of QSAR models predictive of human liver adverse effects of drugs is presented (see Figure 1 for their workflow proposal), and results suggest that they should be considered as complementary information to prioritize compounds for preclinical screening.

  • Genomic indicators in the blood predict drug-induced liver injury. Huang J. et al. Pharmacogenomics J. (5116)
  • KEYWORDS: BIOMARKERS – DILI – HEPATOTOXICITY – PATHWAYS

    Based on previously published set of gene expression data, this article stands a list of 10 identified genes (see Table 2) and a list of 30 biological processes in the blood (see Table 4) that are predictive of liver necrosis.

  • Developing Structure-Activity Relationships for the Prediction of Hepatotoxicity. Greene N. et al. Chem Res Toxicol. (5091)
    Involved Partners: PFIZER, LL

    Data available at ChOXdb
  • KEYWORDS: DATABASES – DILI – HEPATOTOXICITY – STRUCTURE-BASED PREDICTION

    Based on the reference book ‘The Adverse Effects of Drugs and Other Chemical on the Liver‘ and some reviews of hepatotoxicity data, the authors build a structure-searchable database with 1116 chemicals extended to 1266 (see Supplementary Material for names and CAS numbers) after first stage of structural alerts creation using Derek for Windows (DdW). Their approach was evaluated with a Pfizer-developed set of 626 compounds (see Supplementary Material for names, CAS numbers and classification associated to hepatotoxicity [NE (no evidence for hepatotoxicity in any species, 152 total), WE (weak evidence for human hepatotoxicity, 62 total), AH (animal hepatotoxicity observed, not tested in humans, 139 total), and HH (evidence for hepatotoxicity in humans, 273 total)] which has significant overlap and a similar classification schema to a set of 344 compounds described in the literature consulted.

  • Cheminformatics Analysis of Assertions Mined from Literature That Describe Drug-Induced Liver Injury in Different Species. Fourches D. et al. Chem Res Toxicol. (5075)
    Data available at ChOXdb
  • KEYWORDS: ASSAY DATA – DILI – HEPATOTOXICITY – QSAR MODELING – STRUCTURE-BASED PREDICTION – TEXT MINING

    An extensive example of combining text mining and cheminformatics data analysis with Drug-induced Liver Injury (DILI) data is presented in this paper, based on a data set of 951 compounds reported to produce a wide range of effects in the liver in different species (human, rodents, non-rodents) in 650000 MEDLINE records compiled, using BioWisdom’s Sofia platform. The good predictive powder of the QSAR models generated with this data opens up new opportunities for generating and modeling chemical toxicology data.

  • Hepatotoxicity and hepatic metabolism of available drugs: current problems and possible solutions in preclinical stages. Giri S. et al. Expert Opin Drug Metab Toxicol. (5052)
  • KEYWORDS: DRUG SAFETY – HEPATOTOXICITY – METABOLISM – MITOCHONDRIAL TOXICITY

    Based on a set of 900 drugs, this review highligths the unexpected evidence that more than 64 allopathic drugs can induce potentially life-threatening hepatotoxicity with diverse clinical features, and lists the associated pattern hepatotoxicity for 36 of these drugs (see Table 1). Since chemically reactive metabolites have been proposed to be responsible for most types of drug-induced injury, their discussion focuses on drug metabolites formed during biotransformation in a widely accepted organotypical in vitro cellular model with relevance to in vivo two-compartment approaches. They stand out that drug-induced hepatoxicity depends on many factors, such as the host’s metabolic condition, age, sex, nutritional factors, multiple medications, physiological changes (pregnancy and renal impairment), genotype, duration and dosage of drug-enzyme induction, among others; and explain the different patterns of drug-induced hepatoxicity by these six basic mechanisms: i) disruption of calcium homeostasis leading to cell surface blebbing and lysis; ii) canalicular injury; iii) metabolic bioactivation of chemicals via CYP to reactive species; iv) stimulation of autoimmunity; v) stimulation of apoptosis and vi) mitochondrial injury.

2009

  • The future of drug safety testing: expanding the view and narrowing the focus. Stevens JL. et al. Drug Discov Today. (5009)
  • KEYWORDS: CARDIOTOXICITY – DRUG SAFETY – HEPATOTOXICITY – NEUROTOXICITY

    An expanded view on the application of predictive strategies and technologies to early safety decisions and suggestions to narrow the focus for improving preclinical safety testing to the problems that contribute most to adverse drug reactions. Their recommendations go in the direction of reducing three primary causes of toxicity (hepatic, cardiac and neurological) to reduce risk during clinical, reducing the drug withdrawals for cardiac or liver safety concerns to decrease withdrawals, and finally making efforts to improve preclinical testing with studies of 30-day duration or less.

2008

  • Overview: Evaluation of Metabolism-based Drug Toxicity. Li AP. Chem Biol Interact. (3639)
  • KEYWORDS: CYP450 – HEPATOTOXICITY – METABOLISM

    Since a clear understanding of the role of drug metabolism in toxicity can aid the identification of risk factors that may potentiate drug toxicity, and may provide key information for the development of safe drugs, this overview of different approaches to evaluate metabolism-based drug toxicity is recommended.

2007

  • Mitochondrial abnormalities—A link to idiosyncratic drug hepatotoxicity?. Boelsterli UA. et al. Toxicol Appl Pharmacol. (5042)
  • KEYWORDS: DRUG SAFETY – HEPATOTOXICITY – MITOCHONDRIAL TOXICITY – PHARMACOLOGY

    In one hand, this article reviewed recent concepts of idiosyncratic DILI and in the other hand, presented some commonly adopted working hypotheses which, however, were not always fully compatible with the clinical picture and pathogenesis of DILI. The key issue of that hypothesis paper was to address the possible role of underlying genetic and/or acquired abnormalities in mitochondrial function as possible susceptibility factors for hepatic drug idiosyncrasy. See Table 1 for a compilation of drugs associated with idiosyncratic DILI that exhibit a clear mitochondrial hazard, and Table 2 for a list of clinical evidence linking DILI with mitochondrial dysfunction.

2006

  • Drug-Related Hepatotoxicity. Navarro VJ. et al. N Engl J Med. (5045)
  • KEYWORDS: DRUG SAFETY – HEPATOTOXICITY

    This article provides information on the detection, evaluation, possible prevention, and management of drug-related hepatotoxicity since it may not occur during clinical trials, which are usually limited to a few thousand participants and recent studies show that after approval of a drug for use and subsequent marketing, large numbers of patients are exposed, and non-negligible cases appear.

2003