2014

2014

December

November

October

September

August

July

June

May

  • GWAS and drug targets. Cao C. et al. BCM Genomics. (6004)
  • KEYWORDS: PHARMACOLOGY – SYSTEMS BIOLOGY

    This article reports the following interesting information:
    – Table 1: overlap between GWAS reported genes and drug targets for a list of diseases
    – Table 4: machine learning results for different diseases, using a Random forest model
    – Table 5: top ‘false positive’ drug targets for acute lymphoblastic leukemia, as a case study

  • 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.

  • Photochemical toxicity of drugs intended for ocular use. Sahu RK. et al. Arh Hig Rada Toksikol. (6002)
  • KEYWORDS: ASSAY DATA – PHOTOTOXICITY

    Find mean score values and irritancy classification of drugs in Table 6.

  • 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).

  • Data mining reveals a network of early-response genes as a consensus signature of drug-induced in vitro and in vivo toxicity. Zhang JD. et al. Pharmacogenomics J. (5999)
  • KEYWORDS: DATABASES – DATA MINING – SYSTEMS BIOLOGY

    A multivariate analysis of gene expression, cell-based assays, and histopathological data extracted from the TG-GATEs (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation system) database is presented as an alternative to identify gene signatures of drug-induced toxicity. The analysis retrieved four genes—EGR1, ATF3, GDF15 and FGF21—that are induced 2 h after drug administration in human and rat primary hepatocytes poised to eventually undergo cytotoxicity-induced cell death.

  • Evaluation of toxicogenomics approaches for assessing the risk of nongenotoxic carcinogenicity in rat liver. Eichner J. et al. PLoS One. (5998)
  • KEYWORDS: CARCINOGENICITY – DATABASE – RISK ASSESSMENT

    A new toxicogenomics methodology is presented in order to prioritize compounds for long term carcinogenicity assays. The approach presented is based on data from TG-GATEs database for a set of 24 compounds (see Table 1 for compound names and genotoxic/non-genotoxic potential).

  • Liposome electrokinetic chromatography based in vitro model for early screening of the drug-induced phospholipidosis risk. Wang T. et al. J Pharm Biomed Anal. (5997)
  • KEYWORDS: ASSAY DATA – PHOSPHOLIPIDOSIS – RISK ASSESSMENT

    Find phospholipidosis risk data measured by different models in Table 1 for a series of 77 compounds, and check support material.

  • 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.

  • Investigating the different mechanisms of genotoxic and non-genotoxic carcinogens by a gene set analysis. Lee WJ et al. PLoS One. (5995)
  • KEYWORDS: CARCINOGENICITY – GENOTOXICITY – RISK ASSESSMENT – SYSTEMS BIOLOGY

    An example of how gene set analysis (data extracted from Gene Expression Omnibus for a training (13 compounds) and test (22 compounds) sets) can help on the elucidation of different mechanisms that can explain genotoxic and non-genotoxic potential of carcinogen substances (see Table 1 for compounds names and genotoxic/non-genotoxic classification).

  • Integration of bioinformatics to biodegradation. Arora PK. et al. Biol Proced Online. (5994)
  • KEYWORDS: DATABASES – RISK ASSESSMENT – SOFTWARE

    See Table 1 for a list of chemical databases that report data regarding biodegradation; Table 2 for examples of pathway prediction systems; and find a section entitled ‘Computational methods for predicting chemical toxicity’ where several commercial and publicly-available software are listed with details on which kind of prediction can provide.

  • 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.

  • Chemotypes sensitivity and predictivity of in vivo outcomes for cytotoxic assays in THLE and HepG2 cell lines. Shah F. et al. Bioorg Med Chem Lett. (5991)
  • KEYWORDS: ASSAY DATA – MOLECULAR DESCRIPTORS – RISK ASSESSMENT

    This article points out the utility of in vitro ATP depletion assays in both THLE and HepG2 cells for predicting the toxicological outcome in Exploratory Toxicology Studies across 446 Pfizer proprietary compounds (see support material). This study results suggest a higher likelihood of selecting suitable compounds for in vivo safety studies by using cytotoxicity assays in multiple cell-lines over a single cell line. In addition, the study carried out demonstrates that different cell-lines show different sensitivities to compounds depending on their ionization state (acid, base or neutral); HepG2 cells are more sensitive for basic compounds, whereas THLE cells have a relatively higher sensitivity for the acidic and neutral compounds. Combination of these in vitro cytotoxicity assays with physicochemical properties (ie., cLogP >3 and topological polar surface area (TPSA) <75 Å2), are identified as the most effective means to prioritize compounds having a lower probability of causing
    adverse events in vivo.

  • Clearance Mechanism Assignment and Total Clearance Prediction in Human Based upon in Silico Models. Lombardo F. et al. J Med Chem. (5989)

    Involved Partner: PFIZER

  • KEYWORDS: ADME – ASSAY DATA – RISK ASSESSMENT

    A two-tier model based on a data set of 469 compounds (see data in the support material) is presented to help on the assessment of the primary clearance mechanism assignment and total clearance prediction.

  • Interconnectivity of Disparate Nonclinical Data Silos for Drug Discovery and Development. Kasturi J. et al. Ther Innov Regul Sci. (5988)
  • KEYWORDS: ADME – BIOMARKERS – CARCINOGENICITY – CARDIOTOXICITY – DRUG DISCOVERY – DRUG SAFETY – PHARMACOKINETICS – PHARMACOLOGY – REPRODUCTIVE AND DEVELOPMENTAL TOXICITY – RISK ASSESSMENT

    eTOX cited

    The component of the Non-Clinical Road-Map and Impacts on Implementation Working Group associated with the US FDA–PhUSE (Pharmaceutical Users Software Exchange) Computational Sciences initiative presents their briefings on data interconnectivity discussions held on March 2012. Topics discussed:
    -what is data interconnectivity?
    -rationale and importance of being able to perform interconnectivity
    -challenges and limitations of data interconnectivity
    -data interconnectivity as a means for biomarker identification and intrepretation

    To illustrate the current challenges of data interconnectivity, authors present a series fo use cases: 1) in vitro + in vivo and ADME, 2) Reproductive and developmental toxicology + ADME (see Figure 2), 3) Safety pharmacology + Toxicology + ADME (PK/TK), 4) Carcinogenicity + Pharmacologic Classes (see Figure 4).

  • The added value of the 90-day repeated dose oral toxicity test for industrial chemicals with a low (sub)acute toxicity profile in a high quality dataset. Taylor K. et al. Regul Toxicol Pharmacol. (5987)
  • KEYWORDS: ASSAY DATA – REGULATORY GUIDELINES

    Based on data from ECHA Chem database (see tables 2 and 3), authors advice on the convenience of the 90-day repeated dose oral toxicity test and make their criteria proposal to avoid or performe such studies depending on early findings.

  • What’s that gene (or protein)? Online resources for exploring functions of genes, transcripts, and proteins. Hutchins JR. Mol Biol Cell. (5986)
  • KEYWORDS: DATABASES – PHARMACOLOGY – SYSTEMS BIOLOGY

    A comprehensive review on online resources that let exploration data publicly available on functions of genes, transcripts and proteins through 10 key questions (see Figure 1 for general workflow for the analysis of DNA, RNA or protein samples and questions about the hits identified, and supplemental material):
    – sequence and genomic origins
    – functions, pathways and systems
    – homologues and evolution
    – gene expression
    – protein structure
    – protein-protein interactions
    – post-translational modifications
    – genetic variants and disease association
    – drugs and inhibitors
    – summary and overview

April

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

  • Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity. Webb SJ. et al. J Cheminform.. (5977)
  • KEYWORDS: MUTAGENICITY – NETWORKS – QSAR MODELING – RISK ASSESSMENT – STRUCTURE-BASED PREDICTION

    This article presents a new algorithm that enables the interpretation of predictions made by black box models. Authors apply this algorithm to a series of models built on public mutagenicity data taking into account a variety of fingerprint descriptors using the KNIME workflow package for data processing, model building and prediction output (see support material Additional file 1). 4 examples illustrate the performance of such algorithm and point out the limitations in the predictions interpretation.

  • Is regression through origin useful in external validation of QSAR models?. Shayanfar A. et al. Eur J Pharm Sci. (5976)
  • KEYWORDS: QSAR MODELING

    Review on different aspects regarding the reliability for assessing new chemicals properties based on the external validation methods. Authors discuss related criteria as applicability domain, detection of outlier and selection of appropriate method for splitting of data to both training and test sets.

  • Overcoming drug resistance through in silico prediction. Carbonell P. et al. Drug Discov Today. (5975)
  • KEYWORDS: PAHTWAYS – PHARMACOLOGY – STRUCTURE-BASED PREDICTION

    In order to overcome the drug resistance aspects for any new drug candidate, strategies applied in early stage of the drug discovery will not only lower the risk of resistance but also might provide better combined therapies based on the resistance mechanisms and targeted pathways understanding. This article discusses about structure-based modeling and network biology approaches, with examples of application of the current in silico strategies.

  • In silico structure-based screening of versatile P-glycoprotein inhibitors using polynomial empirical scoring functions. Shityakov S et al. Adv Appl Bioinform Chem. (5974)
  • KEYWORDS: ASSAY DATA – P-gP – PHARMACOLOGY – STRUCTURE-BASED PREDICTION

    This article reports an extensive in silico structure-based screening of 1,300 molecules (796 P-gp inhibitors and 504 P-gp non-inhibitors, see data related in the Supplementary Material) to filter and examine them by gradient optimization docking algorithm combined with POLSCORE functions.

  • In vitro methods in drug transporter interaction assessment. Jani M. et al. Drug Discov Today. (5973)
  • KEYWORDS: ADME – PHARMACOKINETICS – PHARMACOLOGY – TRANSPORTERS

    Review of in vitro methods (assays with membrane preparations: vessicular transport and ATPase assays; assays with cells: cellular uptake, cellular efflux and monolayer assays) applied in the assessment stage of drug transporter interaction evaluation. See Figure 1 for illustration of expression of transporters implicated on the 4 major pharmacological barriers (blood-brain barrier, hepatocyte, proximal tubule cell and enterocyte), and Figure 2 for a condensed and modified scheme of proposed drug transporter substrate and inhibitor assessment drafted by FDA in 2012.

  • API-centric Linked data integration: The open PHACTS discovery platform case study. Groth P. et al. Web Semantics: Science, Services and Agents on the World Wide Web. (5972)
    Involved Partner: UNIVIE
  • KEYWORDS: DATABASE – PHARMACOLOGY

    Open PHACTS consortium presents in this article how Application Programming Interfaces (API) can extend the classical Linked Data Application Architecture to facilitate data integration, by describing their Discovery Platform that leverages Linked Data to provide integrated (structural and semantic) access to databases containing pharmacology related data.

  • Transporter assays and assay ontologies: useful tools for drug discovery. Zdrazil B. et al. Drug Discov Today. (5971)
    Involved Partner: EMBL, UNIVIE
  • KEYWORDS: ADME – DATA MINING – P-gP – TRANSPORTERS

    Focused on human P-glycoprotein (Multidrug resistance protein 1; gene name: ABCB1, MDR1), authors working in the framework of the Open PHACTS consortium exemplify in this article how annotation of bioassay data (membrane-based ATPase assays, monolayer assays, scintillation proximity assays or uptake transporter assays) per target class could improve and be added to existing ontologies. The section ‘Specific ontologies for target classes?’ review current available ontologies.

  • Transporter taxonomy – a comparison of different transport protein classification schemes. Viereck M. et al. Drug Discov Today. (5970)
    Involved Partner: EMBL, UNIVIE
  • KEYWORDS: DATA MINING – TRANSPORTERS

    A use-case study on transporters protein family taxonomy. Open PHACTS consortium presents their evaluation of the different transport protein classifications schemes currently available (see Table 1 for a list of examples (i.e., CheMBL, TP-search, TCDB, TransportDB) and how they have addressed the standardization of the diverse terms taking into account when ranging them by sequence-based or structure-based criteria.

  • 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).

  • A new in silico classification model for ready biodegradability, based on molecular fragments. Lombardo A. et al. Chemosphere. (5968)
  • KEYWORDS: STRUCTURE-BASED PREDICTION – RISK ASSESSMENT

    Based on a set of 728 chemicals with ready biodegradability data of MITI-test Ministry of International Trade and Industry, a new rule-based model (rule-set made up of the combination of the statistically relevant fragments and of the expert-based rules by application of SARpy software which automatically extracts knowledge from a dataset and detects the molecular structural fragments associated with the activity of interest) is presented (available on the VEGA web site) to help on the assessment of ready biodegradability. Find the procedure for generating the model in Figure 1 and the model tree that shows how a compound is classified on the bases of the fragments found in Figure 2.

  • Liposome electrokinetic chromatography based in vitro model for early screening of the drug-induced phospholipidosis risk. Wnag T. et al. J Pharm Biomed Anal. (5967)
  • KEYWORDS: ASSAY DATA – PHOSPHOLIPIDOSIS

    Find in Table 1 a list of 73 selected drugs and their phospholipidosis risk data measured by different prediction models.

  • Integrative knowledge management to enhance pharmaceutical R&D. Marti-Solano M. et al. Nat Rev Drug Discov. (5964)
  • KEYWORDS: DRUG DISCOVERY

    Information technologies already have a key role in pharmaceutical research and development (R&D), but achieving substantial advances in their use and effectiveness will depend on overcoming current challenges in sharing, integrating and jointly analysing the range of data generated at different stages of the R&D process. Authors discuss aspects like: data evaluation and integration, ontologies and standards, data and information sharing and data sustainability.

  • How can we discover safer drugs?. Hornberg JJ. et al. Future Med Chem. (5963)
  • KEYWORDS: DRUG DISCOVERY – PHARMACOLOGY

    Briefings on discovery safety state-of-the-art, efforts should be made to failing early for increasing success. Authors discuss on what are the key factors of discovery safety and how should it work in practice (ie., hitting the primary target, based on the available knowledge on the biological function of the target as well as on information (if available) on adverse reactions that emerged in clinical studies with candidates that hit the same target or pathway).

March

  • Legal aspects of text mining. Truyens M. et al. CLSR. (5962)
  • KEYWORDS: TEXT MINING

    Comprehensive review on legal aspects regarding the application of text mining techniques (corpora, creation of a corpus (crawling and scraping), protection of corpora,, copyright law, database law, contract law); and explains the difficulties presented by EU copyright law in comparison with the United States law.

  • Reducing attrition in drug development: smart loading preclinical safety assessment. Roberts RA. et al. Drug Discov Today. (5960)
  • KEYWORDS: DRUG DISCOVERY – DRUG SAFETY – RISK ASSESSMENT

    AZ presents a list of typical clinical pathology parameters for the rodent and non-rodent 14-day dose range finding in haematology, plasma chemistry, urine analysis and coagulation assays (see Table 2), and a list of typical pathology tissue for the rodent and non-rodent 14-day dose range finding studies.

  • Structure-based ligand design to overcome CYP inhibition in drug discovery projects. Brändén G. et al. Drug Discov Today. (5958)
  • KEYWORDS: CYP450 – METABOLISM – STRUCTURE-BASED PREDICTION

    AZ presents structural information on CYP2C9 and CYP3A4 inhibitor complexes is presented as key information for in ongoing drug discovery projects.

  • High-Content Analysis in Toxicology: Screening Substances for Human Toxicity Potential, Elucidating Subcellular Mechanisms, and In Vivo Use as Translational Safety Biomarkers. O’Brien PJ. et al. Basic Clin Pharmacol Toxicol. (5957)
  • KEYWORDS: ASSAY DATA – BIOMARKERS – DRUG SAFETY – HEPATOTOXICITY

    This article shows how the use of high-content analysis (HCA) of in vitro, biochemical and morphological effects of classic (small molecule) drugs and chemicals is concordant with potential for human toxicity. Interesting information listed in tables 1 (why is cell-based cytotoxicity testing needed in drug discovery) and 2 (features of effective cytotoxicity assessments of human toxicity potential), can help to guide this kind of analysis. Table 3 reports experimental data for 30 drugs which helps on the elucidation of subcellular pathogenesis of drug induced cell injuries.

  • Scientific and regulatory reasons for delay and denial of FDA approval of initial applications for new drugs, 2000-2012. Sacks LV. et al. JAMA. (5952)
  • KEYWORDS:DRUG SAFETY – REGULATORY GUIDELINES – RISK ASSESSMENT

    A retrospective review of FDA documents is reported as a comprehensive analysis of data corresponding to disapproval drugs between 2000 and 2012, in order to understand the scientific and regulatory reasons that made their initial applications failed. The understanding of such reasons should help on the improvement of clinical development for new drugs efficiency.

  • An Overview of Ontologies and Data Resources in Medical Domains. Ivanovic M. et al. Expert Syst Appl. (5951)
  • KEYWORDS: DATA MINING – TEXT MINING

    This article reviews the state of the art in biomedical terminologies and ontologies (see Table 1) and resources (tools and environments for knowledge integration/management in medical domains, see Section 4) which can be useful for novices in multi(inter)disciplinary research in medicine/biology/informatics.

  • Knowledge-based extraction of adverse drug events from biomedical text. Kang N. et al. BMC Bioinformatics. (5950)
  • KEYWORDS: TEXT MINING

    Authors present a new knowledge-based approach based on 2972 Medline abstracts (the case reports were selected by a PubMed query with the MeSH (Medical Subject Headings) terms “drug therapy” and “adverse effect”) annotated manually for adverse drug effects. Only those sentences that contain at least one adverse drug effect were made available by the corpus developers. Analysis of results are widely described and discussed among the manuscript.

  • Applications of a 7-day Caco-2 cell model in drug discovery and development. Peng Y. et al. Eur J Pharm Sci. (5948)
  • KEYWORDS: ASSAY DATA – ADME – PHARMACOKINETICS – RISK ASSESSMENT

    This paper provides an overview of a time and resource saving 7-day Caco-2 assay protocol. Authors discuss the impact of various experimental conditions on permeability measurements and its applications during lead optimization in early discovery and for clinical candidate characterization, specifically for prediction of absorption in human, at a later stage in drug development. Also a proposed permeability screening strategy is provided (see Figure 11).

  • Elucidating Substrate Promiscuity in the Human Cytochrome 3A4. Hayes C. et al. J Chem Inf Model. (5947)
  • KEYWORDS: CYP450 – METABOLISM – STRUCUTRE-BASED PREDICTION

    Structural information of CYP3A4. Find ligand-receptor interactions list in Table 4, and the list of failed ligands (docking performance on a per substrate basis for all scenarios) and list of all known CYP 3A4 substrates used in the present study in the support material section.

  • High-throughput identification of off-targets for the mechanistic study of severe adverse drug reactions induced by analgesics. Pan JB. et al. Toxicol Appl Pharmacol. (5946)
  • KEYWORDS: CARDIOTOXICITY – PHARMACOLOGY – SYSTEMS BIOLOGY

    This article presents 53 putative Adverse Drug Reactions-Associated Proteins (ADRAPs) and 24 pathways interlinked with cardiac disorders, of which 10 ADRAPs were confirmed by previous experiments.

February

  • Large-scale biomedical concept recognition: an evaluation of current automatic annotators and their parameters. Funk C. et al. BMC Bioinformatics. (5945)
  • KEYWORDS: DATA MINING – TEXT MINING

    Comparison between 3 dictionary-based systems (NCBO Annotator, MetaMap and ConceptMapper) on concept recognition for 8 ontologies in the Colorado Richly Annotated Full-Text (CRAFT) Corpus (Cell Type ontology, Gene Ontology – Cellular Component, Gene Ontology – Molecular Function, Gene Ontology – Biological Process, Sequence Ontology, ChEBI, NCBI Taxonomy and Protein Ontology), evidences a better performance in the case of ConceptMapper application. The full CRAFT corpus consists of 97 completely annotated biomedical journal articles, while the “public release” set, which consists of 67 documents, was used for this evaluation. CRAFT includes over 100,000 concept annotations from eight different biomedical ontologies.

  • Learning from ‘big data’: compounds and targets. Hu Y. et al. Drug Discov Today. (5943)
  • KEYWORDS: DRUG DISCOVERY

    This editorial article, based on information retrieved from the public domain for 4 drugs, highlights the fact that the drug efficacy not only depends on reaching data completeness, and there is a need of developing approaches to better understand relationships between target engagement by candidate compounds and ensuing therapeutic effects.

  • In vitro-in vivo extrapolation method to predict human renal clearance of drugs. Kunze A. et al. J Pharm Sci. (5942)
  • KEYWORDS: ADME – ASSAY DATA – PHARMACOKINETICS

    This article presents a novel in vitro–in vivo extrapolation (IVIVE) approach to predict human renal clearance, based on measures of the transport of 20 compounds that cover all four classes of the Biopharmaceutical Drug Disposition System in LLC-PK1 cells (see Table 2) and their physicochemical and pharmacokinetic properties (see Table 1).

  • Drug-induced photoallergic and phototoxic reactions – an update. Scheinfeld NS. et al. Expert Opin Drug Saf. (5941)
  • KEYWORDS: SSAY DATA – PHOSPHOLIPIDOSIS

    This article reviews the findings of a PubMed search for the key words ‘photosensitivity’, ‘photosensitive’, ‘phototoxicity’, ‘phototoxic’, ‘photoallergy’ and ‘photoallergic’. Articles published over the last 5 years were compared with those published earlier than this to find updated information on photo-reactions. Authors report in different tables: characteristic of photoallergic and phototoxic reactions (Table 1), topical medications capable of causing photoallergy (Table 2), causing phototoxicity (Table 3), oral medications capable of causing photoallergy (Table 4), systemic medications capable of causing phototoxicity (Table 5), diseases associated with photossensitivity (Table 6), oral medications capable of causing photoallergy by classes (Table 7), oral medications capable of causing photo-onycholysis (Table 8).

  • Translational Bioinformatics Approaches to Drug Development. Readhead B. et al. Adv Wound Care (New Rochelle). (5939)
  • KEYWORDS: DRUG DISCOVERY – NETWORKS

    Interesting review with discussions of findings and relevant literature on: disease taxonomy and drug development, evaluation of therapies beyond population-level, challenges for computational drug repositioning (drug-based (using drug similarity, molecular activity similarity, or molecular docking) approaches, and disease-based (using associative induction transfer, shared molecular pathology or side effect similarity) approaches, and causal network models of diseases.

  • 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.

  • Drug-induced nephrotoxicity: clinical impact and pre-clinical in vitro models. Tiong HY. et al. Mol Pharm. (5935)
  • KEYWORDS: NEPHROTOXICITY

    Comprehensive review on current approaches on the development of in vitro models for the prediction of nephrotoxicity to overcome the clinical impact of drug-induced acute kidney injury in oncology, in sepsis, in transplantation, or in diagnostic imaging.

  • Comprehension of drug toxicity: Software and databases. Toropov AA. et al. Comput Biol Med. (5933)
  • KEYWORDS: DRUG DISCOVERY – SOFTWARE

    A comprehensive review of databases (Table 2) which are sources of available data on toxicity classified in 3 categories (numerical data on experimental toxic endpoints, numerical data on calculated (or extracted from several references) toxic endpoint, and documentation related to toxicity (references and/or descriptions)); and software (Table 3) available for QSAR analysis of toxic endpoints.

  • The Biopharmaceutics Classification System: Subclasses for In Vivo Predictive Dissolution (IPD) Methodology and IVIVC. Tsume Y. et al. Eur J Pharm Sci. (5931)
  • KEYWORDS: DRUG DISCOVERY – STRUCTURE-BASED PREDICTION

    This manuscript presents how to use results to support the proposed simple extension of the Biopharmaceutics Classification System (BCS) to include Subclassification of acid, base and neutral for classes II and IV. It includes some general recommendations for dissolution methodologies.

  • The role of ligand efficiency metrics in drug discovery. Hopkins AL. et al. Nat Rev Drug Discov. (5930)
  • KEYWORDS: ADME – DRUG DISCOVERY – PHARMACOLOGY

    This article analysis reviews different aspects (lipophilicity, binding thermodynamics, ADMET properties, fragment-based methods) regarding the ligand efficiency measurements and their exploitation in terms of drug discovery knowledge.

January

  • The Role of Modeling and Simulation in Development and Registration of Medicinal Products: Output From the EFPIA/EMA Modeling and Simulation Workshop. Manolis E. et al. CPT Pharmacometrics Syst Pharmacol. (5929)
  • KEYWORDS: REGULATORY GUIDELINES

    Representatives from industry, academia, and regulatory agencies from Europe and beyond discussed the role of Modeling and Simulation in the development and registration of medicinal products within plenary and breakout sessions. Table 1 provides links to the corresponding presentations of such Workshop.

  • Applying linked data approaches to pharmacology: Architectural decisions and implementation. Gray AJG. et al. Semantic Web. (5928)
  • KEYWORDS: DATABASE – DRUG DISCOVERY – PHARMACOLOGY – SOFTWARE

    Open PHACTS consortium presents their strategy to define the architectural decisions for implementing a linked data platform to support drug discovery that integrates the pre-competitive openly available data from many semantic web sources. This paper focuses on two key contributions: 1) A set of seven architectural decisions for a data integration platform driven by pharmacological business questions; and 2) A discussion of the use of this platform by three separate drug discovery applications.

  • Pharmacoepidemiological characterization of drug-inducedadverse reaction clusters towards understanding of their mechanisms. Mizutani S. et al. Comput Biol Chem. (5927)
  • KEYWORDS: PHARMACOLOGY – SYSTEMS BIOLOGY

    This article presents a biclustering approach to catalogue the relationship between drugs and adverse reactions from the large FDA Adverse Event Reporting System (FAERS) data set, and the results of this study (163 biclusters of drug-induced adverse reactions (ATC and MedDRA classifications), counting for 691 ADRs and 240 drugs in total) demonstrate a systematic way to uncover the cases different drug administrations resulted in similar adverse reactions, and the same drug can cause different reactions dependent on the patients’ conditions.

  • The Roles of MRP2, MRP3, and OATP1B1 and OATP1B3 in Conjugated Hyperbilirubinemia. Keppler D. et al. Drug Metab Dispos. (5924)
  • KEYWORDS: STRUCTURE-BASED PREDICTION – TRANSPORTERS

    The results of this article show:
    – Under normal conditions, unconjugated bilirubin is taken up into hepatocytes by transporters of the organic anion-transporting polypeptide (OATP) family, followed by conjugation with glucuronic acid, and ATP-dependent transport into bile. This efflux across the canalicular membrane is mediated by multidrug resistance protein 2 (MRP2 or ABCC2)
    – Under pathophysiological conditions, such as cholestatic liver injury and MRP2 inhibition, the basolateral efflux pump multidrug resistance protein 3 (MRP3 or ABCC3) is responsible for the occurrence of conjugated hyperbilirubinemia.
    See Figure 1 where the bilirubin transport into human hepatocytes, conjugation, canalicular and sinusoidal efflux, and reuptake by downstream hepatocytes is schematically depicted.

  • Human tissue in systems medicine. Caie PD. et al. FEBS J. (5923)
  • KEYWORDS: SYSTEMS BIOLOGY

    This minireview argues that a systems medicine approach to pathology will not seek to replace traditional pathology, but rather augment it. Systems approaches need to incorporate quantitative morphological, protein, mRNA and DNA data. A significant challenge for clinical implementation of systems pathology is how to optimize information available from tissue, which is frequently sub-optimal in quality and amount, and yet generate useful predictive models that work.

  • 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.

  • A New Approach to Radial Basis Function Approximation and its Application to QSAR. Zakharov A. et al. J Chem Inf Model. (5921)
  • KEYWORDS: ASSAY DATA – DRUG DISCOVERY – QSAR MODELING

    A novel approach to Radial Basis Function approximation is described, and its validation is presented for 14 public datasets comprising 9 physicochemical properties and 5 toxicity endpoints. Authors also compared their new method with 5 different QSAR methods implemented in the EPA T.E.S.T. program (see Table 2). The series of models created applying this method are available at http://cactus.nci.nih.gov/chemical/apps/cap.

  • Recent advances in modeling languages for pathway maps and computable biological networks. Slater T. et al. Drug Discov Today. (5920)
  • KEYWORDS: NETWORKS – PATHWAYS – SYSTEMS BIOLOGY

    A review on the most recent systems biology modelling languages: BEL (takes semantically rich network description approaches and focuses on modeling a network of causal relationships between entities to support applications such as reasoning toward data interpretation), PySB (takes a programmatic approach to network modeling using a wellknown programming language and associated tools and techniques to facilitate rapid model building, simulation and reuse) and BCML (takes semantically rich network description approaches and supports detailed signalling pathways with dynamic connections to their graphical representations).

  • 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).

  • How drug-like are ‘ugly’ drugs: Do drug-likeness metrics predict ADME behaviour in humans?. Ritchie T. et al. Drug Discov Today. (5918)
  • KEYWORDS: ADME – PHARMACOKINETICS – STRUCTURE-BASED PREDICTION

    Based on the quantitative estimate of drug-likeness (QED) score calculated using 8 important properties (molecular weight, octanol–water partition coefficient, number of H-bond donors, number of H-bond acceptors, molecular polar surface area, number of rotatable bonds, number of aromatic rings, and number of structural alerts), authors present the correlation between the score value for a series of 300 oral drugs with their actual pharmaceutical and pharmacokinetic profiles in human.

  • Addressing toxicity risk when designing and selecting compounds in early drug discovery. Segall MD. et al. Drug Discov Today. (5917)
    Involved Partner: LL
  • KEYWORDS: DRUG DISCOVERY – RISK ASSESSMENT – SOFTWARE

    Focused on the 24 drugs approved by the FDA in 2012 (see support material), the application of the knowledge-based prediction system Derek Nexus evidences the role of such engines to support earlier decisions regarding toxicity risk, and to guide compounds design based on structural toxicity alerts information. Examples of the reasoning levels within this software are provided (see Figure 4 and Box 1).

  • A Non-Binary Biopharmaceutical Classification of Drugs: The ABΓ system. Macheras P. et al. Int J Pharm. (5916)
  • KEYWORDS: ASSAY DATA – ADME

    The ABΓ system presented here is a non-binary biopharmaceutical classification system which is based on the fraction of dose absorbed and relies on permeability, solubility plane. Three categories are defined (A Fa≥0.90, B Fa≤0.20, and Γ0.20 < Fa < 0.90)based on data for 40 drugs (see Table 1 for reported and calculated properties (solubility/dose ratio and effective permeability).

  • On the Use of In Silico Tools for Prioritising Toxicity Testing of the Low-Volume Industrial Chemicals in REACH. Rybacka A. et al. Basic Clin Pharmacol Toxicol. (5913)
  • KEYWORDS: REGULATORY GUIDELINES – SOFTWARE

    Based on data publicly available for a set of 141 industrial chemicals and pesticides with extensive peer-reviewed risk assessment data, this article reports the results of the analysis performed through different in silico model systems (Derek Nexus, Toxtree, QSAR Toolbox, LAZAR,
    TEST and VEGA) and the comparison with expert-judged risk classification according to the CLP (classifying, labelling and packaging) regulation.

  • Large-scale combining signals from both biomedical literature and the FDA Adverse Event Reporting System (FAERS) to improve post-marketing drug safety signal detection. Xu R. et al. BMC Bioinformatics. (5912)
  • KEYWORDS: TEXT MINING – RISK ASSESSMENT

    This article presents an effective approach for a large-scale signal detection from two different sources, FDA Adverse event reporting system (FAERS) and MEDLINE. Concretely, this study retrieved a list of 179,458 candidate drug-side effect pairs supported by evidences encountered in both public databases (based on information identified within 4,285,097 records from FAERS and 21,354,075 MEDLINE articles), which was supported by manual curation.

  • The role of drug metabolizing enzymes in clearance. Di L. et al. Expert Opin Drug Metab Toxicol. (5911)
  • KEYWORDS: ADME – ENZYMES – METABOLISM

    A review on the drug metabolizing enzymes role (aldehyde oxidase, uridine 5′-diphoshpho-glucoronosyltransferase, and sulfotransferases) in the clearance mechanism of a compound.

  • Challenges and prospects for biomarker research: A current perspective from developing world. Gupta S. et al. Biochim Biophys Acta. (5908)
  • KEYWORDS: BIOMARKERS

    A comprehensive list of challenges associated with different stages of biomarker discovery pipeline (see Table 1).

  • 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)).

  • Determining molecular predictors of adverse drug reactions with causality analysis based on structure learning. Liu M. et al. J Am Med Inform Assoc. (5905)
  • KEYWORDS: MOLECULAR DESCRIPTORS – RISK ASSESSMENT – SYSTEMS BIOLOGY

    This article presents a structure-learning-based causality analysis model (CASTLE) to determine factors that play essential roles in organ-specific adverse event reactions from the chemical and bilological profiles of drugs. Interestingly. CASTLE can generate interpretable results by extracting a set of factors that are considered responsible for a certain adverse event reaction (see example in Table 4).

  • 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.

  • Exploratory toxicology as an integrated part of drug discovery – Part I: Why and how. Hornberg JJ. et al. Drug Discov Today. (5899)
    Involved Partner: LDB
  • KEYWORDS: DRUG DISCOVERY – RISK ASSESSMENT

    In this review (part I) authors outline the integrated toxicology strategy designed by Lundbeck pharma to i) identify target- and compound-related safety hazards early on the drug discovery process, ii) optimize chemical series on safety endpoints and iii) provide a broad characterization and risk assessment for new drug candidates.