2013

2013

December

  • Data-driven medicinal chemistry in the era of big data. Lusher SJ. et al. Drug Discov Today. (5898)
  • KEYWORDS: DRUG DISCOVERY

    New data-driven medicinal chemistry approaches are developed to improve decision making in drug discovery projects. They support computational and synthetic scientists in data management, storage and sharing, to keep them up to date with the relevant literature and to help in the dissemination of their research to a wide audience. Different issues like volume and variety of data available or data expected to be generated nextly, ensuring the quality of data by needs of data normalization and ensuring the value of data generated with statistical relevance which can benefit future studies are reviewed as key points of a better exploitation of current data. Authors emphasize the need for data-specialists in different disciplines, and also the requirement for all researchers to become data scientist or knowledge workers; and how new tools will favour a higher rationalization in the design and decision making processes and will help to retain knowledge. Several examples illustrating the current procedures are discussed and some solutions are presented based on recent experiences at pharma industries.

  • PBTK modelling platforms and parameter estimation tools to enable animal-free risk assessment: Recommendations from a joint EPAA – EURL ECVAM ADME workshop. Bessems JG. et al. Regul Toxicol Pharmacol. (5897)
  • KEYWORDS: ADME – DRUG DISCOVERY

    This is a new report of an EPAA – EURL ECVAM expert meeting to provide guidance to test developers, toxicologists, safety assessors and regulators with respect to priority setting in research projects. An overview of the availability of PBTK platforms (what is needed in a modelling platform, current status of PBTK models, examples of models, the metabolism related information, the complexity of PBTK models) is presented and recommendations are detailed to wide use of non-animal based PBTK modelling for animal-free chemical risk assessment. For instance, Figure 1 summaries input parameters for basic PBTK modelling to predict C,t-curves in blood and/or tissues, Table 2 shows test method output parameters and conversion to input parameters in a PBTK model, or tables 3-6 show an overview of in vitro methods for PBTK model parameterisation for absorption, distribution, metabolism, excretion, respectively. Experts agreed that there is a need of prioritisation of the future work for PBTK modelling taking advantage of databases (i.e., KinParDB), and discussions drove to eventually wrap up the meeting with 5 key recommendations: 1) collect/store human chemical-specific kinetic data, 2) form a internation group of PBTK model reviewing experts, 3) give access to PBTK modelling web applications, 4) develop in vitrotools for high-throughput measurement of partitioning and expand the applicability domain for various tools, and 5) develop high-throughput and low cost analytical facilities to measure chemical in physiological media.

  • Building a drug ontology based on RxNorm and other sources. Hanna J. et al. J Biomed Semantics. (5896)
  • KEYWORDS: DATA MINING – TEXT MINING

    For semantic web application purposes, a drug ontology (DrOn) built from mining data from historical releases of the RxNorm starndard drug terminology curated by the National Library of Medicine and mapping of many of RxNorm entites to corresponding Chemical Entities of Biological Interest (ChEBI) classes is presented, see details of development (extraction of information from RxNorm, tracking provenance of terminology, mapping to ChEBI, transforming the data into a normalized format (see Figure 3 for the list of entity types of DrOn and their relationships), creation of the OWL 2.0 version), and contents (DrOn contains a total of 514,268 classes as of this writing. Of these, 2 are MIREOTed, 51 were imported using OWL’s built in mechanisms, 1,885 were taken from ChEBI, two were taken from PRO, and the remaining 512,328 were mined from RxNorm) of this ontology in the full text.

  • Profiling Cumulative Proportional Reporting Ratios of Drug-Induced Liver Injury in the FDA Adverse Event Reporting System (FAERS) Database. Brinker AD. et al. Drug Saf. (5895)
  • KEYWORDS: DATABASE – DATA MINING – DRUG SAFETY – DILI – HEPATOTOXICITY

    A pilot exercise of adverse reaction information analysis (based on data extracted from the FDA Adverse Event Reporting System (FAERS) database) for a set of 5 drugs (see Table 1 for name of drugs, DILI reports in FAERS, proportional reporting rate values and domestic retail prescriptions) to support drug safety in the concerns of idiosyncratic DILI.

  • Quantitative Structure-Activity Relationship Models for Predicting Drug-Induced Liver Injury Based on FDA-Approved Drug Labeling Annotation and Using a Large Collection of Drugs. Chen M. et al. Toxicol Sci. (5894)
  • KEYWORDS: DILI – MOLECULAR DESCRIPTORS – QSAR MODELING

    Based on data extracted from FDA-approved drug labelling, a set of 197 drugs (81 positives and 116 negatives DILI inducers, see Supplementary Material tables) has been used as training set for the development of a QSAR model by means of molecular descriptors using Mold2 and a Decision Forest algorithm. The validation set used in this case contains 190 drugs (95 positive and 95 negative drugs, see Supplementary Material tables). Find a summary of internal cross-validation and external validation results in Table 1, and the high- and low-confidence therapeutic subgroups (second level of ATC Classification) identified from cross-validation of the QSAR model based on the training set in Table 2.

  • A semi-supervised approach to extract pharmacogenomics-specific drug-gene pairs from biomedical literature for personalized medicine. Xu R. et al. J Biomed Inform. (5892)
  • KEYWORDS: DATA MINING – TEXT MINING – SYSTEMS BIOLOGY

    An example of initiatives to extract drug-gene associations from the literature for risk assessment support information. With the aim to collect a comprehensive and accurate pharmacogenomics-specific drug-gene relationship knowledge base, authors present a semi-supervised learning approach (see Figure 1) to iteratively extract and rank such information spread across the biomedical literature. Starting with 19,055 human protein-coding genes, 6707 drugs, 20 million MEDLINE abstracts, and a few PGx-specific drug–gene pairs as seeds, the extraction algorithm achieved a precision of 0.219, a recall of 0.368, and an F1 of 0.274 after two iterations; which in comparison to a dictionary-based approach with PGx-specific gene lexicon as input, the present approach has better performance in terms of precision and F1 parameters.

  • Toward creation of a cancer drug toxicity knowledge base: automatically extracting cancer drug-side effect relationships from the literature. Xu R. et al. J Am Med Inform Assoc. (5891)
  • KEYWORDS: DATABASE – DATA MINING – TEXT MINING – SYSTEMS BIOLOGY

    Focused on the cancer drug toxicity knowledge, a repository of automatically extracted drug-side effect associations identified from the literature texts is presented. The potential of the results retrieved can be evaluated when combined with existing cancer drug side effects knowledge, which can facilitate guidance for drug target discovery, drug repurposing, and even though attempts to toxicity prediction. Details of cancer drug and side effect lexicon are provided. The analysis of the 56,602 drug-side effects pairs extracted points out that the cancer drugs with the same side effects tended to have overlapping indications and overlapping gene targets.

  • Text mining effectively scores and ranks the literature for improving chemical-gene-disease curation at the comparative toxicogenomics database. Davis AP. et al. PloS One. (5890)
  • KEYWORDS: DATABASE – SYSTEMS BIOLOGY -TEXT MINING

    A new example of initiatives that based on text mining techniques extract different biological entities relationships from the literature, in this case the study retrieves chemical-gene, chemical-disease and gene-disease interactions which are curately collected in the Comparative Toxicogenomics Database (CTD) that focuses in environmental chemicals, gene products and their relationship to diseases. Authors present their strategy from identification to associations curation for a set of 14,904 articles triaged fro 7 heavy metals (see figures 2 and 3). In order to reduce manually curators time, they take profit of an own developed web-based curation tool that provides technical support simplifier biocurators work and ensure high confidence of knowledge stored (see Table 1 for a list of rules-based document ranking algorithm) by means of their proposed document relevant score.< /span>

  • Connections in pharmacology: innovation serving translational medicine. Montero-Melendez T. et al. Drug Discov Today. (5889)
  • KEYWORDS: DRUG DISCOVERY – PHARMACOLOGY – SYSTEMS BIOLOGY

    This perspective article discusses, based on Connectivity Map database information (the most recent release contains 7056 gene expression signatures generated from 1309 drugs), the innovative whole-genome-based strategies that can contribute to drug discovery and development by identification of novel treatments for a certain disease, discovery of mechanisms of action of novel or known compounds, and drug repositioning studies.

  • Virtual pharmaceutical companies: Collaborating flexibly in pharmaceutical development. Forster SP. et al. Drug Discov Today. (5888)
  • KEYWORDS: DRUG DISCOVERY – PROJECT

    A proposal of developing flexible collaboration in the pharmaceutical industry landscape in a virtual way. The Virtual Pharmaceutical Companies (VPCs) concept could complement the current scenario of collaborations which would facilitate development speed and efficiency, flexibility and the absence of expensive fixed assets. This review explains what are the virtual companies, describe the 5 possible modes of collaboration between big pharma and virtual companies (in-licensing, founding, acquiring, investing and out-licensing) and includes an example of an outsourcing and collaboration network of a pharmaceutical company in Figure 3 which illustrates how various relations can be established with multiple corporate partners (including virtual pharmaceutical companies, biothechs, other big pharma companies, and contract research and manufacturing organizations) and even though with a network of all relations with regulatory agencies, governments, academia, nonprofit organizations and patients (see Figure 4 for a simplified representation of a pharmaceutical industry network).

  • Chemical predictive modelling to improve compound quality. Cumming JG. et al. Nat Rev Drug Discov. (5887)
  • KEYWORDS: DRUG DISCOVERY – DRUG SAFETY – MOLECULAR DESCRIPTORS – QSAR MODELING – SOFTWARE – STRUCTURE-BASED PREDICTION

    AstraZeneca presents a review on the application of computational methods in guiding the selection of higher-quality drug candidates, based on the company experiences. A selection of commonly used tools for chemical predictive modeling is provided in Table 1 (chemoinformatics tools, statistical tools and toolkits, pipeline tools and data visualization tools). Authors report their rule-based models for compound quality (see Box 1), and list the AZfilters developed (see Box 2) to assist drug design teams in identifying compounds with undesirable chemical features. Discussion on different issues regarding QSAR models points out the need of automated QSAR models building, and review current tools available (OCHEM, Discovery Bus, ChemModLab).

November

  • The State of the Human Proteome in 2013 as viewed through PeptideAtlas: Comparing the Kidney, Urine, and Plasma Proteomes for the Biology and Disease-driven Human Proteome Project. Farrah T. et al. J Proteome Res. (5886)
  • KEYWORDS:DATABASE – SYSTEMS BIOLOGY

    This article presents the update of Human All PeptideAtlas, currently containing 12,644 nonredundant proteins and at least one peptide for each of ~14,000 Swiss-Prot entries (an increase over 2012 of ~7.5%). Table 1 shows the published surveys of urine, kidney, and plasma proteomes. Analysis of this resource contents let authors to address questions like: 1) What are the abundance correlations between each pair of tissue/biofluid-based proteomes? 2) What is the nature of proteins highly enriched in urine over both kidney and plasma? or 3) Of genetic loci previously associated with declining kidney function, which do we observe in kidney, urine, and plasma, and at what relative abundances?. All data is available through web-based repository.

  • Big data in biomedicine. Costa FF. et al. Drug Discov Today. (5885)
  • KEYWORDS: DATABASE – DATA MINING – SYSTEMS BIOLOGY

    This review highlights the impact of the big data aspects in the field of the biomedicine, concretely around the omics data generation, storage and exploitation for the benefit of the patient care. The following information is provided: 1) Examples of companies and institutions that provide solutions to generate, interpret and visualize combined omics and health clinical data (see Table 1), 2) Examples of big corporations offering solutions and pipelines to store, analyze and deal with complex biomedical information (see Table 2), and 3) Examples of companies that offer personalized genetics and omics solutions (see Table 3). Figure 1 illustrates in a general way the scenario of the big data in biomedicine.

  • Emerging technologies and challenges for better and safer drugs. Theodosiou M. et al. Biotechnol Lett. (5883)
  • KEYWORDS: DRUG DISCOVERY – QSAR MODELING – RISK ASSESSMENT – SYSTEMS BIOLOGY

    New developments will help on the decision support for toxicity risk assessment, we need to move out from the old model of one drug, one target and one disease, it was too simplistic way for understanding of a complex system such as human biology. Different in silico (QSAR modeling, similarity ensemble approach (SEA)), in vitro (induced pluripotent stems cells (iPS), and in vivo (transgenic animals, organ-on-a-chip approach) strategies are reviewed, and the new x-omics technologies like Toxicogenomics or gene expression profiling in toxicity studies are emphasized to be used to identify the best patient population for the drug as the blockbuster model of one size fits all is no longer applicable.

  • Genotype to phenotype via network analysis. Carter H. et al. Curr Opin Genet Dev. (5882)
  • KEYWORDS: RISK ASSESSMENT – SYSTEMS BIOLOGY

    The analysis of biological networks to understand why genetic alterations cause changes in the phenotype at the cellular level. This article reviews different aspects of recent developments: networks for biological inference, mutations as network perturbations,network properties o human disease genes, cross-species network analysis, regulatory networks and non-coding DNA; and provides a summary of several common varieties of biological network (see Table 1) and a summary of recent network-based strategies for identifying biological mechanisms underlying genetic disorders (see Table 2).

  • 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).

  • The Ontology of Clinical Research (OCRe): An informatics foundation for the science of clinical research. Sim I. et al. J Biomed Inform. (5880)
  • KEYWORDS: DATA MINING – TEXT MINING – SYSTEMS BIOLOGY

    This article presents an ontolgoy (Ontology of Clinical Research, OCRe) to provide knowledge-based support for the scientific tasks of clinical research data analysis drawing clinical conclusions from study observations. Information of generation, contents, infrastructure and limitations of this new resource is provided with details and examples. This ontology is available in OWL 2 model.

  • Replicated, replicable and relevant–target engagement and pharmacological experimentation in the 21st Century. Kenakin T. et al. Biochem Pharmacol. (5878)
  • KEYWORDS: DRUG DISCOVERY – PHARMACOLOGY – SYSTEMS BIOLOGY

    This overview discusses current trends in the execution and reporting of experiments and the criteria necessary for the physiologically relevant assessment of selective target engagement that can be used to both identify viable new drug targets and advance translational studies.

  • A European perspective on alternatives to animal testing for environmental hazard identification and risk assessment. Scholz S. et al. Regul Toxicol Pharmacol. (5876)
  • KEYWORDS: REGULARORY GUIDELINES

    This review provides an overview on current regulations of chemicals and the requirements for animal tests in environmental hazard and risk assessment. It aims to highlight the potential areas for alternative approaches in environmental hazard identification and risk assessment. Perspectives and limitations of alternative approaches to animal tests using vertebrates in environmental toxicology, i.e. mainly fish and amphibians, are discussed.

  • New developments in the evolution and application of the WHO/IPCS framework on mode of action/species concordance analysis. Meek ME. et al. J Appl Toxicol. (5875)
  • KEYWORDS: DRUG DISCOVERY – PHARMACOLOGY – REGULATORY GUIDELINES – SYSTEMS BIOLOGY

    The World Health Organization/International Programme on Chemical Safety mode of action/human relevance presents, as an addendum to the previous guidance, the update based on recent developments. The revised aspects can be sorted out from the figures 1, 2 and 4, meanwhile Figure 5 illustrates the modified Bradford Hill considerations for weight of evidence of hypothesized modes of action and Figure 7 illustrates a concordance table including dose-response curve (metabolism by CYP2B1, sustained cell damage and repair, and liver and kidney tumors). The lack of human concordance (Case 1), the use of kinetic and dynamic data in species concordance analysis and implications for soe-response analysis (Case 2), the role of mode of action analysis in the evaluation of epidemiological data (Case 3), the use of model of action analysis to guide development of more efficient testing strategies (Case 4), the mode of action analysis in prioritizing substances for further testing (Case 5) the mode of action in the creation of chemical categories (Case 6) and the use of mode of action analysis to identify critical data needs and testing strategies in read-across (Case 7) are discussed with certain examples.

  • Assessing the translatability of In vivo cardiotoxicity mechanisms to In vitro models using causal reasoning. Enayetallah AE. et al. BMC Pharmacol Toxicol. (5874)
  • KEYWORDS: CARDIOTOXICITY -SYSTEMS BIOLOGY

    Based on a causal reasoning engine, this article reports a novel approach to infer upstream molecular events related with cardiotoxicity which cause changes in the expression of certain genes. Comparison of molecular mechanisms of certain drugs that induce cardiac disorders are presented in Figure 1 based on rat and mouse independent experiments, in figures 2 and 3 based on different tissue, in vivo rat heart and in vitro primary rat cardiomyoctes. Authors provide a heatmap (see Figure 4) of major causal reasoning biological networks elicited by the list of cardiotoxic compounds listed in Table 1. This study shows how to translate an in vitro signal to an in vivo outcome.

  • 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.

  • A Framework for Identifying Chemicals with Structural Features Associated with Potential to Act as Developmental or Reproductive Toxicants. Wu S. et al. Chem Res Toxicol. (5868)
  • KEYWORDS: DEVELOPMENTAL AND REPRODUCTIVE TOXICITY – STRUCTURE-BASED PREDICTION

    This paper presents an empirically-based decision tree (see Figure 1) for determining whether or not a chemical has structural features that are consistent with chemical structures known to have toxicity for Developmental and Reproductive toxicity endpoints based on data for a set of 716 compounds. The category/sub-category information, rules and description for the entire tree; toxicity data, code, chemical structures to generate the decision tree are provided as support material.

  • Current status and future prospects of toxicogenomics in drug discovery. Khan SR. et al. Drug Discov Today. (5867)
  • KEYWORDS: DRUG DISCOVERY – SYSTEMS BIOLOGY

    eTOX cited

    A review on various components of holistic toxicogenomics with examples of applicaitons, analytical tools and platforms that illustrate how next-generation drug discovery and development (DDD) is evolving recently (see Figure 1). The toxicogenomics resources (gene expression analysis, global miRNA analysis, proteomic analysis, metabolite analysis, idiosyncratic drug toxicity and epigenomics, omics data analysis and bioinformatics, and integrated informatics) are presented as a collection of approaches that could provide valuable information about drug-induced toxicity, its mechanisms and potential toxicological biomarkers for DDD in a predictable and cost-effective manner.

  • Precompetitive consortia in biomedicine—how are we doing?. Mittleman B. et al.Nat Biotechnol. (5864)
  • KEYWORDS: CONSORTIUM – DRUG DISCOVERY

    By pooling resources and expertise, progressively precompetitive consortia can help advance the drug development. See Box 2 for a list of elements of effective, multi-stakeholder consortia, and Table 1 for a list of benefits, risks and relevant metrics for consortia generation.

  • In vitro metabolism and bioavailability tests for endocrine active substances: what is needed next for regulatory purposes?. Jacobs MN. et al.ALTEX. (5862)
  • KEYWORDS: METABOLISM – REGULATORY GUIDELINES

    This review builds upon the recommendation given in the publication of a Detailed Review Paper of the Organization for Economic Cooperation and Development (OECD) on the use of metabolizing systems for in vitro testing of endocrine substances. Discussions on the importance of metabolism and absorption in prediction are presented together with key considerations for research approaches for short, medium and long term projects

  • TRANSLATIONAL PARADIGMS IN PHARMACOLOGY AND DRUG DISCOVERY. Mullane K. et al. Biochem Pharmacol. (5861)
  • KEYWORDS:DRUG DISCOVERY – PHARMACOLOGY

    A comprehensive review on different topics related with translational pharmacology: biomedical research funding in the decade of the 21st century, .output from biomedical research activities, challenges in effective translational science, fundamentals of translational process, hierarchy in advancing targets and therapeutics.

  • 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).

October

  • The OSIRIS Weight of Evidence approach: ITS for the endpoints repeated-dose toxicity (RepDose ITS). Tluczkiewicz I. et al. Regul Toxicol Pharmacol. (5854)
    Involved Partner: FRAUNHOFER
  • KEYWORDS: PROJECT – RISK ASSESSMENT

    An overview of the RepDose database (the documentation of the study (reliability), the quality of the study design (adequacy) and the scope of examination (valiidity)) and the detailed information about its implementation as an Integrated Testing Strategy data (oral subacute and subchronic repeated-dose toxicity studies in rat) into the OSIRIS webtool is presented in this article, where criteria of the ToxRtool are listed in Table 1, criteria to assess the documentation of the study are listed in Table 2 and criteria to assess the quality of a non-guideline study are listed in Table 3.

  • Exploring the biologically relevant chemical space for drug discovery. Deng ZL. et al. J Chem Inf Model. (5853)
  • KEYWORDS: DRUG DISCOVERY – QSAR MODELING

    Based on drugs launched, authors present their data collection exercise, their QSAR models generation, and a web-server called BioRel to facilitate the use of their algorithms for prediction of Human Intestinal Absorption, Blood-Brain Barrier Penetration, Nucleus and Periplasm Localization and acute toxicity.

  • Some experiences and opportunities for big data in translational research. Chute CG. et al. Genet Med. (5852)
  • KEYWORDS: BIOMARKERS – DATABASE – DRUG DISCOVERY – PROJECT – TRANSLATIONAL RESEARCH

    This review lists several aspects and initiatives towards the application of the translational research strategy. To name a few: i) the eMERGE consortium aims to provide a scalable capacity to execute high-throughput phenotyping of patient cohorts using electronic medical records spearheading the integration of genomic data into clinical practice, ii) the diversity and nature of the translational data (clinical genetic/genomic, public health, direct-to-consumer testing laboratories), and iii) the need for consistency of data and how comparable they are (use data standards for reporting and exploitation through the use of crossreference identifiers (i.e., Human Gene Nomenclature Committee symbols, SNOMED CT, OMIM, PubMed, PharmGKB)) .

  • Genomic and systems approaches to translational biomarker discovery in immunological diseases. MacIsaac KZ. et al. Drug Discov Today. (5851)
  • KEYWORDS: BIOMARKER – IMMUNOTOXICITY – TRANSLATIONAL RESEARCH

    A brief review on the role of biomarkers in the assessment of infectious and immunological diseases, and the evaluation of different aspects that can contribute to their identification and application (i.e., pharmacodynamics, patient stratification) taking into account the emerging technologies (i.e., rapid developments in next-generation sequencing (NGS) technologies with associated bioinformatics algorithm and analysis tools, sequencing of host microbiome).

  • Drug name recognition in biomedical texts: A machine-learning-based method. He L. et al. Drug Discov Today. (5850)
  • KEYWORDS: DRUG DISCOVERY – TEXT MINING

    A new machine-learning-based approach to recognize drug names in text is presented, based on a drug name dictionary resulting of DrugBank and PubMed terminology combination and using the method of context pattern induction. This approach shows a high confidence of recalling information in comparison to the strategy applied by DDIExtraction2011 task (a total of six teams participated in the task of recognition and classification of pharmacological substances, their approaches included a dictionary-based approach, ontology-based approach and machine-learning-based approaches such as CRF, decision tree classifier and SVM classifier).

  • Drug-Induced Perturbations of the Bile Acid Pool, Cholestasis, and Hepatotoxicity: Mechanistic Considerations Beyond the Direct Inhibition of the Bile Salt Export Pump. Rodrigues AD. et al. Drug Metab Dispos. (5849)
  • KEYWORDS: ADME – BIOMARKERS – DILI – HEPATOTOXICITY – PHARMACOKINETICS – RISK ASSESSMENT

    Due to any drug or new chemical entity that inhibits bile salt export pump (BSEP) has the potential to cause cholestasis and possibly liver injury, authors present their work on evaluating its role in the biliary clearance of bile acids. Figure 2 illustrates the liver Bile acids transporters (OST, MPR3/4, OATP1B1, NTCP, BSEP and MRP2) and their regulation (by Nuclear receptors FXR, PXR and CAR, and SULT2A1 phase II enzymes), meanwhile Figure 3 shows a summary of the potential mechanism(s) by which a drug or metabolite can impact the hepatobiliary disposition of bile acids.

  • Applying Linear and Non-Linear Methods for Parallel Prediction of Volume of Distribution and Fraction of Unbound Drug. Del Amo EM. et al. PloS One. (5848)
  • KEYWORDS: ADME – ASSAY DATA – MOLECULAR DESCRIPTORS – PHARMACOKINETICS – STRUCTURE-BASED PREDICTION

    Based on data extracted from the literature for a set of 642 drugs, authors present their predictive models (see Table 1 for the list of molecular descriptors used, and Table 6 for the summary of those more influential in case of classification models) developed to evaluate in parallel the volume of distribution and fraction of unbound drug in plasma values, due to both parameters are expected to be affected by similar physicochemical drug properties. Input and output data is provided in the supplementary material files, and also a comparison to the commercial software Volsurf+ predictivity for the data available.

  • Systems biology in drug discovery and development. Berg E.L. et al. Drug Discov Today. (5847)
  • KEYWORDS: BIOMARKERS – RISK ASSESSMENT – SYSTEMS BIOLOGY

    This review provides a concise glossary of terminology widely used in the field of systems biology, and presents a list of omics data types (transcriptomics, epigenetics, miRNA, proteomics, phosphoproteomics and metabolomics; see Table 1) and a list of systems biology resources regarding data or tools for analysis and visualitzation of the different types of data related (see Table 2).

  • Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges. Fürzéry AK. et al. Clin Proteomics. (5846)
  • KEYWORDS: BIOMARKERS – CARCINOGENICITY – RISK ASSESSMENT

    Following the tumor biomarker definition (is any molecule produced by a tumor or by the host in response to a cancer cell that is objectively measured and evaluated as an indicator of cancerous processes within the body), table 1 provides the list of 23 FDA-approved protein tumor markers currently used in clinical practice.

  • Idiosyncratic drug-induced liver injury: an update on the 2007 overview. Hussaini SH. et al. Expert Opin Drug Saf. (5845)
  • KEYWORDS: BIOMARKERS – CYP450 – DILI – ENZYMES – HEPATOTOXICITY

    This review covers a wide update on the epidemiology, pathogenic mechanisms, diagnosis, outcome, risk factors for idiosyncratic drug-induced hepatotoxicity, specific classes of drug hepatotoxicity and biomarkers to predict DILI (hepatocellular, cholestatic or mixed cases; see Table 2 for a list of known drugs that induce DILI grouped by therapeutic area of disease treatment and section 8 for specific drugs that lead to DILI).
    Examples of cases reviewed:
    -Perhexiline hepatotoxicity is associated with decreased CYP2D6 activity.
    -CYP2C9 and CYP2C19 pholymorphims do not play a role in DILI.
    -Diclofenac induced DILE and CYP2C9 or CYP2C8.
    -Isoniazid DILI was associated with the CYP2E1.
    -Isoniazid hepatotoxicity is associated with deficiency in 2 defective genes for NAT2 enzyme.
    -Diclofenac hepatotoxicity was associated in patients with at least 1 UGT2B7 allele, postulated to be UGT2B7*2.
    -The double GSTT1-GSTM1 null genotype had almost a threefold increased risk of DILI.

  • Evaluation of serum bile acid profiles as biomarkers of liver injury in rodents. Luo L. et al. Toxicol Sci. (5844)
  • KEYWORDS: ASSAY DATA – BIOMARKERS – HEPATOTOXICITY

    This article presents an assay applicable to rat and mouse serum to evaluate levels of individual bile acids such as cholic acid (CA), glycocholic acid (GCA) and taurocholic acid (TCA) as potential biomarkers of liver injury risk assessment in rodent toxicity studies. The results suggest the potential for relationships to be established between these bile acids and specific liver toxicity findings in comparison with the classical biomarkers of hepatotoxicity (ALT, AST, GLDH, ALP, TBIL, GGT, and total bile acids concentrations).

  • The adverse outcome pathway concept: A pragmatic tool in toxicology. Vinken M. et al. Toxicology. (5843)
  • KEYWORDS: HEPATOTOXICITY – RISK ASSESSMENT – SKIN SENSITIZATION

    This paper gives an overview of several examples of the Adverse Outcome Pathway (AOP) strategy (see Figure 1 for chemical-induced skin sensitization, Figure 2 for drug-induced cholestasis, Figure 3 for chemical-induced liver fibrosis and Figure 4 for chemical-induced liver steatosis cases). Discussion to assess the construction and application of such strategies is addressed, which points out major challenges of AOP development in the quantification and inclusion of dose-response relationships, in the implementation of exposure or toxicokinetic data to name a few. < span>

  • Dose-response approaches for nuclear receptor-mediated modes of action for liver carcinogenicity: Results of a workshop. Andersen ME. et al. Crit Rev Toxicol. (5842)
  • KEYWORDS: CARCINOGENITICY – HEPATOTOXICITY – NUCLEAR RECEPTORS – RISK ASSESSMENT

    This review presents the outcomes of a workshop devoted to engage a diverse group of experts to explore Modes Of Action and dose–response implications focused in the case of nuclear receptor-mediated liver cancer, and to identify common issues and uncertainties relevant to human health risk assessment. In particular, the case studies addressed discussed about the activation of the AHR, the CAR and the PPARalpha receptors. Authors provide information about other ongoing initiatives examining dose-response modelling for different toxicity endpoints (ILSI-HESI Risk21, Human Toxicology Consortium, U.S EPA NexGen).

  • A correlation between the in vitro drug toxicity of drugs to cell lines which express human P450s and their propensity to cause liver injury in humans. Gustafsson F. et al. Toxicol Sci. (5841)
  • KEYWORDS: ASSAY DATA – CYP450 – DILI – DRUG SAFETY – HEPATOTOXICITY – RISK ASSESSMENT

    Collection of data on DILI in humans caused by 104 tested drugs based on information extracted from the scientific literature and from US FDA approved product labels, has been assigned for each drug to one of five DILI concern categories defined in this study (drug toxicity to immortalized human liver epithelial (THLE) cells stably transfected with plasmid vectors which encoded human cytochrome P450s 1A2, 2C9, 2C19, 2D6 or 3A4, or an empty plasmid vector (THLE-Null)), see tables 1-8.

  • A sea of standards for omics data: sink or swim?. Tenenbaum JD. et al. J Am Med Inform Assoc. (5839)
  • KEYWORDS: DATABASE – DATA MINING – SYSTEMS BIOLOGY – TEXT MINING

    A critical commentary on the explosion of unstructured data and the needs to establish and adopt standards in areas of knowledge as the –Omics disciplines. Authors evaluate the current situation and make their own proposal of criteria that data standards guidance should consider, in terms of the standard itself definition, its adoption in the scientific community and its compatibility with other standards.

  • A PERSPECTIVE ON THE PREDICTION OF DRUG PHARMACOKINETICS AND DISPOSITION IN DRUG RESEARCH AND DEVELOPMENT. Di L. et al. Drug Metab Dispos. (5837)
    Involved Partner: PFIZER
  • KEYWORDS: ADME – DRUG SAFETY – METABOLISM – MOLECULAR DESCRIPTORS – PHARMACOKINETICS – STRUCTURE-BASED PREDICTION – TRANSPORTERS – QSAR MODELING

    In this commentary format article, authors present their own experience-based perspectives on the tools and methods of predicting human drug disposition using in vitro and animal data (prediction of half-life, volume of distribution, clearance (see Figure 2 for a General Layout of Drug Clearance Mechanisms), oral bioavailability, and drug-drug interactions). Further discussion on the near future challenges for ADME predictions is provided.

  • The Fall and Rise of Pharmacology – (re-) defining the discipline?. Winquist RJ. et al. Biochem Pharmacol. (5836)
  • KEYWORDS: CYP450 – DRUG DISCOVERY – ENZYMES – GPCR – ION CHANNELS – NUCLEAR RECEPTORS – PHARMACOLOGY – TRANSPORTERS

    This article of the Pharmacology in 21st Century Biomedical Research Special Issue of Biomchemical Pharmacology journal, first reviews the evolution of pharmacology discipline aspects (the receptor concept (receptors as drug targets (i.e., GPCRs, LGICs, VGICs, enzymes) and its evolution (the occupancy theory, the ternary complex model, the constitutive receptor activity, the regulation of receptor activity (desensitization/tachyphylaxis/tolerance, dimerization/oligomerization, endocytosis, pluridimensional efficacy, signal transduction), receptor complexes and allosteric modulation (allosterism, multimers)); second, reviews the technologies used to characterize receptor function (receptor isolation, receptor subtypes, receptor binding assays (following the basic concepts of receptor theory: saturability, high affinity, specificity, reversibility and pharmacologically relevant); third, reviews the molecular phase of pharmacology (cloning, orphan receptors, mutagenesis, receptor crystallization); forth, reviews the genomic aspects linked to pharmacology aspects (human genome map, genome-based targets); and finally discuss future considerations taking advantage of the emerging trends.

September

  • Text mining for systems biology. Fluck J. et al. Drug Discov Today. (5835)
  • KEYWORDS: TEXT MINING – SYSTEMS BIOLOGY

    This article overviews the current state-of-the-art of named entity (BioCreative initiative) and relation extraction approaches, and emphasizes the need of accessing variety of training corpora and developing new engines based on text-mining technologies to move the entire field of the systems biology knowledge forward, and not limit it to the text information exploitation, also to images and table information collections.

  • Integrative ‘omic’ approach towards understanding the nature of human diseases. Peterlin B. et al. Balkan J Med Genet. (5833)
  • KEYWORDS: SYSTEMS BIOLOGY

    Focused on the case of Multiple sclerosis human disease, authors present their approach of subsequent integration of public data repositories (genomic data from genome-wide association studies (dbGAP, for epigenomic, transcriptomic and methylomic data ( Array Express or Gene Expression Omnibus, and for next generation sequencing sequencing databases ( European Nucleotide Archive and Sequence Read Archive); to set up a collected database of features such as genes, mRNAs, microRNAs, CpG islands, proteins and others related with significant alterations regarding diseases (See Figure 1 for details of databases and process integration).

  • Pharmacokinetics. Fan J. et al. Biochem Pharmacol. (5831)
  • KEYWORDS: ADME – DRUG DISCOVERY – METABOLISM – PHARMACOKINETICS

    A comprehensive review on why knowledge on the Pharmacokinetics (PK) properties of a New Chemical Entitny (NCE) is critical to its selection, the PK principles (including guidelines for conducting PK studies, equations required for characterizing and understanding the PK of a NCE and its metabolite/s), apart from other discussions on the determination of in vivo PK parameters, or estimation of PK parameters of a metabolite following administration of an NCE to name a few of eTOX consortium interest.

  • Is “Big Data” creepy?. Cumbley R. et al. Computer Law & Security Review. (5830)
  • KEYWORDS: DATABASE

    This article points out the need to develop and refine the techniques used to extract useful information from the current explosion of unstructured information, named as Big Data. Different aspects of the Big Data lifecycle are discussed (collection, combination, analysis and use), with particular emphasis in the privacy implications and its regulation (see Table 1 for a summary of the European Regulators’ Views on Big Data, based on the Article 29 Working Party’s opinion on Big Data and the proposed new General Data Protection Regulation).

  • Dogs and monkeys in preclinical drug development: the challenge of reducing and replacing. Pellegatti M. et al. Expert Opin Drug Metab Toxicol. (5829)
  • KEYWORDS: ADME – RISK ASSESSMENT

    eTOX cited

    Pellegatti presents a review of the opportunities to reduce the number of dogs and monkeys currently used in pharmaceutical research (see Table 1 for recents stats of experiments involving dogs and primates in EU), and highlights present efforts like eTOX project goals as potential solutions to drop down the number of animals needed in the drug discovery field to ensure safety assessment.Table 2 summarizes the difficulties and potential solutions for implementin a new vision in this concern.

  • Tissue specific phosphorylation of mitochondrial proteins isolated from rat liver, heart muscle and skeletal muscle. Bak SY et al. J Proteome Res. (5825)
  • KEYWORDS: ASSAY DATA – CARDIOTOXCITY – HEPATOTOXICITY – MITOCHONDRIAL TOXICITY – MUSCULOSKELETAL TOXICITY

    See Table 1 for a list of 32 Tissue-specific phosphorylation of proteins involved in selected mitochondrial biological processes
    in liver, heart and skeletal muscle in vivo in rat, Table 2 for a list of 28 Kinases identified in mitochondrial preparations from rat liver, heart an skeletal muscle, and supplementary tables 1-4 for data from purity of mithochondrial preparations from rat liver, heart and skeletal muscle and GO and KEGG analysis, including MitoCarta information.

  • Impact of Physiologically-based Pharmacokinetic Modelling and Simulation in Drug Development. Shardlow C. et al. Drug Metab Dispos. (5824)
    Involved Partner: GSK

  • KEYWORDS: ADME – ASSAY DATA – PHARMACOKINETICS

    An example of physiologically-based pharmacokinetic modelling and simulation performed by GSK over the last 5 years on a set of 41 proprietary drugs applient the simCYPTM, a population-based pharmacokinetic simulator. See Table 1 for data regarding the use cases discussed, and tables 2-4 for scenario (i.e., CYP involved, metabolismm, PXT up-regulation), simulation and impact of the studies addressed to inform regulatory communications (Table 2), to have impacted clinical development decisions (Table 3) and to aid the mechanistic understanding of clinical observations (Table 4).

  • Detection of Phospholipidosis Induction: A Cell-Based Assay in High-Throughput and High-Content Form. Shahane SA. et al. (5823)
  • KEYWORDS: ASSAY DATA – PHOSPHOLIPIDOSIS

    Authors present and report data for a series of 24 compounds (see Table 2) based on their developed and validated cell-based phospholipidosis assay in a 1536-well plate format.

  • Biomarkers in Pharmacology and Drug Discovery. Anderson DC et al. Biochem Pharmacol. (5822)
  • KEYWORDS: BIOMARKERS – DRUG DISCOVERY – PHARMACOLOGY

    This article of the Redefining Pharmacology Special Issue of Biomchemical Pharmaoclogy journal, provides a comprehensive review in the biomarkers field. Table 1 lists definitions of the biomarker types variety, and Table 2 shows a list of well-known cancer biomarkers, together with their drug targets and associated diagnostics.

  • Rule-based multi-scale simulation for drug effect pathway analysis. Hwang W. et al. BMC Med Inform Decis Mak. (5818)
  • KEYWORDS: PARHAMCOLOGY – PATHWAY

    This article proposes a rule-based multi-scale modelling platform (see Figure 1) focused on Type 2 diabetes data, as an example of computer model-based experiment which can help to understand drug mechanism and show other ways to effectively apply existing drugs for new targets.

August

  • A systematic approach for identifying and presenting mechanistic evidence in human health assessments. Kushman ME. et al. Regul Toxicol Pharmacol. (5817)
  • KEYWORDS: CARCINOGENICITY – RISK ASSESSMENT – TEXT MINING

    Focused on the question: What are the mechanisms by which a chemical may cause carcinogenicity in the target issue?, authors present a systematic approach to strengthen the validity of scientific evidence evaluations, in terms of chemical toxicity, extracted from the literature through a devised strategy querying the PubMed database(see Table 1). Several literature trees are reported for different terms selected (see figures 3, 4 and 5).

  • From data point timelines to a well curated data set, data mining of experimental data and chemical structure data from scientific articles, problems and possible solutions. Ruusmann V. et al. J Comput Aided Mol Des. (5816)
  • KEYWORDS: DATABASE – DATA MINING – QSAR MODELING

    This article presents a systematic and reproducible workflow (see Figure 1) for collecting series of data points from scientific literature and assembling a database that is suitable for the purposes of high quality modelling and decision support. Focused on the Tetrahymena pyriformis acute aquatic toxicity endpoint data retrieved from the literature (86 publications), authors discuss about different issues on data curation and data quality for both cases, chemical information and toxicological data related. They highlight different curation scenarios for toxicity values summarized in this article and discussed with additional detail in the Supplementary Material (broken reference, changing the sing of a number, interchanging or changing digits inside a number, deviation from trend line).

  • Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data. Hettne KM. et al. BMC Med Genomics. (5815)
  • KEYWORDS: RISK ASSESSMENT – SOFTWARE – SYSTEMS BIOLOGY – TEXT MINING

    A combination of Gene Set Analysis and Next-generation text mining technique retrieves from the literature (13,834,150 PubMed IDs from January 1, 1980 to January 6, 2011) a high amount of chemical response-specific gene sets (~30,200 gene sets for both human and mouse using next-gen TM compared to ~1,200 gene sets for human and ~600 gene sets for mouse using the Comparative Toxicogenomics Database). Details of methodology applied are provided in this article, and the resulting data sets are available for download in a generic format (here). Authors remark the current limitation of their approach in terms of not taking the nature of the relation (for example expression (negative or positive) or phosphorylation) between the genes and chemicals or genes), but this strategy is challenging for supporting the identification of chemical treatment, the elucidation of pharmacological mechanisms and facilitate the compound toxicity profile comparison.

  • The current status of biomarkers for predicting toxicity. Campion S. et al. Expert Opin Drug Metab Toxicol. (5814)
  • KEYWORDS: BIOMARKERS – RISK ASSESSMENT

    This article reviews the current role of biomarkers in the predicting toxicity field, where specific organ toxicity, that is, the safety biomarkers, are progressively identified to assist the drug development process and contribute to the understanding of drug safety. In particular, authors discuss the cases of biomarkers of testicular injury and dysfunction (see section 2.2) and biomarkers of acute kidney injury (section 2.3), highlight the translation of emerging biomarkers from preclinical species to human populations (section 2.4) and revise the regulatory qualification of the translational biomarkers issues (section 2.5).

  • The Toxicology Letters presents an special issue on Abstracts of the 49th Congress of the European Societies of Toxicology (EUROTOX) (Volume 221, Supplement, 28 August 2013, Pages S8).
  • Concordance of gene expression in human protein complexes reveals tissue specificity and pathology. Börnigen D. et al. Nucl. Acids Res. (5813)
    Involved Partner: DTU
  • KEYWORDS: DATABASE – SYSTEMS BIOLOGY – RISK ASSESSMENT

    The TissueRanker is presented as a novel predictive method for disease/tissue association based on co-expression measure of transcripts within human protein complexes, leveraging a recently published global map of human gene expression data from 5,372 samples and representing 128 different tissues in 4 different cell types (normal, disease, neoplasm and cell line). This tool can serve as reference to sort out tissue specificity in different phenotypes observed.

  • Toward a new paradigm for the efficient in vitro-in vivo extrapolation of metabolic clearance in humans from hepatocyte data. Poulin P. et al. J Pharm Sci. (5811)
  • KEYWORDS:

    .

  • Case study: the role of mechanistic network models in systems toxicology. Hoeng J. et al. Drug Discov Today. (5810)
  • KEYWORDS: NETWORKS – SYSTEMS BIOLOGY

    Interesting review on the current network approaches that facilitate the detailed mechanistic understanding of the impact of a given stimulus on a biological system, for instance, the discovery of biological pathways affected in response to active substances. The network-based approaches are revised through 2 use case studies (the xenobiotic metabolism network as a biomarker for cigarette smoke exposure response across in vivo and in vitro systems, and mechanistic approach to the quantification of perturbed processes in the lungs of C57/BI6 mice following cigarette smoke exposure and cessation).

  • A high content screening assay to predict human drug-induced liver injury during drug discovery. Persson M. et al. J Pharmacol Toxicol Methods. (5809)
  • KEYWORDS:

    .

  • Integrated Analysis of Drug-Induced Gene Expression Profiles Predicts Novel hERG Inhibitors. Babcock JJ. et al. Plos One. (5808)
  • KEYWORDS: hERG – PHARMACOLOGY – SYSTEMS BIOLOGY

    Based on drug-induced transcriptional responses data extracted from Connectivity Map database, this article reports the similarities among a set of 300,000 structurally diverse hERG antagonists. The results (see Figure S2 for LQT-annotated drugs, Table S1 for Drugs annotations and SMILES strings, and Figure S4 for correlation between hERG activity and transcriptional similarity) suggest the microarrays data of chemical candidates as a novel proxy measurement that correlates with the ability of them to block the hERG channel.

  • Sensitivity of species to chemicals: Dose-response characteristics for various test types (LC50, LR50 and LD50) and modes of action. Hendriks AJ. et al. Ecotoxicol Environ Saf. (5807)
  • KEYWORDS:

    .

  • Accelerated Caco-2 cell permeability model for drug discovery. Sevin E. et al. J Pharmacol Toxicol Methods. (5804)
  • KEYWORDS: ADME – ASSAY DATA – P-gp

    20 marketed drugs (see Table 1 for names and experimental data values), covering a range of permeabilities and interactions with efflux transporters and therapeutic indications, were selected to evaluate a new accelerated 6 days Caco-2 model (traditionally performed between 21-25 days), and comparison between results points out the high efficiency of this new model and implies an important reduction of costs for screening and shorts the time-feedback to the drug design team.

  • Facilitating the use of large-scale biological data and tools in the era of translational bioinformatics. Kouskoumvekaki I. et al. Brief Bioinform. (5803)
    Involved Partner: DTU
  • KEYWORDS: DATABASES

    Compilation and briefings of open source available services for visualization, integration and enrichment of biological information (Cytoscape, BioGPS, DAVID), services for bioinformatics workflows (Taverna, GenePattern (see Table 2 for examples of use), Galaxy (see Table 3 for examples of use), Kepler) and the need of integration of biommedical ontologies in the dairy computational workflows that support current research in the translational bioinformatics field.

  • Evaluation of an in silico cardiac safety assay: Using ion channel screening data to predict QT interval changes in the rabbit ventricular wedge. Beattie KA. et al.J Pharmacol Toxicol Methods. (5802)
    Involved Partner: GSK
  • KEYWORDS: CARDIOTOXICITY – hERG – LQT – RISK ASSESSMENT

    This article reports an example of a new strategy in the assessment of human clinical QT prolongation occurrence, based on GSK studies data (77 compounds were included for evaluation when using ion channel data from the PatchXpress assay, 121 compounds when using data from the IonWorks/FLIPR assay, and 372 compounds when using predicted IC50 values from the QSAR model; all compounds belong to a wide variety of chemical and therapeutic categories and constitute an unprecedented set size for the in silico assay presented).

  • Validating therapeutic targets through human genetics. Plenge RM. et al. Nat Rev Drug Discov. (5801)
  • KEYWORDS: DRUG DISCOVERY – PHARMACOLOGY – SYSTEMS BIOLOGY

    This review points out the challenges of human genetics consideration at the time to nominate a protein or biomolecule as a potential drug target. The evaluation of human genetic data can facilitate the understanding of perturbing a target in a given manner that can benefit patients and have minimal toxicity effect or at least acceptable side effect. A collection of gene-drug pairs cases is presented in Table 2, and Box 2 lists criteria of gene-drug pairs in target validation stage.

  • Novelty in the target landscape of the pharmaceutical industry. Agarwal P. et al. Nat Rev Drug Discov. (5800)
    Involved Partner: GSK
  • KEYWORDS: DRUG DISCOVERY – PHARMACOLOGY

    Based on information gathered by Citeline in the “Pharmaprojects” database, authors present statistics of the competition between pharmaceutical companies in terms of ‘proven’ and ‘novel’ targets, where totals are 247 and 712, respectively. The analysis shows that the pipelines in the industry are fairly diverse, with more than half of the novel targets being investigated by only one company, when projects associated with multi targets are excluded.

  • Models for open innovation in the pharmaceutical industry. Schuhmacher A. et al. Drug Discov Today. (5799)
  • KEYWORDS: DRUG DISCOVERY

    Lastly, pharmaceutical industry moves forward the concept of open innovation adopting more effective approaches to carry out their R&D organizations. This article analyses the R&D models of 13 multinational pharmaceutical companies (see key financial and R&D figures in Table 1) and describes 4 new types of innovation models (Knowledge integrator, leverager, creator and translator; see Figure 2).

  • Bioinformatic Analysis of 302 Reactive Metabolite Target Proteins. Which Ones Are Important for Cell Death?. Hanzlik RP et al. Toxicol Sci. (5798)
  • KEYWORDS: METABOLISM – RISK ASSESSMENT

    This article reports 302 proteins (rat, mouse, human) known to be targeted by 41 cytotoxic chemically reactive metabolites. See Table 9 that compiles a list of 28 target proteins with the strongest direct link to toxicity based on different sources information (KEGG, GO-BP and GO-MF) and check the support material for a full list of target proteins and their corresponding partner.

  • Towards a detailed atlas of protein-protein interactions. Mosca R. et al. Curr Opin Struct Biol. (5795)
    Involved Partner: CNIO

  • KEYWORDS: DATABASES – SYSTEMS BIOLOGY

    Review on the characteristics of the current available protein interactions data resources (see Table 2 where a list of 15 available resources for the prediction of protein-protein interactions are gathered), and the needs of standardization and curation of their data contents to improve the data quality and reduce efforts needed when scientists attempt to make predictions based on the knowledge stored (see Table 1 for examples of number of protein-protein interactions in the principal curated databases (Intact, BioGRID, MINT, DIP, BIND, HPRD). Authors discuss how an organization of the experimental knowledge of the interaction space in a possible Atlas of protein-protein interactions (see Figure 1) would be beneficial for the scientific community, since we should have the binary information provided by different networks centralized and complemented with related experimental data collected by text mining techniques application.

July

  • Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches. Singh KP. et al. Toxicol Appl Pharmacol. (5794)
  • KEYWORDS: CARCINOGENICITY – MOLECULAR DESCRIPTORS – RISK ASSESSMENT – SOFTWARE

    For a set of 834 structurally diverse chemicals ectracted from CPDB (466 positive and 368 non-positive carcinogens), 12 non-quantum mechanical molecular descriptors were derived (see Table 1 and Figure 2b) to develop 2 types of models (a probabilistic neural network and a generalized regression nural network). Technical details and discussion of their applicability domain are reported and show the potential of predictivity of both models in the case of carciongenicity prediction. In addition, authors report in Table 4a a classification accuracies of positive and non-positive carcinogens form different studies reported in the literature.

  • PK/PD assessment in CNS drug discovery: Prediction of CSF concentration in rodents for P-glycoprotein substrates and application to in vivo potency estimation. Caruso A. et al. Biochem Pharmacol. (5793)
    Involved Partner: ROCHE
  • KEYWORDS: ASSAY DATA – NEUROTOXICITY – P-gp – PHARMACOKINETICS

    Based on experimental data of 61 central nervous system compounds, a study to evaluate the relationship between cerebrospinal fluid to unbound plasma drug partitioning in rats and the mouse PgP efflux ratio obtained from in vitro transcellular studies shows tha the predicted cerebrospinal fluid can be used as a default approach to understand the pharmacokinetics/pharmacodynamics relationships in central nervous system models, and can support the extrapolation of efficacious brain exposure for those drug candidates from rodent to human.

  • 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.

  • Are hERG channel blockers also phospholipidosis inducers?. Sun H. et al. Bioorg Med Chem Lett. (5791)
  • KEYWORDS: hERG – PHARMACOLOGY – PHOSPHOLIPIDOSIS – RISK ASSESSMENT

    Based on pharmacophore models of hERG inhibition and phospholipidosis induction, a set of over 4000 non-redundant drug-like compounds were evaluated for their hERG inhibitory activity and phopholipidosis inducing potential in a quantitative high throughput screening format. Results highlight that hERG blockers and phospholipidosis inducers share a large chemical space (see Figure 5 for results analysis). This exercise serves as a general guideline to avoid phospholipidosis observations in the projects where the hERG channel is the therapeutic target.

  • One hundred thousand mouse clicks down the road: selected online resources supporting drug discovery collected over a decade. Villoutreix BO. et al. Drug Discov Today. (5785)
  • KEYWORDS: DATABASES – DRUG DISCOVERY – SOFTWARE

    eTOX cited

    Review on the different types of online tools (ligand-binding-pocket analysis, virtual screening, compound profiling, off-targets and repurposing, semantic web, etc.) and databases (chemistry databases) collected during the last decade for the authors at VL3D (1435 links on 15 April 2013), and report on recent services with related information (i.e., the eTOX library in the area of toxicology).

  • Systems pharmacology strategies for Drug Discovery and Combination with applications to CVD. Li P. et al. J Ethnopharmacol. (5784)
  • KEYWORDS: ASSAY DATA – DATA MINING – DRUG DISCOVERY – SYSTEMS BIOLOGY

    Results of an integrative machine-learning approach presented for predicting side-effects profiles and understanding of their mechanims show that information about pathways, protein-protein interaction and to a lower extent protein domains also plays an important role in side effect characterization.

  • Integrative relational Machine-Learning Approach for Understanding Drug Side-Effect Profiles. Bresso E. et al. BMC Bioinformatics. (5783)
  • KEYWORDS: ADME – CARDIOTOXICITY – DRUG DISCOVERY – NETWORK – PHARMACOKINETICS – PHARMACOLOGY – SYSTEMS BIOLOGY

    This article reports an integrated systems pharmacology platform for drug discovery ( 1) disease-specific drug-target network (769 drugs and their 372 cardiovascular targets (see Support Material Table S1), 2) database of literature-reported associations, chemicals and pharmacology (510 medical herbs registered in China with more than 31,000 ingredients (TCMSP), and 3) large-scale systematic analysis combining pharmacokinetics, chemogenomics, pharmacology and systems biology data), focused in a set of herbal medicines prescribed for cardiovascular diseases treatment.

  • In vitro blood-brain barrier model adapted to repeated-dose toxicological screening. Fabulas-da Costas A. et al. Toxicol In Vitro. (5782)
  • KEYWORDS: ASSAY DATA – ADME – NEUROTOXICITY

    The European Predict-IV consortium reports their study of the effect on Blood-Brain barrier (BBB) permeability of 12 selected market drugs (see Table 1 for names, CAS number, Molecular weight, therapeutic class and experimental data) after 14 days of repeated treatment to a single preselected concentration.

  • Dealing with metadata quality: The legacy of digital library efforts. Tani A. et al. Inf Process Manag. (5781)
  • KEYWORDS: DATABASES – REGULATORY GUIDELINES

    Briefings on metadata quality in digital libraries, quality assessment frameworks, and approaches to metadata qualitiy issues ( 1) metadata quidelines, standard and application profiles, 2) metadata evaluation approaches, 3) semi-automatic metadata generation approaches, 4) metadata cleaning, enchancement and augmentation approaches, 5) remarks on approaches for resolving metadata quality problems (see Table 2)).

  • Toxicity Assessments of Nonsteroidal Anti-Inflammatory Drugs in Isolated Mitochondria, Rat Hepatocytes, and Zebrafish Show Good Concordance across Chemical Classes. Nadanaciva S. et al. Toxicol Appl Pharmacol. (5780)
  • KEYWORDS: ASSAY DATA – RISK ASSESSMENT

    Analysis of data retrieved from 3 assay platforms (a) respiration of rat liver mitochondria, b) panel of mechanistic endpoints, via high content imaging in rat hepatocytes, and c) viability and liver/gastrointestinal morphology of zebrafish) for a set of eleven NSAIDs (flufenamic acid, tolfenamic acid, mefenamic acid, diclofenac, meloxicam, sudoxicam, piroxicam, diflunisal, acetylsalicylic acid, nimesulide, and sulindac (and its two metabolites, sulindac sulfide and sulindac sulfone)) shows how the combination of these different approaches can help to highlight liver and gastrointestincal toxicities since they reflect the common mechanisms of toxicity that are fundamental across cell or organ systems.

  • High-throughput respirometric assay identifies predictive toxicophore of mitochondrial injury. Wills LP. et al. Toxicol Appl Pharmacol. (5779)
  • KEYWORDS: ASSAY DATA – MITOCHONDRIAL TOXICITY

    Screening of 1760 compounds from the LOPAC and ChemBridge DIVERSet libraries retrieved that 31 (see Table 1 for names and toxicity potential classification) of these assayed compounds decreased uncoupled respiration using a respirometric assay. A cheminformatic analysis of these toxicants grouped by their chemical similarity identified previously uncharacterized mitochondrial toxicants from the ChemBridge DIVERSet, and evidences the advantage of appling this type of approaches in the prediction of mitochondrial toxicity as risk assessment strategies.

  • Drug Metabolites as Cytochrome P450 Inhibitors: a Retrospective Analysis and Proposed Algorithm for Evaluation of the Pharmacokinetic Interaction Potential of Metabolites in Drug Discovery and Development. Callegari E. et al. Drug Metab Dispos. (5778)
  • KEYWORDS: ADME – CYP450 – METABOLISM – PHARMACOLOGY – REGULATORY GUIDELINES

    Based on data of 33 proprietary molecules, this article proposes an algorithm to support the need of evaluating the potential of a drug metabolite to inhibit CYP450 activity rather than the parent drug solely, when clinical drug-drug interactions are analysed (see table 1, 2 and 3 for comparison of parent and metabolite in vitro CYP Ki values for the clinical development candidates).

  • In silico methods to predict drug toxicity. Roncaglioni A. et al. Curr Opin Pharmacol. (5777)
  • KEYWORDS: QSAR MODELING – REGULATORY GUIDELINES – RISK ASSESSMENT

    A 3 pages reviewing about in silico methods to characterize the toxicity of pharmaceuticals (tools to predict toxicity endpoints (ie. genotoxicity or organ-specific models) and to address ADME processes) and methods focused on protein-ligand docking binding, which are experiencing a shift from classical concepts to become decision support systems for the experts. Authors comment on the first attempts of legislation to accept the in silico methods to estimate toxicity, and list problems and future perspectives ( 1) data availability-solutions: introduction of an independent 3rd party entity allowing inclusion of confidential data into a database and use of software capable of extracting rules that can be run by owner of the data and shared on a limited basis, 2) data quality and standardization, 3) definition of the different toxicity endpoints, 4) role of systems biology merging data sets of different origin and contributing to the elucidation of mechanisms, and 5) in silico models not only for classical chemical structures also new types of therapeutics emerging like peptides and nanomaterials).

  • Methodologies to Assess Drug Permeation Through the Blood-Brain Barrier for Pharmaceutical Research. Passeleu-Le Bourdonnec C. et al. Pharm Res. (5776)
  • KEYWORDS: DRUG DISCOVERY – PATHWAYS – RISK ASSESSMENT

    An overview of different aspects related with the assesment of Blood-Brain Barrier permeability (physiology of BBB, mechanisms of transport through the BBB, and strategies for improving brain penetration) and current methodologies (in silico, in vitro(see Figure 1), in vivo) available for this purpose. Several models are presented and should be adapted to the new drug delivery systems for a complete understanding of each drug mode of action.

  • Potentials and limitations of nonclinical safety assessment for predicting clinical adverse drug reactions: correlation analysis of 142 approved drugs in Japan. Tamaki C. et al. J Toxicol Sci. . (5775)
  • KEYWORDS: DRUG SAFETY – PHARMACOLOGY – RISK ASSESSMENT

    This study presents a comprehensive anaylisis of the correlation between the 1256 adverse drug reactions (ADR) observed in human for a list of 142 Japan marketed drugs (approved from 2001 to 2010). The analysis was stratified based on type of ADR (target organ where ADRs were observed (see Table 1), severity and incidence of ADR (see Table 2)), type of drugs (see Figure 2 and Table 3), administration route of drugs (see Figure 2 and Table 5), and therapeutic category of drugs (see Figure 2 and Table 6). Authors report a comparison of target organs and correlation of ADR between small and large molecule drugs (see Table 4) and the proportion of target organs of ADR in each therapeutic category (see Table 7). The results reveal a poor corraltion among therapeutic categories due to basically to the fact that a large proportion of ADRs correspond to gastrointestinal, neurological and hepatobiliary ADRs, which do not have relation with pharmacological activities. See Support Material for a list of Representatives for the toxicological findings (Table 1A) and the relevant pharmacological actions (Table 1B) in animals considered to be concordant with ADRs in humans.

  • Bayesian integrated testing strategy to assess skin sensitization potency: from theory to practice. Jaworska J. et al. J Appl Toxicol. (5773)
  • KEYWORDS: ASSAY DATA – SKIN SENSITIZATION – RISK ASSESSMENT

    Based on a 145 chemicals set (fragrances, preservatives, dyes, dye precursors, halogenated alkanes and solvents) which covers a wide range of physicochemical properties (see support material for compounds informations, SMILES, experimental data and in silico predictions), this article reports a Binary Network Integrated Testing Strategy (124 chemicals as training set and 21 as test set).

June

  • OCTN Cation Transporters in Health and Disease: Role as Drug Targets and Assay Development. Pochini L. et al. J Biomol Screen. (5772)
  • KEYWORDS: DRUG DISCOVERY – TRANSPORTERS – PHARMACOLOGY

    Review of structure information and physiological roles of each OCTN1, 2 and 3, with a detailed discussion of these transporters involvement in human pathologies. Find their substrate specificity, the roles in cell physiology and the association of altered function with pathologies in Table 1.

  • The value of selected in vitro and in silico methods to predict acute oral toxicity in a regulatory context: results from the European Project ACuteTox. Prieto P. et al. Toxicol In Vitro. (5771)
  • KEYWORDS: ASSAY DATA – DATABASE – NEUROTOXICITY – PROJECT

    ACuteTox project reports a challenging exercise to assess the predictive capacity of the developed testing strategies applied for a set of 32 chemicals (see data in tables 1, 3, 4 and 5), and presents a compilation of in vitro and in silico methods included in the prevalidation study together with their abbreviations, test systems, endpoints, time of exposure and expression results (see Table 2).

  • Use of in silico systems and expert knowledge for structure-based assessment of potentially mutagenic impurities. Sutter A. et al. Regul Toxicol Pharmacol. (5770)
    Involved Partners: BHC, AZ, NVS, PFIZER, ROCHE, LDB, GSK
  • KEYWORDS: GENOTOXICITY – MUTAGENICITY – QSAR MODELING – REGULATORY GUIDELINES – SOFTWARE – STRUCUTRE-BASED PREDICTION

    Information relevant to the development of a draft harmonized tripartite guideline ICH M7 on potentially DNA-reactive/mutagenic impurities in pharmaceuticals and their application in practice as intercompanies exercise. This article reviews the well-known srtuctural alerts for DNA reactive, mutagenic carciongens (section 2.1), provides recommendations for the validation and application of new structural alerts for DNA reactive mutagens with unknown carcinogenic potential (section 2.2), and reviews use of empiral and rule-based expert systems (DEREK and ToxTree) and, QSAR-based systems (MultiCASE/MC4PC, Leadscope model applier and SciQSAR) (Section 3).

  • In silico models for predicting ready biodegradability under REACH: A comparative study. Pizzo F. et al. Sci Total Environ. (5769)
  • KEYWORDS: REGULATORY GUIDELINES – SOFTWARE

    Based on a dataset of 722 compounds extracted from the OECD toolbox version 2.0 and BIOWIN version 4.10, this article presents a comparison between 4 models (provided by different platforms: VEGA, TOPKAT, BIOWIN and START) to predict their biodegradability feature. One can find for each model the following information: data source, description of the model, output and its applicability domain (see Table 2 for a summary of main features).

  • A decade of Systems Biology: where are we and where are we going to?. Peitsch MC. et al. Drug Discov Today. (5768)
  • KEYWORDS: SYSTEMS BIOLOGY

    Systems Biology is a biomedical research approach, its application leads to new challenges in pharmacology and toxicology, diagnostics and drug development fields, and even intends to develop a more precise and perzonalized approach to medicine, from therapeutic to prevention perspectives. Emerging initiatives of Big Science scale programs tackle the understanding of major diseases, the risk of exposure to drugs and environmental toxins.

  • Use of category approaches, read-across and (Q)SAR: General considerations. Platlewicz G. et al. Regul Toxicol Pharmacol. (5767)
  • KEYWORDS: MUTAGENICITY – QSAR MODELING – SKIN SENSITIZATION – REGULATORY GUIDELINES – REPRODUCTIVE AND DEVELOPMENTAL

    This article attempts to fill the gap on guidance of how to practically apply categorisation approaches. Limitations of the available regulatory guidance are discussed and an overview of a systematic yet practical approach is presented with details on analogue identification, rationale for grouping, substance identity, and a list of endpoints covered (physicochemical parameters, aquatic toxicity, biodegradation,) bioaccumulation, acute mammalian toxicity (oral route, dermal route, inhalation route, irritation, skin irritation, eye irritation, skin sensitization, mutagenicity), repeated-dose toxicity, and reproductive and developmental toxicity) with their corresponding justification.

  • Evaluating the In Vitro Inhibition of UGT1A1, OATP1B1, OATP1B3, MRP2 and BSEP in Predicting Drug-Induced Hyperbilirubinemia. Chang JH. et al. Mol Pharm. (5765)
  • KEYWORDS: ADME – ASSAY DATA – ENZYMES – METABOLISM – TRANSPORTERS

    A proposed surrogate probe substrates (atazanavir, indinavir, ritonavir, nelfinavir, bromfenac, troglitazone and trovafloxacin) to evaluate the in vitro inhibition of UGT1A1 (see tables 1 and 2), OATP1B1 (see Table 1) and BSEP (see tables 1 and 3) is reported to suitable to assess bilirubin disposition, and help on the hyperbilirubinemia prediction.

  • Usefulness of the addition of beta-2-microglobulin, cystatin C and C-reactive protein to an established risk factors model to improve mortality risk prediction in patients undergoing coronary angiography. Nead KT. et al. Am J Cardiol. (5761)
  • KEYWORDS: BIOMARKERS – CARDIOTOXICITY

    Authors report an study that evidences that beta-2-microglobulin, cystatin C and C-reactive protein concentrations measurement in patients referred for coronary angiography can help in mortality prediction as cardiotoxicity biomarkers.

  • QSAR Models for the Prediction of Plasma Protein Binding. Ghafourian T. et al. Bioimpacts. (5760)
  • KEYWORDS: MOLECULAR DESCRIPTORS – QSAR MODELING – RISK ASSESSMENT

    Based on protein binding data of 794 compounds extracted from the literature (training set 662, external validation 132), a battery of models (stepwise regression models, general regression tree models and boosted tree models) were developed, and those following the boosted tree concept were proved to retrieve more accurate predictions (see Table 1 for a summary of prediction accuracy for all QSAR models evaluated).

  • 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.

  • Safety pharmacology — Current and emerging concepts. Hamdam J. et al. Toxicol Appl Pharmacol. (5757)
    Involved Partners: UCB, SAD, AZ, BI, ROCHE
  • KEYWORDS: DRUG SAFETY – PHARMACOLOGY

    This review describes the safety pharmacology studies defined in the International Conference on Harmonisation guidelines (S7A and S7B), which evaluate the effects of a new chemical entity at both anticipated therapeutic and supra-therapeutic exposures on major organ systems (cardiovascular (see Table 1), central nervous (see tables 2 and 3), respiratory (see Table 4), renal (see Table 6) and gastrointestinal (see Table 5)). Outlines of the current practices and emerging concepts (ie., use of non-standard species, biomarkers, or combination of toxicology and safety pharmacology assessments; see Figure 2) are discussed pointing out their advantage at time of providing mechanistic insights for the adverse effects understanding .

  • Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing. Xu R. et al. BMC Bioinformatics. (5756)
  • KEYWORDS: TEXT MINING

    Based on a disease lexicon resulting from UMLS and Human Disease Ontology and the DrugBank drug lexicon, this article presents a new pattern-based method to extract biomedical relationships between drugs and diseases which retrieved a total of 34,305 unique drug-disease pairs from 20 million MEDLINE abstracts. Details of the entire process (see Figure 1) are provided with semantic analysis of results and particular cases are discussed.

  • The Tox21 robotic platform for the assessment of environmental chemicals – from vision to reality. Attene-Ramos MS. et al. Drug Discov Today. (5755)
  • KEYWORDS: ASSAY DATA – DATABASE – RISK ASSESSMENT

    With the purpose of advancing in vitro toxicological testing, the Tox21 collaboration (EPA, FDA, NTP and NCGC) has developed a battery of in vitro assays to generate target-specific and mechanism-based readouts to be integrated with HTS data. This article provides the description of this platform (screening process within the Tox21, preparation of the Tox21 compounds library, the Tox21 robot and the validation of the Tox21 robotic system, and the data processing and analyses) and their capabilities and usabilities in terms of identification of mechanisms of compound action, prioritizing substances for further toxicological evaluation in vivo and developing predictive models for biological responses. Currently, the collection contains data for 10,000 commpounds.

  • 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.

May

  • Perspectives for integrating human and environmental risk assessment and synergies with socio-economic analysis. Péry AR. et al. Sci Total Environ. (5750)
  • eTOX cited

    KEYWORDS: PROJECT – REGULATORY GUIDELINES – RISK ASSESSMENT

    This reviews the discussions of the first expert meeting (April 2012) and the Scientific Advisory Board meeting (September 2012) within the HEROIC project (Health and Environmental Risks: Organisation, Integration and Cross-fertilisation of Scientific Knowledge, FP7). The aim of this project is to build integrated risk assessment procedures considering the mixture of toxicity effects into human and environmental risk information available. Experts explain the current situation of data sharing and storage, and make their own recommendation to improve in benefit of socio-econimic aspects. Topics disscussed are: improving data availability for risk assessors, list of non-test tools to extrapolate in hazard assessment, integrated testing strategies, adverse outcome pathways, accounting for uncertainty in integrated hazard assessment; and their perspectives in integrated exposure assessment and integrated risk assessment of chemical mixtures.

  • In vitro approaches to investigate cytochrome P450 activities: update on current status and their applicability. Ong CE. et al. Expert Opin Drug Metab Toxicol. (5749)
  • KEYWORDS: CYP450 – METABOLISM – PHARMACOLOGY – QSAR MODELING

    A review focuses on the use of in vitro methodologies to examine CYPs’ role in drug metabolism and interaction, the use of in silico approaches in complementing and supporting the in vitro data (see section 3), and the challenges in extrapolating in vitro data to in vivo situations. A list of commonly used substrates and inhibitor probes for major human CYP enzymes is provided in Table 1.

  • Modeling phospholipidosis induction: reliability and warnings. Goracci L. et al. J Chem Inf Model. (5748)
  • KEYWORDS: ASSAY DATA – MOLECULAR DESCRIPTORS – PHOSPHOLIPIDOSIS

    Based on a set of 466 publicly available compounds compiled from 7 literature sources (see Table 1 for references) with data including humans, rats, dogs and mice measurements, a curated set of 330 compounds is proposed due to the number of inconsistencies found between different sources (see Table 2) and a compromise between quantity and quality is discussed to illustrate how the construction of a database can be critical to evaluate the quality of models. In this case, a PLS model to predict phospholipidosis potential of small molecules is presented.

  • Toward creation of a cancer drug toxicity knowledge base: automatically extracting cancer drug-side effect relationships from the literature. Xu R. et al. J Am Med Inform Assoc. (5746)
  • KEYWORDS: DATABASE – TEXT MINING

    Focused on the case of cancer drug-side effects, authors present their exercise to extract drug-side effects co-occurrence pairs from the literature using a cancer drug lexicon. They built a cancer drug list based on drug-disease treatment pairs from ClinicalTrials.gov (115,026 clinical trial XML files) which retrieved a total of 52,066 unique drug-disease pairs, where 17,386 pairs were related to cancers (using the semantic type ‘Neoplastic Process’ from UMLS) and after MEDLINE-based filtering step they selected manually 100 cancer drugs from the top-ranked. In parallel, they built a side effects lexicon of 49,625 terms based on MedDRA terms (70,177 terms). See Figure 1 for a flow chart depicting the extraction, filtering, ranking and analysis of cancer drug-side effect relationships. The results of this study show that cancer drugs that have the same SEs tend to have overlapping gene targets and overlapping indications, indicating potential value for in silico cancer drug target discovery and drug repurposing.

  • Advances in the development and use of human tissue-based techniques for drug toxicity testing. Clotworthy M. et al. Expert Opin Drug Metab Toxicol. (5745)
  • KEYWORDS: DRUG SAFETY – RISK ASSESSMENT

    A brief overview of the major organs affected by adverse events (Digestive system (liver, intestine), Respiratory system, Immune system, Cardiovascular system, Nervous system, Development and reproduction, Musculoskeletal system (bone, muscle), Eyes and Skin) and some of the models used to predict them. See Table 1 for a list of organ/tissue type availability of normal or healty human biological samples from various sources for research purposes.

  • Scientific competency questions as the basis for semantically enriched open pharmacological space development. Azzaoui K. et al. Drug Discov Today. (5744)
  • KEYWORDS: DATA MINING – DRUG DISCOVERY – PHARMACOLOGY – PROJECT – TEXT MINING

    The Open PHACTS partners were asked to define ‘business’ questions that are of interest for specific research activities and drug discovery in general. The top 20 of the 83 questions collected should be considered indicator of the actual information needs of researchers (pharma industry, academia and biotech). Find the 20 questions in Table 1, and also informative is the supplementary information table that provides a list of 31 data sources (all open and free public access), their data contents and concepts that are of interest for an open pharmacological space generation.

  • A comparative survey of chemistry-driven in silico methods to identify hazardous substances under REACH. Nendzaa M. et al. Regul Toxicol Pharmacol. (5743)
  • KEYWORDS: QSAR MODELING – REGULATORY GUIDELINES – RISK ASSESSMENT – SOFTWARE – STRUCTURE-BASED PREDICTION

    In the framework of REACH. a comparative evaluation of a series of in silicomethods, their applicability domains and limitations, which are available to address prediction of persistence, bioaccumulation potential, acute and long-term aquatic toxicity, PBT/vPvB properties ((very) persistent, (very) bioaccumulative, toxic), CMR (carcinogenicity, mutagenicity, reproductive toxicity), endocrine disruption and skin sensitisation (see Table 1 for an inventory of recommended methods and tools to identify substances these concerns; which can be accessed via ChemProp and OSIRIS Web tools).

  • Safety Pharmacology Investigations in Toxicology Studies: An Industry Survey. Authier S. et al. J Pharmacol Toxicol Methods. (5740)
  • KEYWORDS: DRUG SAFETY – PHARMACOLOGY – REGULATORY GUIDELINES – RISK ASSESSMENT

    Results of a survey addressed to 361 participants (9.1% Asia, 19.4% Europe and 71.4% North America; mostly toxicologists, safety pharmacologists and scientists involved in both disciplines) which was conducted by the Safety Pharmacology Society. Questions cover the following issues: incorporation of Safety Pharmacology (SP) into Toxicology studies (see Figure 1), statistics of SP endpoints measured in Toxicology studies (see Table 1), measure SP endpoints on the appropriate day (see Table 2), regulatory authority feedbacks (see table 3 and 4), the advantages/disadvantages and primary motivating factor of adding SP endpoints into regulatory Toxicology studies ( see tables 5, 6 and 7), and methods used in different non-clinical animal species in which SP endpoints were added into regulatory Toxicology studies for a New Compound Entity (see Figure 4) and for a biological agent (see Figure 5).

  • Indexing molecules for their hERG liability. Ryan A. et al. Eur J Med Chem. (5739)
  • KEYWORDS: CARDIOTOXICITY – DRUG SAFETY – hERG – MOLECULAR DESCRIPTORS – STRUCTURE-BASED PREDICTION – RISK ASSESSMENT

    This article presents the application of the Iterative Stochastic Elimination algorithm as key tool to construct a list of rule-based models (filters) for developing the concept of hERG Toxicity Index based on the exploitation of these filters combinations. This proposal results show good performance of the model for a external test set of more than 1300 hERG blockers extracted fro ChEMBL. See Table 3 for values of predicted hERG liability index of 62 hERG blocking drugs.

  • 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).

  • Emerging Transporters of Clinical Importance: An Update from the International Transporter Consortium. Hillgren KM. et al. Clin Pharmacol Ther. (5734)
  • The International Transporter Consortium proposes expansion of transporters list for evaluation during drug discovery development with the following ones: MATE1, MATE2K, MPR2, MPR3, MPR4, BSEP, nENT1 and PEPT1.This review reports general description, structure and function, genetic variants, and clinical significance of each of these of new recommended.

  • Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction. Mora A. et al. BMC Bioinformatics. (5732)
  • KEYWORDS: NETWORKS – SYSTEMS BIOLOGY

    Discussion on how the protein interaction data integration and post cleaning improves and facilitates the inclusion of network properties as predictive features for identification of drug target, and can help to identify a drug that is more likely to be withdrawn due to its high-centrally degree in a certain network. The full R code to reproduce all the analysis presented in this work is available in the Support Material section.

  • Hybrid in silico models for drug-induced liver injury using chemical descriptors and in vitro cell-imaging information. Zhu XW. et al. J Appl Toxicol. (5731)
  • KEYWORDS: DILI – HEPATOTOXICITY – MOLECULAR DESCRIPTORS – STRUCTURE-BASED PREDICTION

    This article presents new models built based on chemical structure (described by 120 CDK, 101 MOE and 664 Dragon descriptors) and in vitro assay data (hepatocyte imaging assay, HIAT) of a set of 156 DILI positive and 136 DILI negative compounds (see Table S1 of Supplementary material for data information, and tables 1 and 2 for models performance metrics). The discussion of the results provides insights of structural alerts for hepatotoxicity, mechamism interpretation based on HIAT in vitro indicators and the complexitiy of hepatotoxicity understanding and prediction/assessment.

  • QSAR with experimental and predictive distributions: an information theoretic approach for assessing model quality. Wood DJ. et al. J Comput Aided Mol Des. (5730)
    Involved Partner: NVS, AZ, LDB

  • KEYWORDS: PHARMACOKINETICS – QSAR MODELING – RISK ASSESSMENT

    Based on 3 datasets published by AstraZeneca (Log D [38ref], Human Plasma Protein Binding [ref40], and Caco2 A to B permeability [ref39) the evaluation of results supports the treating of QSAR predictions as probability distributions where the respective estimation of error become an integral part of the model generation.

  • Can ‘humanized’ mice improve drug development in the 21st century?. Peltz G. Trends Pharmacol Sci. (5729)
  • KEYWORDS: HEPATOTOXICITY – METABOLISM – PHARMACOKINETICS – RISK ASSESSMENT – SYSTEMS BIOLOGY

    Briefiengs on the toxicology studies with use of chimeric mice to improve the quality of preclinical drug assessment. Lastly, evidences that hepatic clearance and pharmacokinetic properties of selected drugs tested with chimeric mice point out the similarities with humans response and reinforce their consideration in metabolism evaluation of potential drug candidates.

  • Systematic identification of proteins that elicit drug side effects Kuhn M. et al. Mol Syst Biol. (5728)
  • KEYWORDS: PHARMACOLOGY – RISK ASSESSMENT – SYSTEMS BIOLOGY

    A large-scale analysis to identify protein-side effects pairs combining known drug-target and drug-side effect relations which can help on the assessment to predict protein-phenotype relationships. The study of 1428 site effects and related information available (based on STITCH database data, which integrates data from DrugBank, GLIDA, Matador, PDSP Ki Database, BindingDB, ChEMBL databases and a list of articles) retrieved that 732 of these site effects are caused predominantly by means of single proteins, although their perturbation (activation or inhibition) causes a complex site effect.

  • Characterization of drug-induced transcriptional modules: towards drug repositioning and functional Understanding Iskar M. et al. Mol Syst Biol. (5727)
  • KEYWORDS: RISK ASSESSMENT – SYSTEMS BIOLOGY

    This article reports a workflow to identify and characterize drug-induced transcriptional modules across 4 microarray data sets from 3 human cancer cell lines and rat liver, taking into account the conservation of some modules this strategy allow to propose new mechanism of action for marketed drugs and novle biological roles (see Figure 1).

  • Combining MEDLINE and publisher data to create parallel corpora for the automatic translation of biomedical text Jimeno A. et al. BMC Bioinformatics. (5726)
  • KEYWORDS: TEXT MINING

    Based on article titles obtained from MEDLINE and abstract text automatically retrieved from journals websites, this article presents a multi-lingual corpus (English/Spanish and English/French) useful in the biomedical domain.

  • Assessing the gain of biological data integration in gene networks inference. Vicente FF. et al. BMC Genomics. (5725)
  • KEYWORDS: NETWORKS – SYSTEMS BIOLOGY

    A recent example of how the integration of biological information and gene expression profile information can facilitate and improve the knowledge connections between diverse data in order to better understand the biology of a certain system and its components.

  • Detecting tissue-specific early warning signals for complex diseases based on dynamical network biomarkers: study of type 2 diabetes by cross-tissue analysis. Li M. et al. Brief Bioinform. (5724)
  • KEYWORDS: BIOMARKERS – NETWORK – SYSTEMS BIOLOGY

    A novel methodology called dynamic network biomarker (DNB) is presented and its application in the case of the type 2 diabetes disease based on the temporal-spatial gene expression in liver, adipose and muscle tissues. The DNB method is the driven network of the disease to analyse the variation of gene expression from normal state to disease state. Technical details are provided and also the list of GO biological processes (see Table 1) and of KEGG pathways (see Table 2) for each DNB.

April

  • Back to the Future: Safety Pharmacology Methods and Models in 2013. Pugsley MK. et al. J Pharmacol Toxicol Methods. (5723)
  • KEYWORDS: DRUG SAFETY – PHARMACOLOGY

    This editorial article of the Journal of Pharmacological and Toxicological Methods introduces the series of articles included in its Tenth Annual Focused Issue on Methods in Safety Pharmacology (full publication soon), which publishes themed issues arised during the meeting of the Safety Pharmacology Society, held in Phoenix, AZ in 2012. We highly recommend the review of this issue contents.

  • Analysis of Chemical and Biological Features Yields Mechanistic Insights into Drug Side Effects. Duran-Frigola M. et al. Chem Biol. (5722)
  • KEYWORDS: DATABASE – DRUG SAFETY – PHARMACOLOGY – SYSTEMS BIOLOGY

    A meta-data analysis of biological, pharmacological and clinical data available in online databases (SidER, DrugBank, PubChem, STITCH, KEGG, GO, ChEBI), in order to provide mechanistic insights for most of the side effects currently know for the marketed drugs. Figure 3 points out the most commonly associated features that their analysis highlights in terms of therapeutic targets, protein interactions, pathways, biological processes, molecular functions, small fragments, scaffolds and structural terms (see Table 1 for summary of results regarding these categories). See supplementary material for integrated data used in this analysis.

  • Carcinogenicity Prediction of Noncongeneric Chemicals by a Support Vector Machine. Zhong M. et al. Chem Res Toxicol. (5721)
  • KEYWORDS: DATABASE – CARCINOGENITICY – MOLECULAR DESCRIPTORS – RISK ASSESSMENT

    On the basis of 852 chemicals rat data extracted from the CPDBAS database, and 24 molecular descriptors covering a range of physicochemical properties, including electrophilicity, geometry, molecular weight, size, and solubility; a support vector machine-based classification model is presented.

  • Artificial neural network analysis of data from multiple in vitro assays for prediction of skin sensitization potency of chemicals. Hirota M. et al. Toxicol In Vitro. (5720)
  • KEYWORDS: ASSAY DATA – SKIN SENSITIZATION

    Based on a selected set of 64 test chemicals data (see Table 1 and Support Material for CAS number and experimental data), this article presents a new model (iSENS ver 1.) to predict drug-induced skin sensitization combining data for 2 basic tests data (human Cell Line Activation (h-CLAT) and SH (which mesures changes of cell-surface thiols).

  • 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.

  • A dataset on 145 chemicals tested in alternative assays for skin sensitization undergoing prevalidation. Natsch A. et al. J Appl Toxicol. (5716)
  • KEYWORDS: ASSAY DATA – DATABASE – SKIN SENSITIZATION

    This article presents a benchmarking dataset of 145 compounds (see Table 1 and Support Material for structures, smiles, name, MW, logKow, CAS number, and experimental data) to be use as reference for skin sensitization prediction.

  • Qualified kidney biomarkers and their potential significance in drug safety evaluation and prediction. Xie HG. et al. Pharmacol Ther. (5715)
  • KEYWORDS: BIOMARKERS – DRUG SAFETY – NEPHROTOXICITY

    Review on the eight qualified renal injury safety biomarkers (Kidnyey injury molecule-1 (KIM-1), Cystatin C in serum and urine (CysC), Beta-2 microglobulin (B2M), Clusterin (CLU), Trefoil factor 3 (TF3), Albuminuria and urinary albumin (ALB or uALB), Proteinuria and urinary total proteins (TP or uTP) and Renal papillary antigen-1 (RPA-1); see Figure 1 for their location).

  • High-throughput screening of drug-binding dynamics to HERG improves early drug safety assessment. Di Veroli GY. et al. Am J Physiol Heart Circ Physiol. (5714)
    Involved Partner: AZ
  • KEYWORDS: DRUG SAFETY – hERG – LQT – PHARMACOLOGY – RISK ASSESSMENT

    This article shows how neglecting drug-binding kinetics at the level of cardiac ion channels can lead to substantial underestimation of in silico cardiac risk assessments at the ventricular action potential level.

  • 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.

  • In silico modeling to predict drug-induced phospholipidosis. Choi SS. et al. Toxicol Appl Pharmacol. (5712)
  • KEYWORDS: ASSAY DATA – MOLECULAR DESCRIPTORS – PHOSPHOLIPIDOSIS

    Based on the most recent dataset reported by the FDA on the positive and negative induced-phospholipidosis drugs (743 compounds, see Reference, where 385 positive and 358 negative), after dataset cleaning (147 positives, 232 negatives) 2 models were built following 2 different approaches: i) DIPL.Model1 – traning set equal to the full the reduced dataset, external set unavailable and ii) DIPL.Model2 – training set defined as a balanced subset extracted from the full reduced dataset (98 positives, 100 negatives), external set equal to the rest of the full reduced dataset; see Figure 1 for total illustration of sets managed in this exercise, and Table 2 for information of compounds selected for DIPL.Model2. Details of models builiding are provided, and also the list of Molecular descriptors applied (see Table 6).

  • Translational pharmacokinetic-pharmacodynamic modeling of QTc effects in dog and human. Parkinson J. et al. J Pharm Toxic Meth. (5710)
    Involved Partner: AZ
  • KEYWORDS: ASSAY DATA – CARDIOTOXICITY – PHARMACOKINETICS

    >Evaluation of clinical data of 2 proprietary AZ compounds and 2 referencial drugs (moxifloxacin and dofetilide) is reported in order to discuss the potential of translation of QTc effects findings in dogs to predict human clinical outcomes (see tables 1 and 2 for information of doses, study design and data sources for all compounds used in this analysis).

  • Target identification and mechanism of action in chemical biology and drug discovery. Schenone M. et al. Nat Chem Biol. (5707)
  • KEYWORDS:PATHWAYS – PHARMACOLOGY – SYSTEMS BIOLOGY

    With a review on the current approaches available to foster target identification (direct methods, genetic interaction and genomic methods, and computational inference methods) and elucidation of mechanism of action of small molecules, authors present as more efficient the combination of several approaches (See Figure 6 for a conceptual workflow), and make remark that an analytical integration of multiple and complementary approaches will not be the final solution but in most of the aspects can be helpful.

  • Comparison of in silico models for prediction of mutagenicity. Bakhtyari NG. et al. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev. (5706)
  • KEYWORDS: MOLECULAR DESCRIPTORS – MUTAGENICITY – PROJECT – QSAR MODELING – SOFTWARE

    Based on a set of 6612 compounds (SMILES and Ames test data extracted from Hansen K. et al 2009, authors present the results comparison for a series of models addressing mutagenicity prediction buillt using 8 different expert systems (ACD/ToxSuite, ADMET predictor, Derek for Windows, T.E.S.T, TOPKAT, ToxTree, CAESAR and SARpy (last 2 available through VEGA platform)).
    This article reports for each model developed the following information: toxicity data source, description of the model, interpretation of the output and its applicability domain assessment; and a detailed comparison of how this individual models perform with the global dataset defined (see Table 2), and discuss as well on the their performance in terms of the chemicals in the prediction dataset, and the use of applicability domain information. In additions, authors provide a summary of some publications that compare predicitive models for Ames mutagenicity assessment (see Table 3).

  • Scientific challenges and implementation barriers to translation of pharmacogenomics in clinical practice. Lam YW. ISRN Pharmacol. (5705)
  • KEYWORDS: BIOMARKERS

    This review of current challenges emerging from the consideration of pharmacogenomics data in the Clinical practise nowadays, highlights the fact of using pharmacogenomic biomarkers as an appropriate, cost-effective and beneficial for clinical outcome in patients. Discussion on regulatory approval to include pharmacogenomic data in the diagnostic tests and how it can be stored and exploited in the Healthcare System (see Table 2 for a list of practical issues involved in the clinical implementation of pharmacogenomi testing).

  • Pharmaceutical toxicology: Designing studies to reduce animal use, while maximizing human translation. Chapman K. et al. Regul Toxicol Pharmacol. (5704)
  • KEYWORDS: DRUG DISCOVERY – PHARMACOLOGY – REGULARORY GUIDELINES – RISK ASSESSMENT

    Insights on aspects such 1) uptake of in vitro methods to predict human toxicity (see section 2, and Table 2 for a list of validated and accepted methods), 2) incorporation of the latest advances in safety pharmacology assessments (see section 3), 3) optimization of rodent study design in biological development and approaches of developmental and reproductive toxicology (see sections 4 and 5), are provided to investigate current and promising options for improving the efficiency of studies and prediction power.

  • 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).

  • Compound promiscuity – what can we learn from current data?. Bajorath J. et al. Drug Discov Today. (5702)
  • KEYWORDS: PHARMACOLOGY – RISK ASSESSMENT

    Discussion on results from different studies reported to establish compounds promiscuity (focused on compound-target relationships, drug-target relationships, high-dimensional data, molecular scaffolds, activity measurements dependence, structure-promiscuity relationships, etc), based on data from chemical annotated libraries such ChEMBL, PubChem and DrugBank, or from small datasets published in the literature. At some stage of the drug discovery process we need to rationalize the compounds promiscuity at the molecular level for a better risk assesmment of this aspect.

  • Using Pareto points for model identification in predictive toxicology. Palczewska A. et al. J Cheminform. (5701)
  • KEYWORDS: QSAR MODELING – RISK ASSESSMENT – SOFTWARE

    A proposal of 2 automated methods (Average and Centroid Pareto Model Identification, APMI and CPMI respectively) to mine models in disperse repositories in order to identify the suitable model to address a certain endpoint, based on the Pareto neighborhood (structural similarity of chemicals and models performances) of the query chemical compound.

  • Pharmacogenomics of phase II metabolizing enzymes and drug transporters: clinical implications. Yiannakopoulou ECh. et al. Pharmacogenomics J. (5700)
  • KEYWORDS: DRUG DISCOVERY – ENZYMES – PHARMACOLOGY – TRANSPORTER

    A brief review of the clinical implication in pharmocogenomics of Phase II metabolizing enzymes (see Table 1 for examples of affected drug activity) and ATP binding cassette (ABC) transporters (see Table 2 for affected drug activity). This article also reviews role of other types of transporters such SLC, OATP, OAT and OCT families.

March

  • Pharmacokinetics and the drug-target residence time concept. Dahl G. et al. Drug Discov Today. (5699)
  • KEYWORDS: DRUG DISCOVERY – PHARMACOLOGY – RISK ASSESSMENT

    Definitions of On-target and Off-target concepts are provided, together with a list of tools that may facilitate the their mechanistic studies (see Table 1). For further discussions, a set of questions to be consider when building a weight of evidence for species specificity is gathered in Table 2.

  • Improving the reuse of computational models through version control. Waltemath D. et al. Bioinformatics. (5697)
  • KEYWORDS: SOFTWARE

    Review of current approaches to model version control (inside model representation formats or, inside model repositories) and discussion on the concepts for improving them.

  • Species differences in drug transporters and implications for translating preclinical findings to humans. Chu X. et al. Expert Opin Drug Metab Toxicol. (5694)
  • KEYWORDS: ADME – PHARMACOKINETICS – RISK ASSESSMENT – TRANSPORTERS

    This article provides information and examples of species differences in transpoters protein expression (see Table 1 for major species differences of drug transporters in mRNA and protein expression in various ADME-related tissues) and prediction of drug-drug interaction across species.

  • Renal transporters in drug development. Morissey KM. et al. Annu Rev Pharmacol Toxicol. (5693)
  • KEYWORDS: ADME – ASSAY DATA – METABOLISM – NEPHROTOXICITY – TRANSPORTERS

    Since renal drug transporters are important determinants of the total clearance of commonly prescribed drugs, this comprehensive review compiles current knowledge regarding the contribution of renal secretory transporters and the estimation of global renal clearance (see Table 1 for kinetic characteristics of substrates of transporters involved in renal elimination, Table 2 for inhibitors), as well as, which are the transporters involved in renal drug secretion (see Figure 2), briefings on the known clinical drug-drug interactions that these transporters mediate (see examples in Table 3), the FDA recommendations in the design of clinical drug-drug interaction studies (see Figure 4), and how renal clearance differs in special populations (based on chronic kidney disease, age, preganancy stage, and ethnicity, see Table 4 for comparison of mRNA and protein expression levels of renal transpoters in various special populations).

  • Omics and Drug Response. Meyer UA. Annu Rev Pharmacol Toxicol. (5692)
  • KEYWORDS: BIOMARKERS – RISK ASSESSMENT – SYSTEMS BIOLOGY

    A comprehensive review of all omics areas of knowledge addressed to assess personalized medicine (genomics (personal genomes, genome-wide associations studies of drug response), epigenomics, transcriptomics, proteomics, metabolomics, systems pharmacology, and the electronic medical records and biobanks. Biomarkers and personalized drug-response profiles, together wits clinical data, will help to cover the translational scenario that all these data provide for the prediction of drug response for personalized drug therapies definition.

  • One hundred years of drug regulation: where do we go from here?. Woosley RL. et al. Annu Rev Pharmacol Toxicol. (5691)
  • KEYWORDS: REGULATORY GUIDELINES

    Discussion about past, present and future of the regulatory world in terms of drug discovery and knowlege management to ensure drug safety.

  • 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.

  • QSAR Models of Clinical Pharmacokinetics: Clearance and Volume of Distribution. Gombar VK. et al. J Chem Inf Model. (5687)
  • KEYWORDS: ADME -ASSAY DATA – METABOLISM – MOLECULAR DESCRIPTORS – PHARMACOKINETICS – QSAR MODELING

    2 new QSAR models for prediction of systemic clearance and steady-state volume distribution are reported based on intravenous dosing in humans data (experimental values for the 569 and 525 compounds as training sets, respectively, were extracted from Obach et al. 2008) and the application of 19 descriptors (see Table 1).

  • Substrate selectivity of human intestinal UDP-glucuronosyltransferases (UGTs): in silico and in vitro insights. Tripathi SP. et al. Drug Metab Rev. (5686)
  • KEYWORDS: ENZYMES – METABOLISM – PHARMACOKINETICS

    A brief review on UGTs enzymes structure and classification, their expression profile, their substrate specificity, their modulation and regulation as well as the glucuronidation reaction. Authors report on current insights various intestinal UGT isoforms (1A1, 1A6, 1A8, 1A9,1A10, 2B7 and 2B15). A list of 41 drugs with annotation to which enzymes metabolizing them is provided in Table 2 together with pharmacokinetic parameters (Km and Vmax).

  • 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).

  • New perspectives in toxicological information management, and the role of ISSTOX databases on assessing chemical mutagenicity and carcinogenicity. Benigni R. et al. Mutagenesis. (5684)
  • KEYWORDS: DATABASE – CARCINOGENICITY – MUTAGENICITY – RISK ASSESSMENT

    Apart from a deep description of the ISSTOX cluster of toxicological databases supported by the Istituto Superiore di Sanitá, this article provides a compilation of databases on carcinogenicity and mutagenicity available in the public domain (see Table 1).

  • Biological network extraction from scientific literature: state of the art and challenges. Li C. et al. Brief Bioinform. (5683)
  • KEYWORDS: NETWORKS – PATHWAYS – TEXT MINING

    Overview of available resources based on text mining approaches to extract biological networks (signaling pathways, metabolic pathways), see Table 1 for a list of 17 selected systems that aim to discover relations/networks from literature.

  • Regulation of drug-induced liver injury by signal transduction pathways: critical role of mitochondria. Han D. et al. Trends Pharmacol Sci. (5682)
  • KEYWORDS: DILI – IMMUNOTOXICITY – MITOCHONDRIAL TOXICITY – PATHWAYS

    Brief overview of DILI known mechanistic aspects (predictable and idiosyncratic cases), and review of the different pathways that become perturbated for drugs that cause liver injury (adaptation signaling pathway (see Figure 4), mitochondrial adaptation to drugs (see Figure 5), sensitization of hepatocytes to the adaptive immune system and/or innate immune system (see Figure 6), activation of death signaling pathways by idiosyncratic toxicants).

  • 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).

  • pH-gradient PAMPA-based in vitro model assay for drug-induced phospholipidosis in early stage of drug discovery. Balogh GT. et al. Eur J Pharm Sci. (5680)
  • KEYWORDS: ASSAY DATA – PHOSPHOLIPIDOSIS

    This article reports a high-throughput in vitro permeability model (PAMPA) to be used at the early stage of drug discovery for the drug-induced phospholipidosis potential prediction. Find the experimental or estimated values to range a set of 63 compounds in Table 2, and the values of specific tissue lipid mixtures (lung, liver, kidney, heart and BBB) in the Support Material.

  • 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).

February

  • In silico categorization of in vivo intrinsic clearance using machine learning. Hsiao YW. et al. Mol Pharm. (5678)
  • KEYWORDS: ADME – ASSAY DATA – MOLECULAR DESCRIPTORS – QSAR MODELING – STRUCTURE-BASED PREDICTION

    This article reports classification models for in vivo hepatic CLint data containing values as low as 1 ml/min (see Support Material for smiles and clearance values), based on 93 descriptors describing molecular lipophilicity, hydrogen-bonding, topology, size/shape, polarity, and drug-ability calculated using the descriptor generation program Selma. The predictive results pointed out lipophilicity and charge/polarizability types related descriptors as the most relevant ones for clearance prediction.

  • Systems Pharmacology to Predict Drug Toxicity: Integration Across Levels of Biological Organization. Bai JP. et al. Annu Rev Pharmacol Toxicol. (5677)
  • KEYWORDS: NETWORKS – PHARMACOLOGY – SYSTEMS BIOLOGY

    A review on the current strategies (databases or knowledge base resources/initiatives (see Table 1)) to define the global toxicological network, which spans the hierarchy of biological organization, from gene to mRNA to protein to intracellullar organelle to cell to organ to organism. Authors highlight the distinct predictors of connectivity among networks (the drug chemical characteristics; the genetic, transcriptomic and proteomic signatures; the organelle-based and organ-based approaches; and the interorgan relationships) and how the clinial description of drug toxicity using Medical dictionary can help to build up the global network, and point out the ontologies use as integrating axis for a proper knowledge management.

  • Target organ toxicities in studies conducted to support first time in man dosing: an analysis across species and therapy areas. Horner S. et al. Regul Toxicol Pharmacol. (5674)
    Involved Partner: AZ
  • KEYWORDS: DRUG DISCOVERY

    Based on 77 candidate drugs data for rodent and non-rodent studies carried out by AstraZeneca (see Table 1 for a summary of study design features, and Table 2 for the typical tissue evaluation list for a 1 month study), this article presents the breakdown of target organs identified in 1 month studies for rodent and non-rodent (see Table 5 and Figure 1) and the breakdown of unique or common target organs identified in 1 month studies for rodent and non-rodent (see Table 6). The results confirm the value of using non-rodents as a second species in toxicity testing to support human safety.

  • Very large virtual compound spaces: construction, storage and utility in drug discovery. Peng Z. et al. Toxicology. (5673)
    Involved Partner: ROCHE
  • KEYWORDS: DATABASE – DRUG DISCOVERY

    Briefings on large virtual compound spaces reported during the last 10 years (see Table 1).

  • 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.

  • Discovery of novel biomarkers and phenotypes by semantic technologies. Trugenberger CA. et al. BMC Bioinformatics. (5670)
  • KEYWORDS: BIOMARKERS – SEMANTIC WEB – TEXT MINING

    Focused on Obesity and diabetes diseases information, a full strategy (see Figure 2) to identify novel biomarkers and phenotypes from different sources (PubMed abstracts, public clinical trial summaries and internal Merk research documents) by application of the InfoCodex semantic engine is presented. The InfoCodex linguistic database contains multi-lingual 3,5 million entries (words/phrases) characterized by its type, language, significance, and with a hash code for the accelerated recognition of collocated expressions. Find a list of 30 PubMed results with highest confidence levels in Table 3.

  • Molecular signatures of G-protein-coupled receptors. Venkatakrishnan AJ. et al. Nature. (5669)
  • KEYWORDS: DRUG DISCOVERY – GPCR – PHARMACOLOGY

    A comprehensive review of solved structures of GPCRs (see Figure 1, the Time-line of GPCRs structures); the different molecular signatures (extracellular region and ligand-binding pocket accessibility (see Figure 2 where the diversity in the secondary structure elements of GPRs is shown), conserved structural scaffold in the TM region, consensus scaffold of class A GPCR ligand-binding pocket, the ligand-binding pocket and consensus scaffold interface, functional and structural importance of intracellular regions, intrinsically disordered segments in intracellular regions, T3 as a structural and functional hub) and the molecular changes noted during receptor activation (For class A: existence of several conformational states, changes in the EC region, TM changes after agonist binding, TM-intracellular region changes, biased signalling; and structural features of class B, C and others).

  • 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).

  • Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review. Csermely P. et al. Pharmacol & Ther. (5663)
  • KEYWORDS: DRUG DISCOVERY – NETWORKS – SYSTEMS BIOLOGY

    A comprehensive assessment of the analytical tools of network topology and dynamics. The authors discuss about the state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets, and provide compilation of resources available grouped as follows:
    Table 1 – Network visualization tools
    Table 2 – Human disease-related networks and network datasets
    Table 3 – Network-based predictions of disease-related genes as biomarkers
    Table 4 – Comparison methods of molecular networks
    Table 5 – Chemical compound similarity networks
    Table 6 – Protein-protein interaction network resources
    Table 7 – Signaling network resources
    Table 8 – Metabolic network resources
    Table 9 – Drug-design related resources

  • Enrichment of True Positives from Structural Alerts Through the Use of Novel Atomic Fragment Based Descriptors. Long A. et al. Mol Inf. (5662)
    Involved Partner: LL
  • KEYWORDS: ADME – MOLECULAR DESCRIPTORS – STRUCTURE-BASED PREDICTION – RISK ASSESSMENT

    Presentation of 2 novel fragment based atomic descriptors (Atom2EndofBranch and Atom2EndofMolecule, see Figure 4 for definition) development and application, which help to reduce the number of unconfirmed positives portion of prediction following in addition a small set of biotransformation rules (see Table 1).

  • Construction and Consensus Performance of (Q)SAR Models for Predicting Phospholipidosis Using a Dataset of 743 Compounds. Orogo AM. et al. Mol Inf. (5661)
  • KEYWORDS: ASSAY DATA – PHOSPHOLIPIDOSIS – QSAR MODELING – SOFTWARE

    Based on a expanded dataset of phospholipidosis inducers (743 compounds (385 positive and 358 negative)), three commercial QSAR software platforms (MC4PC, Leadscope Predictive Data Miner and Derek for Windows) were used to build and/or test models. Comparison of results retrieved for each platform are reported as well as a list of nonproprietary phospholipidosis negative and positive compounds incorrectly predicted by the 3 methods (see Table 6).

  • The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Hastings J. et al. Nucl Acids Res. (5660)
    Involved Partner: EMBL
  • KEYWORDS: SOFTWARE – TEXT MINING

    Description of the current contents of ChEBI database (see Table 1), its web interface and options of data visualization, and the ontology development steps and results.

  • Integration of in silico and in vitro tools for scaffold optimization during drug discovery: Predicting P-glycoprotein efflux. Desai P. et al. Mol Pharm. (5658)
  • KEYWORDS: P-gP – QSAR MODELING – STRUCTURE-BASED PREDICTION

    See Figure 5 where the strategy for intergration of in silico, in vitro and in vivo tools to identify and resolve issues related to P-gP efflux is presented as an assessment approach to be considered at early stages of drug discovery.

  • 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.

  • An integrated pharmacokinetics ontology and corpus for text mining. Wu H-Y. et al. BMC Bioinformatics. (5656)
  • KEYWORDS: ADME – METABOLISM – PHARMACOKINETICS – TEXT MINING

    An ontology that covers all drug metabolism and transportiation enzymes is presented (owl format athttp://rweb.compbio.iupui.edu/corpus/ontology) to facilitate annotation to all aspects of in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. Application of this ontology retrieved a PK-corpus (4 classes: clinical PK studies (n = 56); clinical pharmacogenetic studies (n = 57); in vivo DDI studies (n = 218); and a novel hierarchical three level annotation schema (see Figure 2) resulted to tag key terms, drug interaction sentences and drug interaction pairs. See Table 1 for description and resources considered to construct different categories of the PK ontology. Find executive description, unit and references of in vitro PK parameters in Table 2 and of in vivo PK studies in Table 4.

  • Towards a Fuzzy Expert System on Toxicological Data Quality Assessment. Yang L. et al. Mol Inf. (5655)
    Involved Partner: LJMU
  • KEYWORDS: SOFTWARE

    Authors present an adaptation of the existing ToxRTool to develop a new expert system, named Toxicological Data Quality Evaluator, which displays for a more efficient assessment the list of criteria that can not be fully satisfied due to missing information available (see Table 1 for descriptions of the scoring system applied to assess data reliability, Table 2 for the list of criteria considered in ToxRtool for in vivo data, and Figure 5 that shows the lis of criteria adapted for the Toxicological Data Quality Evaluator).

  • A pharmacological organization of G protein–coupled receptors. Lin H. et al.Nat Methods. (5654)
  • KEYWORDS: GPCR – PHARMACOLOGY

    Based on ChEMBL database annotations of ligands to class A GPCRs, herein there is a new point of view to classify this protein subfamily (see Figure 1 for a comparison between dendograms representations based on sequence similarity in the binding site and ligand set similarity). Table 1 reports examples of predicted and confirmed ligand associations between GPCRs with low sequence identities. A further approach is applied to extend the ligand dendogram to non-GPCR proteins, and the results identified a set of 485 non-GPCRs highly related to particular GPCRs by ligand similarity, shown in Figure 3.

January

  • Pharmacogenomics of drug-metabolizing enzymes: a recent update on clinical implications and endogenous effects. Sim SC. et al. Pharmacogenomics J. (5652)
  • KEYWORDS: CYP450 – DRUG SAFERY – ENZYMES – METABOLISM

    Review on pharmacogenomics aspects fora list of drugs affected by drug-metabolizing enzymes (DME: Phase I (CYP450: 1A2, 2A6, 2B6, 2C9, 2C19,2D6, 3A4, 3A5, 4F2) and Phase II (UGT1A1, TMPT)) polymorphism (see Table 1). Find a compilation of genome-wide association studies with respect to a set of DME in Table 2.

  • Genomics of ADME gene expression: mapping expression quantitative trait loci relevant for absorption, distribution, metabolism and excretion of drugs in human liver. Schröder A. et al. Pharmacogenomics J . (5650)
  • KEYWORDS: ADME – CYP450 – METABOLISM – RISK ASSESSMENT – TRANSPORTERS

    In order to identify genetic loci associated with quantitative changes in gene expression, this Stuttgart study presents a genome-wide association analysis of 4300 000 SNPs and 48 000 expression phenotypes which determines in a cohort of 149 human surgical liver samples (from Caucasian donors), and sorts out a subset of 95 significant genotype–phenotype relationships, which can be classified as ADME or ADME-related may be of particular relevance for drug disposition and toxicity (see tables 1 and 2 for the full list). 74 of these genotype-phenotype relations were not identified in a previous Seattle study.

  • Using toxicological evidence from QSAR models in practice. Benfenati E. et al. ALTEX. (5649)
  • KEYWORDS: QSAR MODELING – SOFTWARE – STRUCTURE-BASED PREDICTION

    The first inter-laboratory and cross-institutional review exercise on the reliability of QSAR results in the evaluation of an animal model. As potential end users, 28 toxicologists were asked to evaluate the results retrieved by 3 different predictive platforms (EPISuite, T.E.S.T and VEGA) for 3 case studies where different levels of recognition and documentation were provided (high and complex reliability, and uncertainty, respectively). The comparison between data format and contents reported is discussed together with the preferences pointed out by the toxicologists group.

  • 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.

  • The Drug Discovery Today: Technologies journal presents a special issue on Metabolites: structure determination and prediction. The editorial briefly summarises the contents of this special issue. We have compiled herein a selection of articles:
    • Drug metabolism in silico – the knowledge-based expert system approach. Historical perspectives and current strategies. Long A. Drug Discov Today. (5642)
      Involved Partner: LL
    • KEYWORDS: ADME – METABOLISM – SOFTWARE

      Brief overview of historical perspectives and current strategies to exploit available data through expert systems that can help to understand the mechanisms related with drugs metabolism by terms of evaluation of the properties (molecular masses and physicochemical properties) and effects (toxicological, pharmacokinetic and pharmacodynamic) of the metabolites.

    • Software aided approaches to structure-based metabolite identification in drug discovery and development. Pähler A. et al. Drug Discov Today. (5638)
      Involved Partner: ROCHE
    • KEYWORDS: DRUG DISCOVERY – METABOLISM – STRUCTURE-BASED PREDICTION – SOFTWARE

      Briefings on the state-of-the-art methods for metabolism prediction. A review on stand-alone expert systems for the in silico prediction of potential metabolites from a parent drug’s structure (MetaSite (Molecular Discovery), METEOR (Lhasa), MetabolExpert (CompuDrug) and MetaDrug (GeneGo)) and a collection of examples based on available metabolism data in literature, databases such as MDL metabolite (MDL Information Systems), Metabolism (Panlab) and DRUGBANK (University of Alberta). New strategies that combine experimental data with predicted metabolite structures are highlighted as significant steps to facilitate unsupervised data evaluation for guiding medicinal chemistry efforts in the design of promising new medicines.

    • High-throughput, computer assisted, specific MetID. A revolution for drug discovery. Zamora I. et al. Drug Discov Today. (5636)
      Involved Partner: LMD
    • KEYWORDS: ADME – CYP450 – DRUG DISCOVERY – METABOLISM – SOFTWARE

      A validation exercise is reported to illustrate the advantages of a new approach (Met ID*), in extension to the MetaSite software application, that combines the computer assisted assignment of the structure of the metabolite and the time-course for the formation of metabolites, in order to overcome metabolic issues.
      *Met ID definition: Metabolite identification describes the process of detecting known and finding unknown (expected or unexpected) metabolites in biological samples. It typically involves at least partial mass spectrometric structure identification by assignment of chemical structures to characteristic fragment ions (extracted from this article).

  • Strategies for the generation, validation and application of in silico ADMET models in lead generation and optimization. Gleeson MP. et al. Expert Opin Drug Metab Toxicol. (5646)
  • KEYWORDS: DRUG DISCOVERY – IMMUNOGENITICTY – RISK ASSESSMENT

    This editorial article reviews few last cases of market drugs that evidence the need of investing in immunogenicity risk assessment as a routine part of the drug discovery pipeline.

  • Genetics of Heart Failure. Lopes LR. et al. Biochim Biophys Acta. (5645)
  • KEYWORDS: CARDIOTOXICITY – RISK ASSESSMENT – SYSTEMS BIOLOGY

    This article reports list of genes associated to different cardiovascular disorders (dilated cardiomyopathy (Table 1), hypertrophic cardiomyopathy (Table 2), left ventricular non-compaction (Table 3), arrhythmogenic ventricular cardiomyopathy (Table 4)) and the gene overlap between the different inherited cardiomyopathy phenotypes.

  • Estimation of carcinogenicity using molecular fragments tree. Wang Y. et al. J Chem Inf Model. (5644)
  • KEYWORDS: CARCINOGENICITY – RISK ASSESSMENT – STRUCTURE-BASED PREDICTION

    Report on building and pruning a molecular fragment tree to detect substructures as structural alerts related with carcinogenicity occurrence (see Support Material tables for SMILES and data considered for building a model of carcinogenic potential prediction, and Table 5 for details of the 77 structural alerts identified).

  • Big pharma screening collections: more of the same or unique libraries? The AstraZeneca–Bayer Pharma AG case. Kogej T. et al. Drug Discov Today. (5641)
    Involved Partners: AZ, BHC
  • KEYWORDS: DATABASE – DRUG DISCOVERY

    A comprehensive analysis of the comparison between 2 pharma screening collections (2.75M and 1.41M compounds from Bayer Pharma AG and AstraZeneca companies, respectively) and a third party database (CHEMBL). Companies avoid the intellectual property and competition issues (reverse engineering or structure disclosure) by using only the 2D binary molecular fingerprints; in order to keep the molecular structures of both parties confidential. An interesting result to be highlighted from this analysis is the fact that a 95% (144K) of the compounds similar (or with high similarity) between both pharma collections are in the public domain, they mainly can be found in publications, patents and external compound supplier catalogs. As expected the overlap between both pharma collections is small and their sharing challenge envisages advantages to cover different parts of the chemical space.

  • Aqueous solubility: Simple predictive methods (in silico, in vitro and bio-relevant approaches). Elder D. et al. Int J Pharm. (5640)
    Involved Partner: GSK
  • KEYWORDS: ADME – DRUG DISCOVERY – MOLECULAR DESCRIPTORS – SOFTWARE

    This review provides a summary of simple predictive methods used to assess aqueous solubility as well as an assessment of the more complex in silico methodologies and a review of the recent solubility challenge (see Table 1 for a list of 13 most commonly used comercial/freeware software packages for prediction of solubility).

  • Large, chemically diverse dataset of log P measurements for benchmarking studies. Martel S. et al. Eur J Pharm Sci. (5639)
  • KEYWORDS: ADME – ASSAY DATA – MOLECULAR DESCRIPTORS – STRUCTURE-BASED PREDICTION

    Based on lipophiliicty related data for 759 compounds (see Table S2 of the Support Material for SMILES, experimental logP values and pH of the analysis), authors present a data collection of 707 validated logP values as a benchmarking dataset for developing new approaches to predict octanol/water partition coefficients of chemical compounds.

  • Understanding Drugs and Diseases By Systems Biology?. Schneider H-C. et al. Bioorg Med Chem Lett. (5637)
  • KEYWORDS: NETWORKS – PATHWAYS – SYSTEMS BIOLOGY

    An overview of Systems Biology approaches: i) top-down (gene-set enrichment analysis and pathway mapping, classification of diseases and stratification of patients, causal reasoning, mapping of drug and disease signatures and network inference) and ii) bottom-up (molecular mechanistic models, logic-based models, or physiological models).

  • SWSNL: Semantic Web Search Using Natural Language. Havernal I. et al. Expert Syst Appl. (5634)
  • KEYWORDS: NLP – SOFTWARE – TEXT MINING

    An example of the Semantic web searching in Natural Language data processing. Learn from figures 1, 3 and 4 about system architecture and details on ontology application.