2012

2012

December

  • Automating knowledge discovery for toxicity prediction using jumping emerging pattern mining. Sherhod R. et al. J Chem Inf Model. (5633)
  • KEYWORDS: MOLECULAR DESCRIPTORS – MUTAGENICITY – STRUCTURE-BASED PREDICTION

    A new method, based on jumping emerging pattern mining within a set of compounds represented using atom pair descriptors, is presented as a tool to help automate alert identification by mining descriptions of activating features directly from toxicity data sets. Results are reported for different sets (Ames mutagenicity, oestrogenicity, and hERG channel inhibition; see Experimental Section for Data sets description).

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

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

  • Prediction of mutagenicity, carcinogenicity, developmental toxicity, and skin sensitisation with Caesar program for a set of conazoles. Bolčič-Tavčar M. et al. Arh Hig Rada Toksikol. (5631)
  • KEYWORDS: ASSAY DATA – CARCINOGENICITY – DEVELOPMENTAL TOXICITY – MUTAGENITICY – PROJECT – SKIN SENSITIZATION – STRUCTURE-BASED PREDICTION

    Models performed with the program package CAESAR are presented to predict mutagenicity, carcinogenicity, developmental toxicity and skin sensitization for a set of 27 compounds (see Table 1 for names, CAS number and CAESAR predictions compared with EC regulation).

  • Application of In Silico, In Vitro and Pre-Clinical Pharmacokinetic data for the Effective and Efficient Prediction of Human Pharmacokinetics. Grime K. et al. Mol Pharm. (5630)
  • KEYWORDS: ADME – DRUG DISCOVERY – PHARMACOKINETICS

    A comprehensive guidance for assessment of acceptable human pharmacokinetic profile for new drugs candidates. See Table 1, where authors propose a generic DMPK screening cascade.

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

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

  • Towards Virtual Knowledge Broker services for semantic integration of life science literature and data sources. Harrow I. et al. Drug Discov Today. (5628)
  • KEYWORDS: DRUG DISCOVERY – PROJECT – SEMANTIC WEB – TEXT MINING

    This article presents the public Semantic Enrichment of the Scientific Literature (SESL) demonstrator, which focuses on human genes related with Type 2 diabetes melitus, to illustrate how this initiative integrates data from the literature and other public resources based on semantic web standards.

  • Translational research: the changing landscape of drug discovery. Fishburn CS. Drug Discov Today. (5627)
  • KEYWORDS: DRUG DISCOVERY – PROJECT

    Briefings on the situation to afford translational research, who are the players (academic institutions, biotech companies, and large pharmaceutical corporations; together with national institutes of health) and which are the advantages and the challenges of their intrinsic collaboration.

  • Machine Learning Methods for Property Prediction in Chemoinformatics: Quo Vadis?. Varnek A. et al. J Chem Inf Model. (5626)
  • KEYWORDS: MOLECULAR DESCRIPTORS – QSAR MODELING – SOFTWARE

    A compilation of modern approaches to machine learning methods that could be useful for addressing chemoinformatics issues like: improvement of predictive performance of structure-property (activity) models, generation of structures possessing desirable properties, model applicability domain, modeling of properties with functional endpoints, and accounting for multiple molecular species. See Table 1 for a list of chemoinformatics tasks and the appropriate machine learning concepts and methods freely available software. This perspective article also discusses about data types (see Table 2 for different binary, integer and real data classification).

  • Reducing safety-related drug attrition: the use of in vitro pharmacological profiling. Bowes J. et al. Nat Rev Drug Discov. (5625)
    Involved Partner: AZ, GSK, NVS

    eTOX cited
  • KEYWORDS: RUG DISCOVERY – PHARMACOLOGY – RISK ASSESSMENT

    This perspective article provides a comprehensive review on different issues related with the use of in vitro pharmacological profiling to reduce the safety-related drug attrition. Firstly, they list the advantages of in vitro profiling (see also Box 1), then discusse about the ‘cross-pharma’ knowledge, and finally present a list of 44 targets resulting of the comparison between the 4 pharmaceutical companies co-authors of this article in their ordinary screenings (see Table 1 for the full list, 24 GPCRs, 6 Enzymes, 7 ion channels, 3 neurotransmitter transporters, 2 nuclear receptors and 1 kinase) to be considered as the minimal panel that should provide a broad early assessment of the potential hazard of new drug candidates.

  • Exposition and reactivity optimization to predict sites of metabolism in chemicals. Cruciani G. et al. Drug Discov Today. (5624)
  • KEYWORDS: CYP450 – METABOLISM – STRUCTURE-BASED PREDICTION

    This article presents recent developments included in a new release of MetaSite software, MetaSite4, to respond the medicinal chemists demand on ‘more quantitative’ predictions to improve design of modified structures with altered metabolism. For instance, MetaSite4 is more accurate in the prediction of the site of metabolism for CYP2D6 and CYP2C9 than for 3A4.

  • QSAR studies of macrocyclic diterpenes with P-glycoprotein inhibitory activity. Sousa IJ. et al. Eur J Pharm Sci. (5623)
  • KEYWORDS: ASSAY DATA – P-gP – QSAR MODELING

    This article reports 2 QSAR models based on data from a set of 51 bioactive diterpenic compounds which include lathyrane and jatrophane-type diterpenes (see Table 1 for experimental activities values at 2 different concentrations, and molecule structures in figures of the Supplementary data).

  • The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so. Kell DB. et al. Drug Discov Today. (5622)
  • KEYWORDS: PHARMACOLOGY – TRANSPORTERS

    A review on experiments that might usefully be done and that would provide evidende on the drugs and xenobiotic carriers in real biology membranes (eg.,evidence form enzyme kinetics, genetic evidence for specific drug carriers in yeast) is presented as a response to another paper on the evidende for the role of ransport proteins in affecting drug uptake into cells. See in Table 1 a compilation of web-accessible resources for assessing (potentially promiscuous) drug–target (including drug–transporter) interactions (‘drug’ here often meaning small molecule ligand rather than licensed drug)..

  • GlaxoSmithKline opens the door on clinical data sharing. Harrison C. Nat Rev Drug Discov. (5621)
  • KEYWORDS: ASSAY DATA – DRUG DISCOVERY – RISK ASSESSMENT

    GSK announced recently that next year they will share ‘patient-level’ raw data from clinical trials of approved drugs and failed investigational compounds.

November

  • Preclinical experimental models of drug metabolism and disposition in drug discovery and development. Zhang D. et al. Acta Pharmaceutica Sinica B. (5620)
  • KEYWORDS: ADME – DRUG DISCOVERY – METABOLISM

    Discussion on strategies in the applications of both in vitro and in vivo experimental models of drug metabolism and disposition, taking into account PK–PD and PK–TK considerations. Table 1 lists the model systems for particular ADME studies; Table 2 lists the role of pharmacokinetics studies in drug discovery and development at discovery, preclinical, clinical and late clinical stages; and Table 3 shows a list of selected Cyp-knockout and human CYP-transgenic mouse models together with a set of references.

  • Harmonization and semantic annotation of data dictionaries from the Pharmacogenomics Research Network: A case study. Zhu Q et al. J Biomed Inform. (5619)
  • KEYWORDS: PROJECT – TEXT MINING

    An example of initiatives related to understand how genome contributes to an individual’s response medication. The Pharmacogenomics Research Network presents their study on harmonization and semantic annotation of Pharmacogenomics data dictionaries collected from different partners of such consortium. The results of this study highlighted the significant amount of variability in this type of data, and confirm the need of its standardization for any purpose of exploitation to ensure quality data. See Table 1 for examples of heterogeneity in data dictionaries, Figure 1 to know the annotation pipeline used in this study and Table 7 for the categorization of results and examples of 797 variables from 4 different groups within the consortium.

  • The Functional Genomics Network in the evolution of biological text mining over the past decade. Blaschke C. et al. N Biotechnol. (5618)
    Involved Partner: CNIO
  • KEYWORDS: NETWORKS – SYSTEMS BIOLOGY – TEXT MINING

    Biological text mining has evolved in such a way that progressively has provided support to the data annotation in the field of functional genomics and genome annotation, which has helped to deal with massive data analysis. Authors review the beginnings of biomedical text mining application and the impact it has meant for disease research in general. Additionally, they list several limitations which still difficult the wide exploitation of text mining methods (lack of standardization and centralization of text mined data, the need of the democratization of text mining, and the evident need of making published biological information more accessible).

  • Improving R&D productivity of pharmaceutical companies through public–private partnership: experiences from the Innovative Medicines Initiative. Laverty H. et al. Expert Rev Pharmacoecon Outcomes Res. (5617)
    eTOX cited
  • KEYWORDS: DRUG DISCOVERY – PROJECT

    An article that describes the role and goals of the Innovative Medicine Initiative’s collaborative model and reviews the different topics addressed by their funded ongoing projects, and how they work to combine data, resources and expertise to generate knowledge output.

  • Challenges and recommendations for obtaining chemical structures of industry-provided repurposing candidates. Southan C. et al. Drug Discov Today. (5616)
  • KEYWORDS: DATABASES – DRUG DISCOVERY – PHARMACOLOGY – DATA MINING

    A comprehensive review of difficulties to retrieve chemical structures from literature, and diverse sources, both public and private, due to the inconsistency and non-standard labeling of compounds (names, numbers, etc.). The authors discuss different cases as examples and attempt to make their suggestions to simplify the code-name mapping and facilitate the name-to-structure, in order to provide consistent data for in silico repurposing efforts to find new treatments, for instance, for rare or neglected diseases.

  • Biological network inference for drug discovery. Lecca P. et al. Drug Discov Today. (5615)
  • KEYWORDS: DRUG DISCOVERY – NETWORKS – PHARMACOLOGY – SYSTEMS BIOLOGY

    A brief overview on recent computational technologies developed for network inference applied to the drug discovery field. These techniques can facilitate pursuing a drug target, analyzing the molecular mechanisms and the mechanisms of action underlying drug efficacy, predicting drug-target interactions and toxicity events (see Table 1). There are several computational methods applying different algorithmic approaches (eg., classifier-based algorithms, reverse-engineering approaches), see Table 2 for a list of references of different tools and strategies with respect their inputs and outputs.

  • Data integration of non-animal tests for the development of a test battery to predict the skin sensitizing potential and potency of chemicals. Vedani A. et al. Toxicol In Vitro. (5614)
  • KEYWORDS: ASSAY DATA – SKIN SENSITIZATION – SOFTWARE

    This article reports data for a set of 101 chemicals tested with LLNA, human cell line activation test (h-CLAT), direct peptide reactivity assay (DPRA) and in silico prediction system (DEREK) (see tables 1 and 2 for experimental data values).

  • Open PHACTS: Semantic interoperability for drug discovery. Williams AJ. et al. Drug Discov Today. (5613)
  • KEYWORDS: DATABASES – PHARMACOLOGY – PROJECT

    This is a referencial article to be aware of the goals and purposes of the IMI project Open PHACTS. Authors are members of different partners of the consortium, and they present and discuss issues like the role of the semantic web and the intrinsic value of data. Based on the current situation of pharmacological data spread across diverse resources, OPS platform is presented as a challenge to avoid complexities and be a centralized solution that will make efforts to ensure deliverable of quality data and annotation.

  • Text-mining solutions for biomedical research: enabling integrative biology. Rebholz-Schuhmann D. Nat Rev Genet. (5612)
  • KEYWORDS: TEXT MINING

    A comprehensive review on the evolution of the text-mining as solution for the retrievement of relevant information for the biomedical research field. Authors pointed out several tools (see Table 1) and list different aspects to be taken into account when one need to identify statements from scientific text: 1) identification of named entities, 2) entity disambiguation and 3) identification of relations between named entities. See Box 1 for a summary of text-mining processing steps, Box 2 for a brief overview of the evolution of the text-mining. The application of text mining methods helps to build knowledge bases from literature context to eventually derive knowledge discovery driving integrative research (see Box 3 which refers to the development and use of ontologies as key point to facilitate the integration of data/knowledge).

  • Pesticide induced immunotoxicity in humans: A comprehensive review of the existing evidence. Corsini E. et al. Toxicology. (5610)
  • KEYWORDS: ASSAY DATA – CARCINOGENICITY – IMMUNOTOXICITY

    A review on the existing evidence of pesticide-induced effects on immunological parameters in humans. Concretely, authors presents their results for a series of organophosphates, carbamates, organochlorinated pesticides, dithiocarbamate fungicides and other pesticides (see Tables 1-4 for experimental data). this article includes a brief discussion on the possible role of pesticide-induced immunosuppression in the occurrence of carcinogenicity events.

  • Expanding medicinal chemistry space. Barker A. et al. Drug Discov Today. (5608)
    Involved Partner: AZ
  • KEYWORDS: DRUG DISCOVERY – PHARMACOLOGY

    Discussion on the expected bioactive chemical space, limitation to access to the biologically relevant chemical space due to the diversitiy-oriented or biology-oriented synthesis, the role of the natural products in this relevant chemical space, and review on the advances in screening techniques (e.g., phenotypic screening, fragment-assisted lead generation).

  • Development of a high-throughput electrophysiological assay for the human ether-à-go-go related potassium channel hERG. Gillie DJ. et al. J Pharmacol Toxicol Methods. (5607)
    Involved Partner: GSK
  • KEYWORDS: ASSAY DATA – hERG – PHARMACOLOGY

    This article describes a high-throughput electrophysiology assay for hERG that runs on the Ion works Barracuda. They report the concentration-response curves for a set of 11 drugs (see Table 1), and compare their results with IC50 data from different sources (see Table 2).

  • Four disruptive strategies for removing drug discovery bottlenecks. Ekins S. et al. Drug Discov Today. (5604)
    Involved Partner: PFIZER
    eTOX cited
  • KEYWORDS: DRUG DISCOVERY – PROJECT

    Briefings on the main obstacles that the drug discovery pipeline still faces nowadays, discussion on ongoing initiatives of public-private collaborations, and a list of 4 strategies to disrupt across current performance in order to reimagine the drug discovery. Their proposals cover issues like: minimum data quality standards, strict timelines for data submission, open accessibility for data generated by public funded resources, encourage early stages collaborations between different stakeholders to reduce duplication of efforts and resources, making relevant clinical data available, build models and improve predictive tools, use of collaborative tools to share data, etc.

October

  • Exploring QSTR analysis of the toxicity of phenols and thiophenols using machine learning methods.Asadollahi-Baboli M. Environ Toxicol Pharmacol. (5603)
  • KEYWORDS: ASSAY DATA – PHARMACOLOGY – QSAR MODELING – STRUCTURE-BASED PREDICTION – RISK ASSESSMENT

    Based on experimental data of 51 substituted phenol and thiophenol, authors report the predicted pEC50>/sub> values using CART-SVR (Classification and Regression Tree – Support Vector Regression model) (see Table 1 for experimental and predictive values, Table 2 for classes and descriptors applied in this modeling exercise, Figure 1 for the flowchart of hybrid method of CART-LS-SVR used in the QSTR analysis, Figure 3 for the optimal decision tree for descriptor selection in modeling of toxicity of the phenols and thiophenols set, and Table 4 for a comparison of statistical parameters of the models presented in this article with previously reported models). Authors report in Table 5 the predicted pEC50>/sub> values for a new set of 18 phenols and thiophenols derivates from the training set.

  • Testing strategies for embryo-fetal toxicity of human pharmaceuticals. Animal models vs. in vitro approaches: a workshop report. van der Laan JW. et al. Regul Toxicol Pharmacol. (5602)
  • KEYWORDS: DATA MINING – REPRODUCTIVE AND DEVELOPMENTAL TOXICITY – RISK ASSESSMENT

    A comprehensive review of the discussions and statements on the state-of-the-art of various in vitro models for prediction of developmental toxicity is reported as outcome of a workshop held in Leiden in October 2011, where the following questions were debated (see also section 2. Objectives of the workshop):
    ·What details should be known from the rat/rabbit EFD strudies to come to decisions?
    ·Should we use a large number of compounds to ‘‘validate’’/evaluate the new methods, or should we focus on decisive strategies with a small number of carefully chosen compounds?
    ·Which in vivo endpoints should be covered anyway in in vitro systems?
    ·What are the performance criteria that would make people accept a test or battery?
    ·How would you resolve discordance between the in vitroassay and the in vivo assay?
    ·How should we move into human cells? What do we do when the animal cells give us a signal and the human cells are clean?
    ·What should be the series of steps that get us from the ‘‘Here and now’’ to the place where in vitro approache(s) are included in the testing strategy for EFD for human pharmaceuticals?
    DECIDART, a stepwise paradigm is presented (see Figure 1), to illustrate how the in vitro screen (EST battery) together with the pharmacodynamic properties of a compound can help to determine the drug candidate’s liability for reproductive toxicity.

  • Renal Organic Anion Transporters (SLC22 Family): Expression, Regulation, Roles in Toxicity, and Impact on Injury and Disease. Wang L. et al. AAPS. (5601)
  • KEYWORDS: ASSAY DATA – NEPHROTOXICITY – TRANSPORTERS

    This review article provides an overview of the transporter activity of organic anion transporters (OATs, SLC22) in terms of their expression and function in the kidney observed (see Table I for the full list of OATs members family for human and rodent, and details of their transport activity, mRNA expression detected in kidney, and their corresponding protein expression confirmed in the kidney as well, Table II for a summary of OAT regulation, Table III for influence of renal injury and disease on OAT protein localization and expression level, and Table IV for a list of substrates and inhibitors with kinetic values for different cells system as an update of a 2010 publication (link:VanWert AL, Gionfriddo MR, Sweet DH. Organic anion transporters: discovery, pharmacology, regulation and roles in pathophysiology.

  • An optimized gene set for transcriptomics based neurodevelopmental toxicity prediction in the neural embryonic stem cell test. Pennings JL. et al. Toxicology. (5599)
  • KEYWORDS: ASSAY DATA – NEUROTOXICITY – REPRODUCTIVE AND DEVELOPMENTAL TOXICITY – RISK ASSESSMENT

    This article reports transcriptomic data for a set of 10 compounds in 19 different types of exposure (see Table 1) as a result of combination of 2 previous studies addressed to identify an optimized gene set for neurodevelopmental toxicity prediction in ESTn cell test model. The results of their analysis highlight a set of 29 genes for such target prediction.

  • Latest advances in computational genotoxicity prediction. Naven RT. et al. Expert Opin Drug Metab Toxicol. (5598)
    Involved Partner: PFIZER
  • KEYWORDS: GENOTOXICITY – MUTAGENICITY – REGULATORY GUIDELINES – SOFTWARE – STRUCTURE-BASED PREDICTION

    Some insights in computational genotoxicity prediction are discussed in terms of regulatory application of in silico genotoxicity predictions, chemical similarity (the more dependant a toxicological endpoint is on structurally distinct toxicophers, the less-applicable the concept of similarity becomes) and applicability domain

  • Safety immunopharmacology: Evaluation of the adverse potential of pharmaceuticals on the immune system. Descotes J. J Pharmacol Toxicol Methods. (5597)
  • KEYWORDS: DRUG SAFETY – IMMUNOTOXICITY – PHARMACOLOGY – RISK ASSESSMENT

    Authors overview different aspects (immune-mediated adverse effects of pharmaceuticals, immunosupression, immunostimulation, hypersensitivity and auto-immunity) to be taken into account in the field of the safety immunopharmacology as a starting point and draw perspectives for future development in line of drug safety assessment, and report also a list of selected assays and models to be considered (see Talbe 1).

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

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

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

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

  • Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?. Martin TM. et al. J Chem Inf Model. (5594)
  • KEYWORDS: MOLECULAR DESCRIPTORS – QSAR MODELING – STRUCTURE-BASED PREDICTION

    Discussion to determine whether rational division methods lead to more predictive models compared to traditional random division when training and test sets need to be defined to perform QSAR modeling.

  • In Silico Prediction of Chemical Ames Mutagenicity.t Xu C. et al. J Chem Inf Model. (5593)
  • KEYWORDS: ASSAY DATA – GENOTOXICITY – MUTAGENICITY – RISK ASSESSMENT – STRUCTURE-BASED PREDICTION

    Thia article reports the 5 models built (Support Vector Machine (SVM), C4.5 Decision Tree (C4.5 DT), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and Naïve Bayes (NB), along with five fingerprints, namely CDK fingerprint (FP), Estate fingerprint (Estate), MACCS keys (MACCS), PubChem fingerprint (PubChem), and Substructure fingerprint (SubFP)) based on a set of 7617 diverse compounds (4252 mutagens and 3365 non-mutagens) collected from 4 papers. The PubChem-kNN, MACCS-kNN and PubChem-SVM models show high and reliable predictive accuracy for mutagenesis prediction when a external validation set extracted from the website of Lazar toxicity was considered. A total of 25 substructures are identified as responsible for the mutagenesis (see Table S3 of Support Material), and concretely, 6 fragments are highlighted as structural alerts (see Table 5).

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

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

  • SOT/EUROTOX Debate: Biomarkers From Blood and Urine Will Replace Traditional Histopathological Evaluation to Determine Adverse Responses. Boekelheide K. et al. Toxicol Sci. (5588)
  • KEYWORDS: BIOMARKERS – DRUG SAFETY – RISK ASSESSMENT

    Summary of outcomes from the SOT/EUROTOX 2011 debate regarding the new trends on biomarkers usability respect to traditional histopathological analysis. Advantages of their use justify this shift of techniques application, histopathological analysis lacks the quantitative objectivity inherent in biomarkers, while biomarkers are measured with the constanly evolving technologies. As an example of biomarkers utilization and exploitation, authors report known biomarkers in the acute kidney injury case and emphasize the current initiatives to reach consensus in use and applicability of such biomarkers, like the IMI SAFE-T project (see Figure 1 for an overview of the strategy for qualification of translational safety biomarkers).

  • Self-organizing molecular field analysis of NSAIDs: assessment of pharmacokinetic and physicochemical properties using 3D-QSPkR approach. Honey et al. Eur J Med Chem. (5587)
  • KEYWORDS: ADME – ASSAY DATA – DRUG SAFETY – PHARMACOKINETICS – QSAR MODELING – RISK ASSESSMENT – STRUCTURE-BASED

    Pharmacokinetics data for a set of 22 NSAIDs (see Table 1 for Volume of distribution and pKa values).

  • The determination and interpretation of the therapeutic index in drug development. Muller PY. et al. Nat Rev Drug Discov. (5586)
  • KEYWORDS: DRUG SAFETY – PHARMACOLOGY

    This perspective article presents briefings on the concept and use of the therapeutic index (TI) in drug development, which should represent various types of safety and efficacy data generated as in vitro and in vivo (animals and humans). Related topics like common used exposure parameters (see Box 1),the limitations of exposure data, how to consider plasma protein binding and tissue exposure (see Box 2), off-target safety margins, on-target vs off-target pharmacology or toxicity (see Box 3), the role of TI in decision-making, the determination and intepretation of the TI (see Box 6), and the general limitations of the TI. Box 5 provides an example of a translational TI heatmap grid (see Table S1 of Support Material) that illustrate how different Safety endpoints (COX1 binding, COX1 whole blood assay, etc…) are evaluated as efficacy endpoints in in vitrro, animal and human pharmacodynamics parameters..

September

  • Qualified kidney biomarkers and their potential significance in drug safety evaluation and prediction. Xie HG. et al. Pharmacol & Ther. (5585)
  • KEYWORDS: BIOMARKERS – DRUG SAFETY – NEPHROTOXICITY

    Briefings on a set of eight qualified biomarkers to detect and predict drug-induced nephrotoxicity (beta2-Microglobulin (B2M), Clusterin (CLU), Cystatin C (CysC (urinary)), Kidney injury molecule-1 (KIM-1), Trefoil factor 3 (TFF3), urinary Albumin (uALB), Renal papillary antigen-1 (RPA-1) and urinary Total Proteins (uTP); see Figure 1 to know their localization (proximal tubules, glomerulus, distal tubules, and collecting duct)).

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

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

  • A tool to facilitate clinical biomarker studies – a tissue dictionary based on the Human Protein Atlas. Kampf C. et al. BCM Med. (5583)
  • KEYWORDS: BIOMARKERS – SOFTWARE – TEXT MINING

    In order to aid clinicians and scientists in understanding the use of tissue histology, pathology (focused on cancer pathologies) and cell biology in diagnostics and biomarker studies, this article reports the generation of a dictionary based on microscopy images of normal and cancer tissues, and images of cells (immunofluorescence and confocal microscopy images) created as an amendment to the Human Protein Atlas.

  • A chemistry wiki to facilitate and enhance compound design in drug discovery. Robb GR. et al. Drug Discov Today. (5582)
    Involved Partner: AZ
  • KEYWORDS: DRUG DISCOVERY – SOFTWARE

    A team of AZ presents their novel software tool, initially named Compound Design Database and evolved to a more robust successor named Design Tracker, based on the principles of wikis and social networks with hypothesis-driven design to facilitate collaborative working (medicinal, computational, synthetic and physical chemists with pharmacokinetics experts on each project), visual planning (mostly experts are not located together), and incorporation of predictive science to improve design capability. The Design Tracker is now standard for use on all drug discovery projects at AZ.

  • Relevance of non-guideline studies for risk assessment: The coverage model based on most frequent targets in repeated dose toxicity studies. Batke M. et al. Toxicol Lett. (5580)
  • KEYWORDS: RISK ASSESSMENT – TEXT MINING

    See Table 3 for a list of anatomical targets relevant for repeated dose toxicity studies.

  • Discovery of 3H-imidazo[4,5-c]quinolin-4(5H)-ones as potent and selective dipeptidyl peptidase IV (DPP-4) inhibitors. Ikuma Y. et al. Bioorg Med Chem. (5576)
  • KEYWORDS: ASSAY DATA – ENZYMES – hERG – PHARMACOLOGY – PHOSPHOLIPIDOSIS

    New series of dipeptidyl peptidase IV inhibitors that show reduced risk for hERG channel inhibition and induced phospholipidosis.

  • Identification of Drugs Inducing Phospholipidosis by Novel in vitro Data. Muelhlbacher M. et al. ChemMedChem. (5575)
  • KEYWORDS: DRUG DISCOVERY

    Based on a highthroughput cell-culture-based method to evaluate the phospholipidosis induction, a set of 297 drug-like compounds were tested at 2.5 and 5.0 microM (see Support material for names, smiles and activity for each concentration). The results surprisingly find as potential inducer a set of 28 compounds which were not previously reported to induce phospholipidosis (see the list in section ‘Identification of PLD-inducing agents’). Interestingly, authors checked the distribution of PLD-active and PLD-inactive compounds with respect to their ATC classifications (see Table 2).

  • In silico drug repositioning – what we need to know. Liu Z. et al. Drug Discov Today. (5574)
  • KEYWORDS: DRUG DISCOVERY

    Brief insights on the drug repositioning concerns from a in silico viewpoint. One can address repurposing with a purpose (drug-centric or disease-centric), with a strategy (target-driven, genome-wide or text-mining), or with confidence (i.e. the Drugs of New Indications database, with data from literature review, the drug repositioning Wiki and the Rare Disease Repurposing Database (RDRD)).

  • Databases fight funding cuts. Monya B. Nature. (5570)
  • KEYWORDS: DATABASE – PROJECT

    This Nature News reports the funding difficulties that could experience current public resources in the near future, with the example of the case of 5 US National Library of Medicine resources (Protégé, BioMagResBank, Repbase, REBASE and CASP databases).

August

  • Drug induced phospholipidosis: An acquired lysosomal storage disorder. Shayman JA. et al. Biochim Biophys Acta. (5568)
  • KEYWORDS: ENZYME – PHOSPHOLIPIDOSIS

    Argumentation of phospholipidase A2 role to further understand the drug induced phospholipidosis mechanism.

  • Phospholipogenic Pharmaceuticals Are Associated with a Higher Incidence of Histological Findings than Nonphospholipogenic Pharmaceuticals in Preclinical Toxicology Studies. Barone LR. et al. J Toxicol. (5567)
    Involved Partner: AZ
  • KEYWORDS: PHOSPHOLIPIDOSIS – RISK ASSESSMENT

    Based on experimental data regarding the phospholipogenic potential of a dataset of 46 phospholipogenic (37 bases, 4 neutral compounds, 3 zwitterionic and 1 acid) and 62 nonphospholipogenic (9 bases, 34 neutral compounds, 1 zwitterionic and 18 acids), authors evaluate the response in 9 organs (adrenal gland; bone marrow; kidney; liver; lung;
    lymph node; spleen; thymus; and reproductive organs), for 3 different species (dog, mouse, and rat) with study durations ranged from 5 days to 2 years, where the majority of repeat dose studies were conducted for 1 to 4 weeks duration.Table 2 shows the incidence of nonphospholipidosis histological findings for liver, kidney, lung, adrenals, and lymphoid tissues for 46 phospholipogenics and 62 nonphospholpogenics. Assay data is not provided.

  • Mining the pharmacogenomics literature–a survey of the state of the art. Hahn U. et al. Brief Bioinform. (5566)
  • KEYWORDS: DATA MINING – PHARMACOLOGY – TEXT MINING

    A comprehensive review on published exercises of automatically text mining tools/applications from the period 2008 to 2011 in the field of the pharmacogenomics literature. Identification of entities at genotype level (genes, genomic variations, proteins and protein mutations), at phenotype level (diseases, therapies, animal models), or at pharmacological level (drugs, treatment-related chemicals). A summary of remarks on text mining approaches is provided for future techniques, corpora or software development.

  • Establishing in vitro to clinical correlations in the evaluation of cardiovascular safety pharmacology. Chain ASY. et al. Drug Discov Today. (5565)
    Involved Partner: GSK
  • KEYWORDS: CARDIOTOXICITY – DRUG DISCOVERY – PHARMACOLOGY

    An example of Translational pharmacology strategies: from animal to man and back, focused on cardiovascular safety. Three technologies are applied in this study (see Table 1 for a summary): 1) in vitro experiments (hERG assays), 2) in vivo studies (anaesthetised animal models, and conscious animal models)and 3) establishing translational correlations (computational methods: pharmacokinetic-pharmacodynamic modelling) to finally assess the drugs safety.

  • Design of a testing strategy using non-animal based test methods: lessons learnt from the ACuteTox project. Kopp-Schenider A. et al. Toxicol In Vitro. (5564)
  • KEYWORDS: PROJECT

    Summary of lessons learnt by the ACuteTox project. The full article reading is recommended to be aware of a series of observations made from the statistical point of view of the development of testing strategies (ROC analysis, Classification and regression trees, random forests, …).

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

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

  • CORAL: QSPR model of water solubility based on local and global SMILES attributes. Toropov AA. et al. Chemosphere. (5562)
  • KEYWORDS: ASSAY DATA – PHARMACOKINETICS – QSAR MODELING – SOFTWARE – STRUCTURE-BASED PREDICTION

    Based on water solubility data of 1311 substances (see Support material for CAS number, SMILES and values of negative logarithm of water solubility) extracted from the web of Virtual Computational Chemistry Laboratory, authors present a QSPR model developed appling the CORAL software.

  • Metabolomics and its potential in drug development. Beyogly D. et al. Biochem Pharmacol. (5561)
  • KEYWORDS: ADME – DRUG DISCOVERY – METABOLISM – PHARMACOLOGY

    A brief overview of Metabolomics discipline definition and requirements to address a proper metabolomic study, and the rationale for biomarkers identification (for the regulatory authorities and the Pharma industry). There is an undoubtedly need to apply the metabolomics information during the preclinical drug development to anticipate both undesired cross-pharmacological and toxicity events.

  • Unraveling toxicological mechanisms and predicting toxicity classes with gene dysregulation networks. Pronk TE. et al. J Appl Toxicol. (5560)
  • KEYWORDS: BIOMARKERS – NETWORK – RISK ASSESSMENT – SKIN SENSITIZATION – TOXICOGENOMICS

    new example of how gene dysregulation network construction and its corresponding analysis can help to formulate hypotheses on functional relationships between genes and their role in different toxicity events. Based on data from a human skin sensitization study (with 8 sensitizing and 6 irritating compounds, extracted from the supplementary data of Vandebriel et al. 2010 article) authors present a network of 115 pairs with 106 genes.

  • Chemogenomics of allosteric binding sites in GPCRs. Gloriam DE. et al. Drug Discov Today. (5557)
  • KEYWORDS: PHARMACOLOGY

    Focused on GPCRs bioactivity, this article reviews recent techniques (target binding site-focused (structure-based approaches), chemogenomic ligand-based (privileged structure-based focused libraries), ligand-site pair-based (pahrmacophore models, machine-learning methods), and provides examples of application of these chemogenomic techniques to allosteric GPCR ligands in the case of GPRC6A and aminergic-like receptors. See Table 2 for a comparison summary of chemogenomic lead discovery techniques and allosteric applications presented in this article.

  • Transcriptional data: a new gateway to drug repositioning?. Iorio F. et al. Drug Discov Today. (5556)
    Involved Partner: EMBL
  • KEYWORDS: DRUG DISCOVERY – SYSTEMS BIOLOGY

    This articles describes methods, case studies and resources (ArrayExpress, GEO, cMap, DAVID, MsigDB, GeneSigDB, … see Table 1 for short description, accessibility and general features) to discuss current challenges and benefits of exploiting existing repositories of microarray data with the aim to facilitate the search space for systematic drug repositioning.

  • Graph mining: procedure, application to drug discovery and recent advances. Takigawa I. et al. Drug Discov Today. (5555)
  • KEYWORDS: DATABASE – DATA MINING – DRUG DISCOVERY

    A brief review on graph mining techniques (see section ‘Frequent subgraph mining’ and Table 2 with a list of sparse learning methods and extensions to graphs) that can support the drug discovery to integrate data from the huge number of compounds stored in a wide screen of databases (see Table 1 for a list of relevant chemical databases (12 public and 5 commercial)).

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

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

July

  • Membrane transporters in drug development. Keogh JP. Adv Pharmacol. (5553)
  • KEYWORDS: PHARMACOLOGY – TRANSPORTERS

    A comprehensive review on drug transporters (28 relevant transporters provided in Table 1): location, orientation, function, distribution (see Figure 1), expression, species differences, challenges and opportunities for drug development (see Table 2 that lists tools available for investigation of transporters interactions, their availability, utility and translatability to human outcomes), their clinical relevance, drivers of their Drug-drug interaction risks (passive membrane permeability, transporter-metabolism interplay, specific substrates and inhibitors, genetic polimorphisms, their membrane regulation).

  • Investigative safety science as a competitive advantage for Pharma. Moggs J. et al. Expert Opin Drug Metab Toxicol. (5550)
    Involved Partner: NVS
  • KEYWORDS: DRUG SAFETY – PHARMACOLOGY – PROJECT – RISK ASSESSMENT

    An integrated translational safety sciences approach for pharmaceutical R&D should facilitate the following steps, as a team from Novartis company presents: i) elucidation of mechanisms of toxicity; ii) discrimination between toxic and adaptive responses; iii) enhanced assessment of safety margins; iv) earlier termination of unsafe molecules; v) enhanced candidate selection; vi) enhanced assessment of human relevance for preclinical toxicities; vii) prediction of reversibility for a given toxicity; viii) prediction of toxicity risks following chronic treatment; ix) fine-tuning of dosing regimens to maximize risk/benefit; x) mitigation or counteraction of a toxic event; xi) monitoring of adverse effects in the clinic; xii) safety knowledge-driven design of follow-up drugs; and xiii) elucidating the species specificity of pharmacologic and toxicologic effects and their relevance for humans. Current efforts are exemplified by projects like MARCAR, PSTC, SAFE-T, Tox21 NIEHS/NTP/EPA or eTOX.

  • Toxicokinetics as a key to the integrated toxicity risk assessment based primarily on non-animal approaches. Coecke S et al. Toxicol In Vitro. (5549)
  • KEYWORDS: RISK ASSESSMENT

    This article summarizes the current status of alternative non-animal methods regarding toxicokinetics and reproduces some examples (see Table 1) extracted from this other article Alternative (non-animal) methods for cosmetics testing: current status and future prospects-2010. Some critical aspects are discussed as crucial requirements, see section 5 and conclusions section.

  • Drug Transport by Organic Anion Transporters (OATs). Burckhard G. Pharmacol & Ther. (5548)
  • KEYWORDS: PHARMACOLOGY – TRANSPORTERS

    Comprehensive review on general aspects of Organic Anion Transporters (OATs) substrate specificity and ability to exchange extracellular against intracellular organic anions. Differences between human and rodent transports are discussed and remarkable information like a list of single nucleotide polymorphisms leading to altered sequence of human OAT1,2,3,4 and UART1 transporters (see Table 3) and experimental data related with interaction of human OAT1,2 and 3 with 50 drugs (see table 5, 6 and 7), and the location of OATs in proximal tubule cells of human and rodents (see Figure 10) are provided.

  • A Text-Mining System for Extracting Metabolic Reactions from Full-Text Articles. Czarnecki J. et al. BMC Bioinformatics. (5546)
  • KEYWORDS: METABOLISM – TEXT-MINING

    This article presents a new approach to extract data related with metabolic reactions from the literature. Authors review existing text-mining resources and describes their reaction extraction algorithm (sentence selection phase, entity assignment phase and assignment scoring phase). To proceed in this task, authors discuss about different characteristics like multiple entity type and entity mismatch (enzymes and metabolites) and ternary (and n-ary) relationships (enzyme C catalyzes the conversion of substrate D to product E), that can difficult the extraction of metabolism related information. See Support material for lexic terminology used in this study.

  • My data are your data. Marx V. Nat Biotechnol. (5545)
  • KEYWORDS: DATA MINING

    A brief overview on the status and challenges of data sharing, pointing out the limitations to apply the semantic web approach on current data. New initiatives and efforts must be done at several stages of data generation and storage.

  • Application of PBPK modeling to predict human intestinal metabolism of CYP3A substrates – An evaluation and case study using GastroPlus™. Heikkinen AT. et al. Eur J Pharm Sci. (5543)
    Involved Partner: ROCHE
  • KEYWORDS: CYP450 – METABOLISM – PHARMACOKINETICS – STRUCTURE-BASED PREDICTION

    This study evaluates the accuracy and precision of commercial software (GastroPlus™) Fg (fraction of absorbed dose escaping first-pass metabolism in the gut wall) predictions for 20 CYP3A substrates using in vitro and in silico input data for metabolic clearance and membrane permeation, and illustrates a potential impact of intestinal metabolism modeling on decision making in a drug Research and Development project (see Table 2 for physicochemical properties data, Table 3 for in vitro metabolism, permeability and in vivo and predicted Fg).

June

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

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

  • Relating molecular properties and in vitro assay results to in vivo drug disposition and toxicity outcomes. Sutherland J. et al. J Med Chem. (5541)
  • KEYWORDS: ADME – MOLECULAR DESCRIPTORS – NETWORKS – PHARMACOKINETICS – STRUCTURE-BASED PREDICTION

    Based on data of 173 chemical series from a database of 3773 compounds with rodent pharmacokinetic and toxicology data (repeat-dose toxicology studies of 4-14 days duration as well as single-dose pharmacokinetic rodent studies), the authors present their work on examining the relationship between molecular properties and in vivo surrogate assays vs. in vivo properties (see Table 1 where the list of examined properties with their descriptions are provided for in vivo pharmacokinetics, physical properties, in vitro ADMET surrogate assays, in vitro pharmacology assays, and calculated molecular properties). Correlation between pairs are analysed, and their approach highlight as the most predictive ones: rat primary hepatocyte (RPH) cytolethality / volume of distribution (Vd) for in vivo toxicology outcomes, scaled microsome metabolism / calculated logP for in vivoo unbound clearance, and calculated logD / kinetic aqueous solubility for thermodynamic solubility.

  • Relative parameter sensitivity in prenatal toxicity studies with substances classified as developmental toxicants. Rorije E. et al. Reprod Toxicol. (5540)
  • KEYWORDS: ASSAY DATA – REPRODUCTIVE AND DEVELOPMENTAL TOXICITY – RISK ASSESSMENT

    This article review data for a selection of 22 publicly available developmental toxicity studies (see Table 1 for substance name, CAS number, EU classification and study reference; Table 2 for test species (rat, mice, rabbit, macaque), Dose levels and Maternal and developmental toxicity critical effects at LOAEL; Table 3 for parameters affected at or above study maternal or developmental toxicity; and Table 4 for the minimum set of parameters required to determine these toxicity endpoints).

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

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

  • A Structure Based Model for the Prediction of Phospholipidosis Induction Potential of Small Molecules. Sun H. et al. J Chem Inf Model. (5538)
  • KEYWORDS: MOLECULAR DESCRIPTORS – PHOSPHOLIPIDOSIS – QSAR MODELING – STRUCTURE-BASED PREDICTION

    Based on data of induction of phospholipidosis in HepG2 cells from 1280 (Library of Pharmacologically Active compounds, LOPAC), 2816 (National Institutes of Health Chemical Genomics Centrer Pharmaceutical collection, NPC) and 1395 (Tocris Biosciences collection) compounds, this article reports predictive models built using different training data and molecular descriptors (atom type or MOE 2D descriptors).

  • In silico Prediction of Total Human Plasma Clearance. Lombardo F. et al. J Chem Inf Model. (5537)
    Involved Partner: NVS
  • KEYWORDS: ADME – ASSAY DATA – MOLECULAR DESCRIPTORS – STRUCTURE-BASED PREDICTION

    Based on a large dataset of 754 compounds (220 neutral, 184 anionic, 285 cationic and 65 zwitterionic, see support material), authors present their PLS based model to tackle the plasma clearance prediction (see Table 2 and support material for the full list of descriptors considered in this study).

  • Large-scale prediction and testing of drug activity on side-effect targets. Lounkine E. et al. Nature. (5536)
    Involved Partner: NVS
  • KEYWORDS: DRUG DISCOVERY – DRUG SAFETY – PHARMACOLOGY – NETWORKS

    Based on data from proprietary databases (GeneGo Metabase, Thompson Reuters Integrity, Drugbank and GVKBio) or new experimental assays (Novartis), authors present their work to predict the adverse drug reactions for a set of 656 marketed drugs. A total of 339 molecular targets (defined as species-specific expansion of 73 targets from the Novartis safety panel, see tables 2 and 5 from Support Material) were found to have more than 285,000 chemicals annotated in ChEMBL database. The use of target annotations from GeneGo Metabase, Integrity, Drugbank, ChEMBL and GVKBio, provides a list of drug-targetpairs and target–ADRpairs and a network of drug-target-ADR can be generated (see Support material tables and Figure 3) to highlight new drug-off-target predictions (see Table 1 for examples).

  • Open PHACTS: Semantic interoperability for drug discovery. Williams AJ. et al. Drug Discov Today. (5533)
    Involved Partners: VUA, AZ, UNIVIE
    eTOX cited
  • KEYWORDS: PROJECT – DRUG DISCOVERY – PHARMACOLOGY

    The Open PHACTS project presents discussions on different aspects considered in the development of their open pharmacology space platform (industry drivers, the role of the Semantic Web, the intrinsic value of data, the multiplicity and quality of legacy data resources, the challenges of computational processing, the complexity of the name space, the need for community annotation, the data scope and acquisition, etc.). A short list of questions (see Box 1) is reported as examples of envisaged queries that users of OPS platform will be able to address to support their research steps.

  • Update on EPA’s ToxCast Program: Providing High Throughput Decision Support Tools for Chemical Risk Management. Kavlock R. et al. Chem Res Toxicol. (5532)
  • KEYWORDS: DATABASE – HEPATOCARCINOGENESIS – MOLECULAR DESCRIPTORS – REPRODUCTIVE AND DEVELOPMENTAL TOXICITY – RISK ASSESSMENT – TEXT MINING

    From 2007, the US Environmental Protection Agency (EPA) ToxCast has aimed to probe key biological events and pathways that can be perturbed due to interaction with certain chemicals by using high-throughput screenings, focused in cancer, reproductive and developmental toxicity. This article describes the results achieved during the Phase I of the project: the ToxCast chemical library (Phase I: 309 unique compounds, mostly pesticide type, see Table 1 for in vivo study data) and theToxCast Bioassay library (Phase I: 10 assay platforms and over 650 features (see Table 3 and sections 3.1-3.13 for further details of each assay platform). The compiled data of different toxicity endpoints has been made publicly available through 3 venues: the ToxCast Web site, the EPA ACToR database and other sites (ie., PubChem). The project has addressed 33 predictive different signatures (see the list in Table 5) and applies their Toxicity Prioritization Index (ToxPi) approach to group results into common domains of information.
    Since 2011, the project is under its Phase II stage and the database includes additional 700 compounds data and 135 chemicals data (preclinical and clinical data donated by several pharma companies: Pfizer Inc., Merck & Co., Inc., GlaxoSmithKline Plc., Sanofi-Aventis U.S. LLc., F. Hoffmann-La Roche Ltd., and Astellas Pharma Inc.).

May

  • Network analysis has diverse roles in drug discovery. Hasan S. et al. Drug Discov Today. (5528)
    Involved Partner: GSK
  • KEYWORDS: DRUG DISCOVERY – NETWORKS – PHARMACOLOGY – SYSTEMS BIOLOGY

    A review on the variety of different network approaches: 1) biological, 2) genome-scale metabolic, 3) protein interaction and signalling, 4) drug function and 5) social.

  • In-silico Predictive Mutagenicity Model Generation Using Supervised Learning Approaches. Seal A Mr. et al. JCheminform. (5527)
  • KEYWORDS: ASSAY DATA – DATABASE – MOLECULAR DESCRIPTORS – MUTAGENICITY – STRUCTURE-BASED PREDICTION

    Presentation of a new mutagenicity benchmark data set with around 8000 chemicals as a result of combination of 2 datasets (4337 compounds, and 6512 compounds from a benchmark dataset). In order to build models to predict the mutagenic potencial several classification algorithms (Naïve Bayes, Random Forest, J48 and SMO with 10 fold cross-validation and default parameters) were used for model generation on these data sets. Concretely, the Random Forest model was applied for the approved drugs in DrugBank database and for metabolies from the Zinc database, and reported a rate of Tru Positives of almost 85%.

  • Pharmacogenomic biomarkers for drug induced liver injury. Aklillu E. et al. Toxicol Lett. (5526)
  • KEYWORDS: PROJECT – DRUG DISCOVERY – PROJECTS

    A comprehensive compilation of current models of partnership between large pharma companies, academic institutions and biotech companies. Concretely this article reports on 16 precompetitive public–private partnerships for innovative drug discovery (see Table 1, like the IMI), 13 crowd sourcing models as open Innovation in drug discovery (see Table 2) and 7 Industry- Academic partnership (see Table 3).

  • US EPA – ToxCast and the Tox21 program: Perspectives. Judson R. Toxicol Lett. (5524)
  • KEYWORDS: DATABASE – PROJECTS

    Short communication on current attempts of both projects ToxCast (screening of 2000 compounds) an Tox21 (screening of 8000 compounds): (1) To build predictive models (toxicity signatures) of chemical toxicity linking in vitro activity to in vivo adversity; (2) to use these signatures to prioritize data-poor chemicals for more in-depth testing; and (3) to support systems biology models that can provide better understanding of the mechanistic basis of chemical toxicity.

  • Exploring the Human Protein Atlas in the field of toxicology. Uhlen M. Toxicol Lett. (5522)
  • KEYWORDS: BIOMARKERS – DATABASE

    Short communication that the Human Protein Atlas data is under analysis to find potential biomarkers for toxicology. The current version 9.0 of the Human Protein Atlas (www.proteinatlas.org) contains more than 15,000 validated antibodies targeting 12,200 genes corresponding to more than 60% of the protein-encoded genes in humans.

  • Systems biology tools for toxicology. Geenen S. et al. Arch Toxicol. (5520)
  • KEYWORDS: DRUG DISCOVERY – SYSTEMS BIOLOGY – PATHWAYS

    Focused on the glutathione detoxification pathway, this article discusses about how systems biology could help to understand the toxicity mechanisms based on, for instance, kinetic modelling,and metabolic control, robustness and flux analysis.

  • Improving integrative searching of systems chemical biology data using semantic annotation. Chen B. et al. J Cheminform. (5519)
  • KEYWORDS: DATABASES – DATA MINING – DRUG DISCOVERY – PHARMACOLOGY – PATHWAYS – PROTEINS – TEXT MINING

    The Chem2Bio2OWL is a generalized chemogenomics and systems chemical biology OWL ontology called that describes the semantics of chemical compounds, drugs, protein targets, pathways, genes, diseases and side-effects, and the relationships between them, to facilitate answer to complex queries like: 1. What are the protein targets of the drug Troglitazone? 2. Find PPARG inhibitors with molecular weight less than 500. 3. Which pathways will be affected by Troglitazone? 4. Find all bioassays that contain activity data for a particular target. 5. What liver-expressed proteins can a given compound interact with? 6. Which proteins are able to interact with protein PPARG ? 7. Which drugs are used to treat diabetes but withdrawn from market? 8. Which assays test the activity of Troglitazone against PPARG ? preferably give the literature.

  • Measuring the level of activity in community built bio-ontologies. Malone J. et al. J Biomed Inform. (5516)
  • KEYWORDS: DATA MINING – TEXT MINING

    An analysis on 43 bio-ontologies (see Table 1), with a total of 5036 versions, to report global and individual trends. Authors detail their method to perform this analysis (see section 2) and list some recommendations for the evolution of such ontologies regarding their updates.

  • The use of quantum-chemical descriptors for predicting the photoinduced toxicity of PAHs. Al-Fahemi JH. J Mol Model. (5514)
  • KEYWORDS: MOLECULAR DESCRIPTORS – PHOTOTOXICITY – QSAR MODELING – STRUCTURE-BASED PREDICTION

    Based on a set of 19 polyaromatic hydrocarbons (PAHs) structures, various quantum-chemical descriptors (energy of energy of the highest occupied molecular orbital (EHOMO), the energy of the lowest unoccupied molecular orbital (ELUMO), the difference in energy between those orbitals (EGAP), electronegativity (χ), chemical potential (μ), chemical hardness (η), softness index (S), electrophilicity (ω), and polarizability (α)) together with some physicochemical descriptors (molecular weight (M), logP, volume (V) and molar refractivity (MR)) are used to develop QSAR models to predict the phototoxicity potential of PAHs structures. The results show that ELUMO, EHOMO, EGAP, S, χ, MR and M can help in the assessment of such prediction (see Table 2 for correlation with logEC50), but the models developed in fact seem to have the limitation of a close applicability domain capability.

  • Using Self-Organizing Maps to Accelerate Similarity Search. Fanny B. et al. Bioorg Med Chem. (5513)
  • KEYWORDS: DRUG DISCOVERY – MOLECULAR DESCRIPTORS – STRUCTURE-BASED PREDICTION

    This article presents how use of Kohonen Sel-fOrganizing Maps based on vectors of high-dimensional real-value decritptors accelerates the performance of similarity searches. Authors work with different sets of molecules (DataBase 55613 molecules, Query Set 2000 molecules, Extended 5320 molecules, SmallRef 11168 molecules, External Database 160000 molecules and a subset of the Database of Useful Decoys (DUD)).

  • Comparative evaluation of pKa prediction tools on a drug discovery dataset. Balogh GT. et al. J Pharm Biomed Anal. (5511)
  • KEYWORDS: ADME – ASSAY DATA – PHARMACOKINETICS

    A new exercise to predict pKa values with higher accuracy, based on consensus of different tools (ACD, Epik, Marvin, Pallas and PhAlg (ADMEBox)) evaluated for a set of 95 diverse drug-like molecules (see Support Material 1).

  • OpenTox predictive toxicology framework: toxicological ontology and semantic media wiki-based OpenToxipedia. Tscheremenskaia O. et al. J Biomed Semantics. (5509)
  • KEYWORDS: PROJECT – DATABASE – DATA MINING – PROJECT

    The OpenTox consortium presents the use case example on the RepDose database to show the interoperability of their services and data resources (see Support material which offers the Repdose database annotated by Organs and Effects Ontology in RDF/N3 format). They report the development and contents of different ontologies defined in the framework of the OpenTox project: a)Toxicological ontology – listing the toxicological endpoints; b) Organs system and
    Effects ontology – addressing organs, targets/examinations and effects observed in in vivo studies; c) ToxML ontology – representing semi-automatic conversion of the ToxML schema; d) OpenTox ontology– representation of OpenTox framework
    components: chemical compounds, datasets, types of algorithms, models and validation web services; e) ToxLink–ToxCast assays ontology and f) OpenToxipedia community knowledge resource on toxicology terminology.

  • Ontology-Driven Relational Query Formulation Using the Semantic and Assertional Capabilities of OWL-DL. Munir K. et al. Knowledge-Based Systems. (5506)
  • KEYWORDS: CONSORITUIM – DATA MINING – PROJECT – TEXT MINING – VOCABULARIES

    The article presents a systemic and modular architectural framework, the Ontology Based Query Formulation (OntoQF) (see an schema in Figure 2) developed in the frameowork of the Health-e-Child project (see section 5 and Figure 9 for project goals and application of this tool). TheOntoQF aims to assist domain experts to conduct domain specific studies without requiring previous knowledge of the database query languages like SQL. The authors discuss in detail several aspects of this engine (see sections 3, 4).

  • What is the most important approach in current drug discovery: doing the right things or doing things right?. Elebring T. et al. Drug Discov Today. (5505)
  • KEYWORDS: DRUG DISCOVERY

    A recommended reading to reflect about pros and cons of the three targeted aspects -speed, cost and quality- for pharma companies currently to increase success in their new drug candidates.

  • Systematic drug repositioning based on clinical side-effects. Yang Let al. PLoS One. (5503)
  • KEYWORDS: DATABASE – DRUG DISCOVERY

    This articles provides a network of 3,175 site effect – disease associations (see Support Material Table S1) from PharmGKB information.

April

  • Systematic identification of genomic markers of drug sensitivity in cancer cells. Garnett MJ. et al. Nature. (5502)
  • KEYWORDS: ASSAY DATA -BIOMARKERS – DATABASES – GENOTOXICITY – PHARMACOLOGY

    A new database of 130 drugs under clinical and preclinical investigation is presented as an example of valuable information to support the systematic identification of biomarkers, focused in the drug sensitivity in a wide list of cancer cells that represent most of the tissue-type and genetic diversity of human cancers (see Support Material S2). For future updates of this database visit the Genomics of Drug Sensitivity in Cancer.

  • The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Barretina J. et al. Nature. (5501)
    Involved Partner: NVS
  • KEYWORDS: ASSAY DATA – DATABASES – GENOTOXICITY – PHARMACOLOGY

    Presentation and description of the Cancer Cell Line Encyclopedia (CCLE), a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. A total of 479 of these cell lines allowed identification of genetic, lineage and gene-expression-based predictors based on the pharmacological profiles of 24 anticancer drugs (see Support Material 2, Table S6) to assess drug sensitivity (see Support Material 3, Table S11 for cell-compound activity).

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

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

  • Expanding the medicinally relevant chemical space with compound libraries. López-Vallejo F. et al. Drug Discov Today. (5498)
  • KEYWORDS: DATABASES – DRUG DISCOVERY – PHARMACOLOGY

    Discussion on data available and possible strategies to move research from the chemical space with current known therapeutic effect to an unexplored chemical space based on combinatorial libraries structures. In order to explore new approaches, properties of a set of 30 small-molecule combinatorial libraries (TSRL), a large collection of natural products assembled in the Traditional Chinese Medicine (TC) database, and other standard databases like ZINC, DrugBank, Maybridge and NCI are evaluated in terms of complexity and structural diversity.

  • Qualitative prediction of blood-brain barrier permeability on a large and refined dataset. Muehlbacher M. et al. J Comput Aided Mol Des. (5497)
  • KEYWORDS: ASSAY DATA – MOLECULAR DESCRIPTORS – PHARMACOKINETICS

    Authors presents the compilation of a large dataset of 362 compounds with experimental logBB values (see support material table), apply a model previously described in the literature, and calculate quantitative models using bootstrap validated multiple linear regression. The evaluation of a list of 33 molecular descriptors developed or reproduced in addition to the standard descriptors (see Table 1) reports that a subset of 4 molecular descriptors (see section Qualitative models and Table 4) provides the best models and predictions.

  • 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. (5495)
  • KEYWORDS: ASSAY DATA – PHARMACOKINETICS

    The largest dataset to predict Volume of distribution is presented as compilation of several resources to support the assessment of human pharmacokinetic prediction based on in vivo animal pharmacokinetic data (see support material for data of over 400 compounds (name, CAS number, human/rat/dog/monkey volume of distribution and clearance values)).

  • Comprehensive Assessment of Human Pharmacokinetic Prediction Based on In Vivo Animal Pharmacokinetic Data, Part 2: Clearance. Lombardo F. et al. J Clin Pharmacol. (5494)
  • KEYWORDS: ASSAY DATA – PHARMACOKINETICS

    The largest dataset to predict Clearance is presented as compilation of several resources to support the assessment of human pharmacokinetic prediction based on in vivo animal pharmacokinetic data (see support material for data of over 400 compounds (name, CAS number, human/rat/dog/monkey volume of distribution and clearance values)).

  • Cationic amphiphilic drugs cause a marked expansion of apparent lysosomal volume: Implications for an intracellular distribution-based drug interaction. Funk RS. et al. Mol Pharm. (5490)
  • KEYWORDS: PHARMACOKINETICS – PHOSPHOLIPIDOSIS

    This study aims to predict the influence of accumulation in lysosomes of one drug once other a second drug is administered. Some information of related mechanisms to the phospholidosis is discussed.

  • Prediction of the human oral bioavailability by using in vitro and in silico drug related parameters in a physiologically based absorption model. Paixão P. et al. Int J Pharm. (5489)
  • KEYWORDS: ADME – ASSAY DATA – PHARMACOKINETICS

    This article presents the evaluation of a model to predict human oral bioavailability (see Figure 1 to know the structure of the physiological-based pharmacokinetic model of absorption considering drug dissolution and absorption in the GIT and drug metabolization in the liver) and its applicability for a set of 164 drugs (see Table 2 for drug name, drug oral bioavailability, drug total Clearance determined in plasma, blood to plasma concentration ratio of the drug, drug class (acid, basic, neutral or zwitterionic) and the drug blood hepatic clearance). Different groups of these set of compounds are defined to evaluate prediction performance depending on the data available: 1) 49 drugs with in vitro data for both Papp and Clint (see Table 3); 2) 25 drugs with in vitro data for Clint only (see Table 4); 3) 22 drugs with in vitro data for Papp only (see Table 5); and 4) 68 drugs with only in silico based data (see Table 6). The in silico based data estimation is a valuable approach to estimate this fundamental pharmacokinetic parameter, the oral bioavailability.

March

  • Consensus hologram QSAR modeling for the prediction of human intestinal absorption. Moda TL. et al. Bioorg Med Chem Lett. (5487)
  • KEYWORDS: ASSAY DATA – METABOLISM – PHARMACOKINETICS – PHARMACOLOGY – QSAR MODELING – STRUCTURE-BASED PREDICTION

    Based on a experimental data of 638 compounds extracted from PK/DB database (see compounds names and values in tables 1 (510 training set) and 2 (128 test set) of Support Material), authors apply a hologram QSAR modeling method to predict human intestinal absorption, a pharmacokinetic property of interest as it is an essential requirement for orally administration of drugs to define dose and to achieve a certain pharmacological effect.

  • Prediction of promiscuous p-glycoprotein inhibition using a novel machine learning scheme. Leong MK. et al. PloS One. (5486)
  • KEYWORDS: ASSAY DATA – METABOLISM – P-gP – PHARMACOKINETICS – PHARMACOLOGY – STRUCTURE-BASED PREDICTION – TRANSPORTERS

    Following the consensus tendency of using pharmacophore ensemble to model interaction between P-gP and its substrates or inhibitors, this article presents a newly invented pharmacophore ensemble/support vector machine (PhE/SVM) schema based on the data compiled from the literature (find compounds names, SMILES and pEC50 values in Table 1 of the Support Material, training set (31) and the outlier set (11)) to predict the binding affinity of P-gp inhibitors. Particularly, the HypoGen module in Discovery Studio software is applied for automatic pharmacophore generation..

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

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

  • Aggregating Data for Computational Toxicology Applications: The U.S. Environmental Protection Agency (EPA) Aggregated Computational Toxicology Resource (ACToR) System. Judson RS. et al. Int J Mol Sci. (5483)
  • KEYWORDS: DATABASE – PHARMACOLOGY – RISK ASSESSMENT

    A comprehensive description of the ACToR (Aggregated Computational Toxicity Resource) database which comprises: core ACToR (chemical identifiers and structures, and summary data on hazard, exposure, use, and other domains), ToxRefDB (Toxicity Reference Database, a compilation of detailed in vivo toxicity data from guideline studies), ExpoCastDB (detailed human exposure data from observational studies of selected chemicals), and ToxCastDB (data from high-throughput screening programs, including links to underlying biological information related to genes and pathways). Details of compilation and organization of such amount of compounds information are shown in figures 1 and 2, and to figure out of the data search options some screenshots are provided (see figures 3-7, and 9).

  • Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data. Rusyn I. et al. Toxicol Sci. (5480)
  • KEYWORDS: ADME – CARCINOGENICITY – MOLECULAR DESCRIPTORS – QSAR MODELING – SOFTWARE – STRUCTURE-BASED PREDICTION – TOXICOGENOMICS

    A review to sum up novel strategies for integrating chemical structural information with bioactivity data (see Figure 1); examples of commercial toxicity predictors (see Table 1 for details of ADMET Predictor, ACD/Tox Suite, DEREK and DEREK Nexus, TOPKAT, CASE, Leadscope Model Applier, and HazardExpertPro and ToxAlert); examples of toxicity predictors in public domain (see Table 2 for details of T.E.S.T (EPA), OncoLogic (EPA), OpenTox, OECD QSAR Toolbox, OCHEM and ChemBench), and examples of toxicity data-based predictive models (see Table 3 for details of input variables and references for several models currently available in the public domain for Reproductive toxicity, Hepatotoxicity, Hepatobilliary injury, Hepatocarcinogenicity, Hepatotumorogenesis, Neprhotoxicity and related predictions).

  • Towards a gold standard: regarding quality in public domain chemistry databases and approaches to improving the situation. Williams AJ. et al. Drug Discov Today. (5479)
  • KEYWORDS: DATABASES

    Discussion on error detection in databases, the quality of chemistry databases, the trust and chemistry of databases, structure-identifier relationships in chemical databases, misassociations examples, structure validation filters (incorrect valence, atom labels, aromatic bonds, non-zero total charge, absent stereochemistry, salts with covalent bonds, 0D structure layout, and duplicated structures), data proliferation between databases, structure standardization (e.g, Open PHACTS strategy), provenance in databases, crowdsourced review of public domain databases, to highlight the need of correction and harmonization of current databases contents.

  • A regulatory approach to assess the potency of substances toxic to the reproduction. Muller A. et al. Regul Toxicol Pharmacol. (5477)
  • KEYWORDS: ASSAY DATA – REPRODUCTIVE AND DEVELOPMENTAL TOXICITY

    This article reports average values and potency differences for NOEL LOAEL and ED10 parameters for all developmental toxicants gathered in a database of compounds (see Table 1), together with differences in the average values of parameters between substances classified in different categories (see Table 2), between substances classified for developmental toxicity based on rat or rabbit studies (see Table 3) and between substances known toxic to the development and also classified for mutagenicity or not (see Table 4). In addition, differences in average values are also analysed for effects on sexual function and fertility (see tables 7-10). Find the individual parameters for 99 substances (names and CAS) for developmental toxicity in the Support Material table.

  • Integrating non-animal test information into an adaptive testing strategy – skin sensitization proof of concept case. Jaworska J. et al. ALTEX. (5475)
  • KEYWORDS: ASSAY DATA – SKIN SENSITIZATION

    Compilation of information for a data set consisted of responses of 142 chemicals (find names, SMILES and CAS number and experimental data in the Support Material) in the following tests: epidermal bioavailability data, peptide reactivity assays, dendritic cell activation, and TIMES predictions.

  • Data mining techniques and applications – A decade review from 2000 to 2011. Liao S-H. et al. Expert Syst Appl. (5474)
  • KEYWORDS: DATA MINING – TEXT MINING

    An exhaustive review on Data Mining techniques emerged during the last decade (2000-2011) to highlight the different trends in such field. Those based on Neuronal networks (see Table 2), Algorithm architecture (see Table 3), Dynamic prediction bases approaches (see Table 4), Analysis of system architecture (see Table 5), Intelligence agent systems (see Table 6), Modeling (see Table 7), Knowledge-based systems (see Table 8), System optimization (see Table 9) and Information systems (see Table 10), their limitations and applications.

February

  • Computational Prediction of Metabolism: Sites, Products, SAR, P450 Enzyme Dynamics, and Mechanisms. Kirchmair J. et al. J Chem Inf Model. (5473)
  • KEYWORDS: DATABASES – CYP450 – METABOLISM – PHARMACOLOGY – QSAR MODELING – SOFTWARE

    A comprehensive compilation of information around CYP450 enzymes structures and the prediction of their metabolic activity. More than useful can be the list of methods for predicting structures of metabolites (SOMs), and interactions with metabolizing enzymes (see Table 1) that authors split in 4 main blocks: methods for predicting SOMs (e.g., MetaSite), methods for predicting xenobiotic metabolites (e.g., Meteor), methods for predicting CYP binding affinity/inhibition by xenobiotics, and methods for predicting CYP induction by xenobiotics.

  • 2011 Annual Meeting of the Safety Pharmacology Society: an overview. Cavero I. et al. Expert Opin Drug Saf. (5472)
    Involved Partners: AZ, PFIZER, NVS, J&J, ROCHE
  • KEYWORDS: DRUG SAFETY – NEPHROTOXICITY – PARMACOLOGY

    This article provides summaries of some of the presentations (3 Plenary sessions, and 2 from Track A: Safety Pharmacology between academia, regulators and industry; 5 from Track B: Applied techonologies and 2 from Track C: Translation) held in the 2011 Annual meeting of the Safety Pharmacology Society, Innsbruck September 2011. The keynote of this edition of addressed to examine the known an the still to be known on drug-induced neprhotoxicity.

  • The role of aldehyde oxidase in drug metabolism. Garattini E. et al. Expert Opin Drug Metab Toxicol. (5470)
  • KEYWORDS: ENZYMES – METABOLISM

    A comprehensive review of information on Aldehyde oxidase to understand its role in drug metabolism. Authors discuss on its structure and catalytic activity (see Figure 1), provide examples of drugs and xenobiotics that metabolizes (see Figure 2), remark the variance in different animal species of its active genes, and finally provide a list of all the known human AOX1 gene allelic variants available (see Table 2).

  • Photosensitivity: a current biological overview. Elkeeb D. et al. Cutan Ocul Toxicol. (5469)
  • KEYWORDS: ASSAY DATA – PHOTOTOXICITY

    A review on tests (animal data, in vitro assays, and human data (see tables 2,3 and 4 respectively)) for evaluation of photosensitivity (phototoxicity and photoallergy) and a list of photosensitizing chemicals/drugs (see Table 1).

  • Nuclear receptors in the multidrug resistance through the regulation of drug-metabolizing enzymes and drug transporters. Chen Y. et al. Biochem Pharmacol. (5466)
  • KEYWORDS: CYP450 – ENZYMES – METABOLISM – NUCLEAR RECEPTORS – TRANSPORTERS

    A valuable update on drug biotransformation , metabolism and excretion system information regarding the PXR and CAR nuclear receptors regulation of phase I (CYP1A1, CYP1A2, CYP2A4, CYP2A6, CYP2B1/2, CYP2B6, CYP2B10, CYP2C8, CYP2C9, CYP2C19, CYP2C37, CYP3A2, CYP3A4, CYP3A7, CYP3A11, CYP3A23, CYP4F12, CYP7A1, AKR1C1/2, ALDH1, and AKR1B7) and phase II (UGT1A1, UGT1A3, UGT1A6, UGTA9, UGT2B1, UGT2B5, GSTA1, SULT2A1, SULT1E1, SULT2A2, and SULT1B1) drug metabolizing enzymes, and drug transporters (MDR1, MRP1, MRP2, MRP3, MRP4, and SLCO1A4) with data of their co-expression in human prostate tumor (see Table 1 for a compilation of references).

  • Reactions and enzymes in the metabolism of drugs and other xenobiotics. Testa B. et al. Drug Discov Today. (5462)
  • KEYWORDS: ADME – CYP450 – ENZYMES – METABOLISM – STRUCTURE-BASED PREDICTION

    A meta-analysis of current research on reactions and enzymes related with drug metabolism, based on 903 papers compiled (see Support Material for a full references list). This overview reports a total of 1171 distinct substrates with 6767 distinct metabolites. According to reaction types, defined in Table 2 (type of reaction, the enzyme that produces it, the metabolic generation (first, second or third-plus)), the distribution of these metabolites is analyzed to highlight role of different enzymes involved in the drug metabolism (see Table 3).

January

  • (Q)SAR Modeling and Safety Assessment in Regulatory Review. Kruhlak NL. et al. Clin Pharmacol Ther. (5461)
  • KEYWORDS: CARCINOGENICITY – GENOTOXICITY – PHOSPHOLIPIDOSIS – QSAR MODELING – REPRODUCTIVE AND DEVELOPMENTAL TOXICITY – STRUCTURE-BASED PREDICTION

    A summary of recent models developed (nonclinical: carcinogenicity, reproductive and developmental, phospholipidosis; adverse human effects: hepatobiliary, renal/bladder, cardiac, pulmonayr, immunological) and used by the US Food and Drug Administration (FDA) Center for Drug Evaluation and Research (CDER), see Table 1cadd. Focused on toxicity prediction, basic principles of QSAR modeling are compiled to stand the requirements and limits of this methodology.

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

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

  • Predicting the mechanism of phospholipidosis. Lowe R. et al. J Cheminform. (5458)
  • KEYWORDS: PHOSPHOLIPIDOSIS – STRUCTURE-BASED PREDICTION

    Application of the Parzen-Rosenbaltt Window method to a set of 182 compounds (100 phospholipidosis inducers, and 82 negative) taking into account 10 models built with different subsets of ChEMBL database retrieves a list of 20 phospholipidosis-relevant targets (see Table 2), that can help to understanding the phospholipidosis mechanism.

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

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

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

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

  • Should medicinal chemists do molecular modelling?. Ritchie T.J. et al. Drug Discov Today. (5454)
  • KEYWORDS: DRUG DISCOVERY

    This is an interesting article opinion on pros and cons to give a role to the medicinal chemists in performing Computer-aided Drug Design (CADD) activities. Authors highlight different aspects to take into account at time to train the medicinal chemists to be involved in such activities, and also remark the needs of software developments considering this new potential users. Advantages are clear but companies and vendors need time for adaptation.

  • Can we discover pharmacological promiscuity early in the drug discovery process?. Peters J-U. et al. Drug Discov Today. (5453)
  • KEYWORDS: DRUG DISCOVERY – PHARMACOLOGY

    Based on compounds collected in BioPrint database, this analysis of pharmacology promiscuity finds out that the promiscuity decreases for highly lipophilic compounds presumably due to insufficient solubility; increases with molecular weight in neutral compounds; increases in charged compounds when decreases their number of hydrogen bond donors (HBD) and acceptors (HBA); an that the lipophilicity correlates directly with the number of aromatic rings and inversely with HBD and HBA.

  • Systems chemical biology and the Semantic Web: what they mean for the future of drug discovery research. Wild DJ. et al. Drug Discov Today. (5452)
  • KEYWORDS: PROJECT – DRUG DISCOVERY – PROJECT – SOFTWARE – SYSTEMS BIOLOGY

    This is an article from the Open PHACTS consortium to aware the scientific audience about the Semantic Web basis and their current applications in the drug discovery field (see Figure 2, example of disease association to a ceratin drug through a network of protein annotations from diverse sources). Different formats (OWL, XML) to facilitate ontologies storage and the use of sparQL language to search queries defined in RDF following the triple store concept are shown to be powerful for knowledge discovery with the idea to interlink information from the the use of several databases integrated.

  • Chromatography approaches for early screening of the phospholipidosis-inducing potential of pharmaceuticals. Jiang Z. et al. J Pharm Biomed Anal. (5451)
    Involved Partner: NVS
  • KEYWORDS: ASSAY DATA – PHOSPHOLIPIDOSIS

    In order to facilitate phospholipidosis power of new drugs, this article supports a new strategy to alert about phospholipidosis risk at early drug discovery stages. Based on a set of 36 compounds, authors evaluate correlation between chromatographic retention parameters and other well known drug properties like volume of distribution and lipophilicity (see values in Table 1). The results recommend to consider both log kAOT and log Vd show because the higher the values are, the higher the phospholipidosis risk.

  • Clearance-dependent underprediction of in vivo intrinsic clearance from human hepatocytes: Comparison with permeabilities from artificial membrane (PAMPA) assay, in silico and caco-2 assay, for 65 drugs. Hallifax D. et al. Eur J Pharm Sci. (5450)
  • KEYWORDS: ASSAY DATA – PHARMACOKINETICS

    Report of data, combining published intrinsic clearances and permeability constants for a range of 65 drugs (see Table 1), including PAMPA data generated for a study to predict the clearance aspects in the case of human hepatocytes. The results of the evaluation of such data comparison revealed that the prediction accuracy was not dependent on the relative permeability (measured as the ratio of CLint to permeability), which indicates the absence of a general rate limitation by passive hepatocyte uptake on metabolic clearance concerns.

  • Self-organizing ontology of biochemically relevant small molecules. Chepelev LL. et al. BMC Bioinformatics. (5449)
  • KEYWORDS: DATA MINING – TEXT MINING – SEMANTIC WEB

    In order to facilitate the integration of bioactivity data form different sources, the authors present a methodology to make chemical classification criteria more transparent to both human and machines. Based on Semantic Web technologies, their present the design of a chemical ontology as a result of 60 MeSH and 40 ChEBI chemical classes (see figures 5, 6 and 7), providing details on relationships defined along this approach (see figures 1, 2 and 3, and Table 2 for a list of chemical entities covered in this exercise).