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Previous (2003-2009)

2009

  • Calling International Rescue: knowledge lost in literature and data landslide!. Attwood TK. et al. Biochem J. (5174)
  • KEYWORDS: DATA MINING – SOFTWARE – TEXT MINING

    This article offers an attractive reading of a extended review (readable by UTOPIA piece of software) that examines some recent initiatives to make published biomedical texts more machine-readable and outlines a variety of projects involving academic-journal collaborations.

  • Classification of cytochrome P450 inhibitors with respect to binding free energy and pIC50 using common molecular descriptors. Dagliyan O. et al. J Chem Inf Model. (5059)
  • KEYWORDS: CYP450 – DRUG-DRUG INTERACTION – MOLECULAR DESCRIPTORS – STRUCTURE-BASED PREDICTION

    Evaluation of the relationship between binding free energies and biological activities (pIC50) for a set of cytochromes (see Table 1 where CYP considered and number of ligands are listed) based on common molecular descriptors calculated by using the E-DRAGON Web server. The descriptor sets used are basically constitutional descriptors (walk and path counts, topological descriptors, connectivity indices, information indices, 2D autocorrelations, edge adjacency indices, Burden eigenvalues, topological charge indices, eigenvalue based indices, Randic molecular profiles, geometrical descriptors, RDF descriptors, 3D-MORSE descriptors, WHIM descriptors, GETAWAY descriptors, functional group counts, atom-centered fragments, charge descriptors, and molecular properties). The mixed integer linear programming based hyperboxes (MILP-HB) method stands out due to very accurate prediction provided for all data considered in this study.

  • A method for automated molecular optimization applied to Ames mutagenicity data. Helgee EA. et al. J Chem Inf Model. (5073)
    Involved Partner: AZ
  • KEYWORDS: MUTAGENICITY – QSAR MODELING – STRUCTURE-BASED PREDICTION

    Description and application of a new method that optimizes a compound in terms of structure-based prediction in case of a set of compounds that have a mutagenicity liability. After identification of substructures that contribute the most to the mutagenicity prediction, the specific substructures are replaced for each query compound by application of a deterministic fashion to produce a range of new, nonmutagen, compounds.

  • Computational toxicology approaches at the US Food and Drug Administration. Yang C. et al. Altern Lab Anim. (5055)
  • KEYWORDS: DATABASES – REGULATORY GUIDELINES – RISK ASSESMENT

    This commentary review describes former and current scientific initiatives at the United States Food and Drug Administration (US FDA), in the area of computational toxicology methods: toxicology-based QSAR models (see Table 1 with computational toxicology 2-D QSAR model types, which have been developed through research at the FDA/CDER), and ToxML databases and knowledgebases.

  • Safety Testing of Drug Metabolites. Thompson TN. Annu Rep Med Chem. (5014)
  • KEYWORDS: DRUG SAFETY – METABOLISM

    This book chapter reviews the evolution of the Metabolites in Safety Testing (MIST) guidance reported by the FDA, from 2002 to 2008. Based on the clear mandate for introducing new drugs is that they be both effective and safe, the FDA emphasizes that gaining a more thorough understanding of how metabolism may affect the toxicity of drugs is not only prudent, but necessary and list recommendations, not requirements, to achieve this objective.

  • Improving Compound Quality through in vitro and in silico Physicochemical Profiling. van de Waterbeemd H. Chem Biodivers. (5054)
  • KEYWORDS: ADME – DRUG DISCOVERY – QSAR MODELING – STRUCTURE-BASED PREDICTION

    Current physicochemical in vitro assays and in silico predictions to support compound and library design through to lead optimization are reviewed in this paper with special focus to lipophilicity (log P/D), solubility, and permeability (Caco-2 cell line and PAMPA assay) since they are the most important physicochemical properties and drive key ADMET properties such as absorption, cell penetration, access to the brain, volume of distribution, plasma protein binding, metabolism, and toxicity, as well as biopharmaceutical behavior. They provide an scheme with the key inter-relationships between physical-chemical properties (see Figure 2) to aware about their strong interdependence.

  • VirtualToxLab – in silico prediction of the toxic (endocrine-disrupting) potential of drugs, chemicals and natural products. Two years and 2,000 compounds of experience: a progress report. Vedani A. et al. ALTEX. (5028)
  • KEYWORDS: CYP450 – hERG – NUCLEAR RECEPTORS – QSAR MODELING – RISK ASSESSMENT – SOFTWARE

    Introduction to an in silico tool for predicting the toxic (endocrine-disrupting) potential of drugs, chemicals and natural products, the VirtualToxLab™. At that time, the software was able to calculate the binding affinity of any molecule of interest towards a series of 12 proteins (AR, AhR, ERα, ERβ, GR, LXR, MR, PPARγ, TRα, TRβ, CYP2A13 and CYP3A4, known or suspected to trigger adverse effects and estimates the resulting toxic potential, currently 16 validated models (addition of hERG, CYP1A2, CYP2C9 and CYP2D6, see http://www.biograf.ch/index.php?id=projects&subid=virtualtoxlab). Remarkable advantages are, in contrast to other approaches in this field, it allows to rationalize a prediction at the molecular level by interactively analyzing the binding mode of the tested compound with any target protein in 3D, it has a user-friendly interface which allows up/download of all necessary data (compounds, 3D models, affinities, toxic potential), its input can easily be generated using the VTLBuilder (a 3D model builder) supplied with the distribution and it is accessible over the Internet (via a secure SSH protocol) and available for any science-oriented organization by a modest fee. As similar tools, its application pretends to help in the reduction of animal testing.

  • Predicting new molecular targets for known drugs. Keiser MJ. et al. Nature. (5015)
  • KEYWORDS: DRUG DISCOVERY – NETWORKS – PHARMACOLOGY

    Assuming that drug–target combinations exist, this work examplfies by a comparison between 3,665 US Food and Drug Administration (FDA)-approved and investigational drugs against more than 1,400 protein targets, defining each target by its ligands how computationally can be addressed an exploration of possible interactions between chemical and biological entities. They stand out by application of the chemical similarity approach that one may be able to suggest side-effects and new indications for many drugs predicting polypharmacology on a large scale.

  • Impact of OATP transporters on pharmacokinetics. Kalliokoski A. et al. Br J Pharmacol. (5043)
  • KEYWORDS: PHARMACOKINETICS – TRANSPORTERS

    This review updates the current knowledge about the expression and function of human OATP transporters expressed in various tissues important for pharmacokinetics, and their substrate and inhibitor specificities (se Table 2 and 3), as well as pharmacogenetics.

  • In silico toxicology for the pharmaceutical sciences. Valerio LG Jr. Toxicol Appl Pharmacol. (4399)
  • KEYWORDS: DATABASES – DRUG SAFETY – STRUCTURE-BASED PREDICTION

    Current topics and outline of general considerations in structure-based predictive in silico technologies in toxicology are detailed, together with fundamental concepts covered for non-specialists understanding of what in silico toxicology involves, why their applied use is of interest in the pharmaceutical sciences, and how these methods work or not as a paradigm. A balanced critical assessment is presented with attention to limitations to facilitate objectivity into the consideration of their integration within regulatory risk/benefit-based decision making. This review includes an extended compilation of publicly available toxicity, QSAR, and human health effect databases (see Table 1).

  • Benchmark Data Set for in Silico Prediction of Ames Mutagenicity. Hansen K. et al. J Chem Inf Model. (5034)
    Involved Partner: BSP
  • KEYWORDS: ASSAY DATA – MUTAGENICITY – STRUCTURE-BASED PREDICTION – SOFTWARE

    Based on a new unique public Ames mutagenicity data set of nonconfidential compounds (3503 Ames positive and 3009 Ames negative, SMILES and SDF are available from http://ml.cs.tu-berlin.de/toxbenchmark), this article provides a comparison between commercial tools (DEREK, MultiCASE and an off-the-shelf Bayesian machine learner in Pipeline Pilot) and four noncommercial machine learning implementations (Support Vector Machines, Random Forests, k-Nearest Neighbors, and Gaussian Processes) to evaluate current Ames mutagenicity prediction tools. All five machine learning methods yield good results, and future development must be addressed to improve their accurary and interpretation of results.

  • Novel opportunities for computational biology and sociology in drug discovery. Yao L. et al. Trends Biotechnol. (5005)
  • KEYWORDS: BIOMARKERS – DRUG DISCOVERY – PATHWAYS – TEXT MINING

    Overview of emerging computational approaches for modeling entire biological pathways relevant to diseases. These promising opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. The authors remark the lack of models to interlink acurately a successful discovery and the dynamic organization of researchers and resources that derive it.

  • Approaches to seizure risk assessment in preclinical drug discovery. Easter A. et al. Drug Discov Today. (5040)
    Involved Partner: AZ
  • KEYWORDS: DRUG DISCOVERY – RISK ASSESSMENT

    An update of current techniques (In silico approaches: Computational models and Computer simulation of seizure; in vitro assays: Pharmacological profiling and In vitro electrophisiology; and in vivo methods: Zebrafish, Early in vivo studies (DMPK), Standard safety studies, Rodent precipitant models and Electroencephalogram) available to assess of seizure risk.

  • Drug-induced arrhythmias and sudden cardiac death: implications for the pharmaceutical industry. Killeen MJ. Drug Discov Today. (5001)
  • KEYWORDS: CARDIOTOXICITY – RISK ASSESSMENT

    This review begins by exploring clinical findings and potential mechanisms underlying drug-induced sudden cardiac death and then goes on to assess current and explore future strategies to detect cardiotoxicity at the preclinical stage to afford the ability of medications to induce potentially fatal arrhythmias which is undoubtedly; a significant problem facing the pharmaceutical indrustry.

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

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

  • Toxicity Testing in the 21st Century: Bringing the Vision to Life. Andersen ME. et al. Toxicol Sci. (5021)
  • KEYWORDS: DRUG DISCOVERY – PATHWAYS – RISK ASSESSMENT

    Opinion of two committee members that contribute to the National Academy of Sciences report titled ‘Toxicity Testing in the 21st Century: A Vision and a Strategy’ in 2007, where the key elements were related to toxicity testing and included the types of in vitro tests and short-term in vivo tests to evaluate perturbations on toxicity pathways, and dose-response and extrapolation modeling, which provided the requisite tools for interpreting toxicity testing results for assessing human health risk assessment (see Table 1).

2008

  • Application of in vitro neurotoxicity testing for regulatory purposes: Symposium III summary and research needs. Bal-Price AK. et al. Neurotoxicology. (5547)
  • KEYWORDS: NEUROTOXICITY

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  • Sharing Chemical Information without Sharing Chemical Structure. Masek BB. et al. J Chem Inf Model. (5003)
  • KEYWORDS: STRUCTURE MASKING – MOLECULAR DESCRIPTORS

    One of the last examples of efforts to design an strategy to safely exchange chemical information without revealing chemical structures. They demonstrate that unless sufficient precautions are taken, de novo design software such as EA-Inventor (Tripos) is able to derive a unique chemical structure or a set of closely related analogs from some commonly used descriptors like MW, NNHb-D, NHb-A, Nrot, NSCA, MlogP, ClogP, 2D-BCUT and MACCs-vector. The development of a procedure for assessing the risk of revealing chemical structure when exchanging chemical descriptor information helps them to provide a list of guidelines for safe exchange of data.

  • Toxicity Data Informatics: Supporting a New Paradigm for Toxicity Prediction. Richard AM. et al. Toxicol Mech Methods. (5020)
  • KEYWORDS: DATABASES – DATA MINING – STRUCTURE-BASED PREDICTION

    Based on three recently public data iniciatives focused on the world of chemical toxicity information (ToxML, DSSTox and ACToR), this review attemps to answer several questions regarding detection of repositories with toxicity data, role of toxicologists and modelers to produce and evaluate toxicity data, data best organization to be explorate by data mining, list of chemicals with highest regulatory interest, and list of chemicals with richest foundation of toxicity data for anchoring new predictive methods.

  • Physiochemical drug properties associated with in vivo toxicological outcomes. Hughes JD. et al. Bioorg Med Chem Lett. (5038)
    Involved Partner: PFIZER
  • KEYWORDS: MOLECULAR DESCRIPTORS – PHARMACOLOGY

    Difficulties in developing predictive molecular models for toxicity outcomes are explained by the diversity of mechanisms (classification according to the causative molecular features or activities: 1) the primary pharmacology or mechanism of action of the molecule under study, 2) the secondary pharmacology of the molecule, 3) the presence of a well-defined structural fragment or toxicophore in the molecule, and 4) the overall physicochemical properties of the molecule) that give rise to toxic outcomes. This article evidences, based on a data set consisitng of animal in vivo toleration studies on 245 preclinical Pfizer compounds, that there is a relationship between promiscuity and attrition, though different physicochemical properties trends, particularly, they find an increased likelihood of toxic events for less polar, more lipophilic compounds.

  • Toxmatch – A Chemical Classification and Activity Prediction Tool based on Similarity Measures. Gallegos-Saliner A. et al. Regul Toxicol Pharmacol. (3294)
  • KEYWORDS: SOFTWARE – STRUCTURE-BASED PREDICTION

    Toxmatch, a recent new software tool to encode a number of similarity indices to help in the systematic development of chemical groupings, including endpoint specific groupings and read-across, and the comparison of model training and test sets. Toxmatch provides means to compare a chemical or set of chemicals to a toxicity dataset through the use of similarity indices.

  • Preclinical assessment of cardiac toxicity. Kettenhofen R. et al. Drug Discov Today. (5002)
  • KEYWORDS: CARDIOTOXICITY – RISK ASSESSMENT

    The exact prediction of the clinical behavior of drugs represents one of the most difficult duties in preclinical drug development, here in this paper they evidence that embryonic stem (ES) cell-derived cardiomyocytes provide a highly relevant and robust system to detect accurately the cardiotoxic potential of compounds. Taken advantage of the fact that ES cell-derived cardiomyocytes can be stored as pre-seeded multiwell plates and pre-seeded coverslips, the use of such a system for medium to high throughput screening at early stages of drug development is favorable and may be regarded as a prototype to realize toxicological modelling of substances to prevent misrouted development, especially at stages preceding clinical trials.

  • Toxicological Relationships Between Proteins Obtained from Protein Target Predictions of Large Toxicity Databases. Nigsch F. et al. Toxicol Appl Pharmacol. (3274)
  • KEYWORDS: DATABASES – PHARMACOLOGY

    Performance of a computational multiclass model using the Winnow algorithm based on a dataset of protein targets derived from the MDL Drug Data Report to predict protein targets of drug-like and toxic molecules, provides the identification of clusters of proteins related by their toxicological profiles, as well as toxicities that are related.

  • The role of ABC transporters in drug absorption, distribution, metabolism, excretion and toxicity (ADME–Tox). Szakács G. et al. Drug Discov Today. (5000)
  • KEYWORDS: ADME – STRUCTURE-BASED PREDICTION – TRANSPORTERS

    Description of the relevant human ABC drug transporters (see Table 1), list of pharmacologically relevant substances interacting with MDR1, MRP1 and ABCG2 (see Table 2) and review of the models and assay systems (In vivo: Cells-based assays (Cytotoxicity/chemosensitivity assay/drug resistance, Cellular accumulation, and Transcellular (‘vectorial’) transport); Membrane/protein-based assays (ATPase activity measurement); Drug binding photoaffinity labeling; and Vesicular transport. In vivo: Pgp–KO animals, Abcg2–KO mice and MRP1-3–KO animals. And Clinical: Improving oral bioavailability and CNS penetration, Individualized medicine—role of MDR–ABC transporter polymorphisms) that can be applied for the analysis of their expected drug interactions are provided in order to stand out factors limiting in vitroin vivo-clinical extrapolation of the impact of ABC transporters on ADMET.

  • Metabolism and Toxicity of Drugs. Two Decades of Progress in Industrial Drug Metabolism. Baillie TA. Chem Res Toxicol. (5048)
  • KEYWORDS: ADME – METABOLISM – METABONOMICS – PHARMACOKINETICS

    Summary of notable advances, during the last two decades, in areas such as Molecular Biology, Genetics, and Bioanalytical chemistry related to Drug Metabolism focuses on the critical nature of key partnerships between Drug Metabolism, Medicinal Chemistry, and Safety Assessment groups in attempting to advance drug candidates with a low potential for causing adverse events in humans. The authors bet for a maturation of in silico modeling ADME predictions, an appreciation of the functional consequences of genetic polymorphisms in drug transporters, developments in mass spectrometry and allied techniques to impact Drug Metabolism in the future.

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

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

  • Understanding Genetic Toxicity Through Data Mining: The Process of Building Knowledge by Integrating Multiple Genetic Toxicity Databases. Yang C. et al. Toxicol Mech Methods. (5010)
  • KEYWORDS: DATABASES – DATA MINING – GENOTOXICITY – STRUCTURE-BASED PREDICTION

    A worthy example of multiple genetic toxicity databases integration, both public (FDA data from approved new drugs applications, food contact notifications, recognized as safe food ingredients and chemicals from the NTP and CCRIS databases) and private (from private industry according to ToxML criteria) data included. This paper demonstrates, by application of data mining/profiling methods, the usefulness of representing a chemical by its structural features and the use of these features to profile a battery of test rather than relying on a single toxicity test of a given chemical. Across the genetic toxicity endpoints, Table 4 shows a selection of influential features of chemicals in the integrated database, meanwhile Table 7 shows the interdependency of some features and subtance use types.

  • The hERG K+ channel: target and antitarget strategies in drug development. Raschi E. et al. Pharmacol Res. (5011)
  • KEYWORDS: CARDIOTOXICITY – hERG – LQT

    A review to outline hERG K+ channel localization and functions in different tissues (see Table 1), and discusion of the possible implications for drug development as antitarget or target, since this channel is of interest for both basic researchers and clinicians because its blockade by drugs can lead to QT prolongation, which is a risk factor for torsades de pointes, a potentially life-threatening arrhythmia.

  • hERG Classification Model Based on a Combination of Support Vector Machine Method and GRIND Descriptors. Li Q. et al. Mol Pharm. (5012)
  • KEYWORDS: CARDIOTOXICITY – hERG – MOLECULAR DESCRIPTORS – STRUCTURE-BASED PREDICTION

    Based on 495 compounds with hERG information collected from the literature, they report the combination of the GRIND descriptors with a support vector machine (SVM) method to discriminate between hERG blockers from non-blockers. Assuming some method limitations, the performance of their model shows an improvement between 10% and 20% in the prediction of blockers compared to other methods available at the same time.

2007

  • The application of discovery toxicology and pathology towards the design of safer pharmaceutical lead candidates. Kramer JA. et al. Nat Rev Drug Discov. (5008)
  • KEYWORDS: CARDIOTOXICITY – CYP450 – DRUG-DRUG INTERACTION – DRUG SAFETY – hERG – P-gp – PHARMACOLOGY

    Discussion about how the early application of preclinical safety assessment can identify predictable safety issues earlier in the testing paradigm. They review about prospective in vitro toxicology assays, including genetic toxicology, safety pharmacology (tipically limited to an assessment of hERG binding and /or hERG blockade), drug-drug interactions (cythocrome P450 inhibition and induction or P-glycoprotein interaction assays), and metabolite-mediated toxicity

  • Analysis of Pharmacology Data and the Prediction of Adverse Drug Reactions and Off-Target Effects from Chemical Structure. Bender A. et al. ChemMedChem. (5016)
  • KEYWORDS: DATABASES – DRUG SAFETY – PHARMACOLOGY

    An exploration of the Preclinical Safety Pharmacology (PSP) chemical space (Novartis in-house data and WOMBAT database) to provide a predictive model for preclinical profiling targets. Due to PSP relevance for the prediction of adverse drug reactions, they present a predictive model for adverse drug reactions, emplyoing the World Drug Index (WDI), and propose the combination of both models as methodological novel efforts to accelerate drug discovery and decrease late stage attrition in drug discovery projects.

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

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

  • Toxicological and clinical computational analysis by the Informatics and Computational Safety Analysis Staff of the US FDA/CDER. Benz DR. et al. AATEX. (5166)
  • KEYWORDS: QSAR MODELING – REGULATORY GUIDELINES

    This article presents a table with the list of animal toxicological and human adverse clinical effects computational toxicology models used by the Informatics and Computational Safety Analysis Staff at US FDA/CDER categorized by endpoints and grouped as discrete/binary or non-discrete/continuous models.

  • Toxicological and clinical computational analysis and the US FDA/CDER. Benz RD. Expert Opin Drug Metab Toxicol. (5025)
  • KEYWORDS: DATABASES – DATA MINING – QSAR MODELING – STRUCTURE-BASED PREDICTION – TOXICOINFORMATICS

    This is a recommended article for those who are not familiar with computational toxicology with special focus on some of the terminology and basic principles of this field. The author reports on the progress that the FDA, Center for Drug Evaluation and Research made in compiling databases of toxicological and clinical data at that time, from which successful predictive toxicology models had been made. A short discussion about feasibility of replacing testing animals with predictions made with computers is also provided by this expert opinion article.

  • Idiosyncratic Toxicity: A Convergence of Risk Factors. Ulrich RGl. Annu Rev Med. (5044)
  • KEYWORDS: CYP450 – DRUG SAFETY – METABOLISM – PHARMACOLOGY

    An updated discussion about diverse specific risk factors identified that can put an individual at higher risk for an adverse drug reaction (see Figure 2 for a list of risk factors). They conclude that drug-related risk factors include metabolism, bioactivation and covalent binding, and the inhibition of key cell functions, meanwhile patient-related risk factors include underlying disease, age, gender, comedications, nutritional status, activation of the innate immune system, physical activity, and genetic predispositions.

  • Achieving confidence in mechanism for drug discovery and development. Pitluk Z. et al. Drug Discov Today. (5017)
  • KEYWORDS: DATA MINING – DRUG DISCOVERY

    Based on the fact that the drug development decisions mostly are made on determinations of cause and effect from experimental observations, they opened a discussion about difficulties to determine compound mechanisms from large-scale multi-omics technologies. Some promising iniciatives, regarding computational learning methods that identify from compound data the circuits and connections between drug-affected molecular constituents and physiological observables must be taken into account like network inference approaches.

  • Development, interpretation and temporal evaluation of a global QSAR of hERG electrophysiology screening data. Gavaghan CL. et al. J Comput Aided Mol Des. (5018)
  • KEYWORDS: hERG – MOLECULAR DESCRIPTORS – QSAR MODELING – STRUCTURE-BASED PREDICTION

    Based on a hierarchical PLS modelling approach where four different descriptor packages were considered to build a ‘global’ model of hERG K+ channel with 1312 compounds and their electrophysiology data, new hERG specific fragment-based descriptors were developed and identified as the most influential descriptor package in such approach. See Table 3 for a selection of fragments proposed as contributing to a molecules propensity to inhibit hERG (positive contribution generated from a priori knowledge, and negative contribution generated from LeadScope analysis).

  • Systems chemical biology. Oprea TI. et al. Nat Chem Biol. (5019)
  • KEYWORDS: DATABASES – SYSTEMS BIOLOGY

    With this commentary article, the authors provide a revision of recent initiatives to emphasize the critical need to develop cheminformatics tools that integrate chemical knowledge (see Box 1) with biological databases and simulation approaches (see Box 2).

2006

  • Future of Toxicology Predictive Toxicology: An Expanded View of “Chemical Toxicity”. Richard AM. et al. Chem. Res. Toxicol. (5058)
  • KEYWORDS: DATABASES – DATA MINING – QSAR MODELING – STRUCTURE-BASED

    New trends in predictive-toxicology were appreciated basically in two areas that were particularly promising: Toxicity Data Informatics (developed to facilitate data integration and enable relational exploration and mining of data across both historical and new areas of toxicological investigation) and Bioassay Profiling (devoted to large-scale high-throughput screening approaches that use chemicals as probes to broadly characterize biological response space, extending the concept of chemical “properties” to the biological activity domain). Figure 2 represents the multidimensional integration of activities towards an improved predicitive-toxicology scenario.

  • The Art of Data Mining the Minefields of Toxicity Databases to Link Chemistry to Biology. Yang C. et al. Curr Comput Aided Drug Des. (5057)
  • KEYWORDS: DATABASES – DATA MINING

    Discussion about data mining strategies to link chemistry and biology where structure integration and the use of relational database models with rigorous standards and controlled vocabularies to represent complex experimental toxicity data are prerequisites. The article updates about recent trends to optimize toxicity databases contents with standards that are in progress to provide databases (DSSTox, ToxML, PubChem) that can be mined and exploited.

  • Scientific Perspectives on Drug Transporters and Their Role in Drug Interactions. Zhang L. et al. Mol Pharm. (5049)
  • KEYWORDS: ADME – DRUG-DRUG INTERACTION – P-gp – REGULATORY GUIDELINES – TRANSPORTERS

    This commentary focuses on the potential role that drug transporters may play in drug-drug interactions and what information may be needed during drug development and new drug application (NDA) submissions to address potential drug interactions mediated by transporters (see Table 1 where ara listed the major human drug transporters). Based on P-gp transporter they discuss several proposals for evaluating drug transporter mediated interactions.

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

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

  • Toxicogenomic analysis methods for predictive toxicity. Maggioli J. et al. J Pharmacol Toxicol Methods. (1644)
  • KEYWORDS: DATA MINING – TOXICOGENOMICS

    As widely accepted that gene expression data can provide an early indication of toxicity because toxin-mediated changes in gene expression are often detectable before clinical chemistry, histopathology, or clinical observations suggest a toxic effect, this article reviews the process of predictive toxicogenomics (Data Preparation, Class Comparison, Class Prediction and Class Discovery, and Evaluation).

  • Chemical structure indexing of toxicity data on the Internet: Moving toward a flat world. Richard AM. et al. Curr Opin Drug Discov Devel. (5004)
  • KEYWORDS: DATABASES – DATA MINING – NLP

    Discussion of public initiatives to standardized chemical structure annotation of public toxicity databases (chemical content annotators, structure locator services, large structure/aggregator web sites, structure browsers, International Union of Pure and Applied Chemistry (IUPAC) and International Chemical Identifier (InChI) codes, toxicity data models and chemical/biological activity profilings). These all initiatives are playing a role in overcoming barriers to the integration of toxicity data, and are bringing researchers closer to the reality of a mineable chemical Semantic Web. The Distributed Structure-Searchable Toxicity database (DSSTox) is presented as a case study.

2005

2004

2003