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Background: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silica models built using existing data facilitate rapid acute tox- icity predictions without using animals. Objkctivks: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organ- ized an international collaboration to develop in silica models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50 ≤ 50 mg/kg)], and nontoxic chemicals (LD50 > 2,000 mg/kg). Mkthods: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. Results: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in viva results. Discussion: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in viva rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemica

BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP’s list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCastTM metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCastTM/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy ass

Contains fulltext : 179528.pdf (publisher's version ) (Closed access), This chapter explores the economic, political, and organisational effects of external agentification in the municipal waste service in four countries: Poland, the Netherlands, Norway and Spain (Catalonia). Using Van Thiel´s typology of agentification, it turns out that the four countries have all followed trajectories of external agentification, but at a different pace, in different periods, and in varying degrees. Furthermore, an analysis of secondary data indicates mixed effects of external agentification. Public law-based inter-municipal companies (type 2) and private law based municipal-owned limited companies (type 3) seem to have some positive economic effects, although transaction costs may increase. The political effects are clearly negative in the sense that accountability becomes more problematic. Due to the lack of data, the findings on organisational effects are inconclusive.

The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu . © 2011 The Author(s).

In a volume that deals with convergence / divergence in the institutional development of the public services sector in some European countries, in a comparative perspective, chapter 4 traces changes in an area of policy (pre-school child care) that displays strong similarities across four countries (UK, France, Germany and Italy). There are similar approaches in child protection involving the courts and key roles for public agencies; a low level of child care provision in the early years with strong independent sectors; strong emphasis on pre-school education, with the main institutional provision linked with the regular education system, except in Germany. Dissimilarities arise from differences in local government system and/or the relationship between central and local government, such as relatively uniform system in France and the UK, variations between Lænder in Germany, and greater autonomy at the regional and local levels as a source of diversity in Italy. The strong central policy drives towards pre-school education and child care to facilitate parental labour force participation is causing some convergence. In this respect, developments in Italy, the UK and Germany may be reducing differences between their systems and that of France.

In a book that provides a comprehensive analysis of the provision of public services in some European countries, this chapter reconstructs key developments in long-term care in England, France, Germany and Italy since the beginning of the 1990s, with a view to identifying changes in the horizontal and vertical governance of services, that is, the role of public, commercial and non-profit organizations in service provision and the distribution of competencies among the different tiers of government. In line with the overall focus of the volume, special attention is paid to the changing role of local government regarding Long-term care, that is particularly suited for such a focus. Whereas in other fields of social policy such as pensions, health care or labour market policy, central government stepped in at an early stage, the responsibility for providing and financing services for the frail elderly was largely left with local governments in most OECD countries, until recently major reforms and changes have taken place.

The estimation of accuracy and applicability of QSAR and QSPR models for biological and physicochemical properties represents a critical problem. The developed parameter of “distance to model” (DM) is defined as a metric of similarity between the training and test set compounds that have been subjected to QSAR/QSPR modeling. In our previous work, we demonstrated the utility and optimal performance of DM metrics that have been based on the standard deviation within an ensemble of QSAR models. The current study applies such analysis to 30 QSAR models for the Ames mutagenicity data set that were previously reported within the 2009 QSAR challenge. We demonstrate that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs. The presented approach identifies 30-60% of compounds having an accuracy of prediction similar to the interlaboratory accuracy of the Ames test, which is estimated to be 90%. Thus, the in silico predictions can be used to halve the cost of experimental measurements by providing a similar prediction accuracy. The developed model has been made publicly available at http://ochem.eu/models/1

C-Galactosides have been used as scaffolds to design a library of non-hydrolysable inhibitors of cholera toxin (CT). Test elements from the library were synthesized and found to inhibit CT binding to an asialofetuin-coated SPR chip with micromolar affinity. Preliminary results are reported. © 2007 Elsevier Ltd. All rights reserved.

Contains fulltext : 46080.pdf (publisher's version ) (Open Access)

Two novel galactose-derived bicyclic acetamides were synthesized. The conformational behavior of these cyclic acetamides in aqueous solution was fully investigated. The NMR spectroscopic data suggests that the pyran ring interconverts between two (4C1, 1S3) or three (4C1, 1S3 and 1C4) conformations and molecular modelling studies allowed the structures' geometries to be defined. Three different force fields were used (Amber*, MM3* and OPLSAA) and fitting procedures were employed to reproduce the experimental 3JH-H coupling constants and NOE contacts. Evaluation of the results established which of these three well-known force fields were the most reliable in reproducing the experimental observations and should therefore be used for analogous design studies. © Wiley-VCH Verlag GmbH & Co. KGaA, 2006.

Contains fulltext : 47120.pdf (publisher's version ) (Open Access)

© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, WeinheimIn this work, we report QSPR modeling of the free energy ΔG of 1 : 1 hydrogen bond complexes of different H-bond acceptors and donors. The modeling was performed on a large and structurally diverse set of 3373 complexes featuring a single hydrogen bond, for which ΔG was measured at 298 K in CCl4. The models were prepared using Support Vector Machine and Multiple Linear Regression, with ISIDA fragment descriptors. The marked atoms strategy was applied at fragmentation stage, in order to capture the location of H-bond donor and acceptor centers. Different strategies of model validation have been suggested, including the targeted omission of individual H-bond acceptors and donors from the training set, in order to check whether the predictive ability of the model is not limited to the interpolation of H-bond strength between two already encountered partners. Successfully cross-validating individual models were combined into a consensus model, and challenged to predict external test sets of 629 and 12 complexes, in which donor and acceptor formed single and cooperative H-bonds, respectively. In all cases, SVM models outperform MLR. The SVM consensus model performs well both in 3-fold cross-validation (RMSE=1.50 kJ/mol), and on the external test sets containing complexes with single (RMSE=3.20 kJ/mol) and cooperative H-bonds (RMSE=1.63 kJ/mol).

© 2015 American Chemical Society. A generic chemical transformation may often be achieved under various synthetic conditions. However, for any specific reagents, only one or a few among the reported synthetic protocols may be successful. For example, Michael β-addition reactions may proceed under different choices of solvent (e.g., hydrophobic, aprotic polar, protic) and catalyst (e.g., Brønsted acid, Lewis acid, Lewis base, etc.). Chemoinformatics methods could be efficiently used to establish a relationship between the reagent structures and the required reaction conditions, which would allow synthetic chemists to waste less time and resources in trying out various protocols in search for the appropriate one. In order to address this problem, a number of 2-classes classification models have been built on a set of 198 Michael reactions retrieved from literature. Trained models discriminate between processes that are compatible and respectively processes not feasible under a specific reaction condition option (feasible or not with a Lewis acid catalyst, feasible or not in hydrophobic solvent, etc.). Eight distinct models were built to decide the compatibility of a Michael addition process with each considered reaction condition option, while a ninth model was aimed to predict whether the assumed Michael addition is feasible at all. Different machine-learning methods (Support Vector Machine, Naive Bayes, and Random Forest) in combination with different types of descriptors (ISIDA fragments issued from Condensed Graphs of Reactions, MOLMAP, Electronic Effect Descriptors, and Chemistry Development Kit computed descriptors) have been used. Models have good predictive performance in 3-fold cross-validation done three times: balanced accuracy varies from 0.7 to 1. Developed models are available for the users at http://infochim.u-strasbg.fr/webserv/VSEngine.html. Eventually, these were challenged to predict feasibility conditions for ∼50 novel Michael reactions from the eN

© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, WeinheimIn this work, we report QSPR modeling of the free energy ΔG of 1 : 1 hydrogen bond complexes of different H-bond acceptors and donors. The modeling was performed on a large and structurally diverse set of 3373 complexes featuring a single hydrogen bond, for which ΔG was measured at 298 K in CCl4. The models were prepared using Support Vector Machine and Multiple Linear Regression, with ISIDA fragment descriptors. The marked atoms strategy was applied at fragmentation stage, in order to capture the location of H-bond donor and acceptor centers. Different strategies of model validation have been suggested, including the targeted omission of individual H-bond acceptors and donors from the training set, in order to check whether the predictive ability of the model is not limited to the interpolation of H-bond strength between two already encountered partners. Successfully cross-validating individual models were combined into a consensus model, and challenged to predict external test sets of 629 and 12 complexes, in which donor and acceptor formed single and cooperative H-bonds, respectively. In all cases, SVM models outperform MLR. The SVM consensus model performs well both in 3-fold cross-validation (RMSE=1.50 kJ/mol), and on the external test sets containing complexes with single (RMSE=3.20 kJ/mol) and cooperative H-bonds (RMSE=1.63 kJ/mol).

© 2015 American Chemical Society. A generic chemical transformation may often be achieved under various synthetic conditions. However, for any specific reagents, only one or a few among the reported synthetic protocols may be successful. For example, Michael β-addition reactions may proceed under different choices of solvent (e.g., hydrophobic, aprotic polar, protic) and catalyst (e.g., Brønsted acid, Lewis acid, Lewis base, etc.). Chemoinformatics methods could be efficiently used to establish a relationship between the reagent structures and the required reaction conditions, which would allow synthetic chemists to waste less time and resources in trying out various protocols in search for the appropriate one. In order to address this problem, a number of 2-classes classification models have been built on a set of 198 Michael reactions retrieved from literature. Trained models discriminate between processes that are compatible and respectively processes not feasible under a specific reaction condition option (feasible or not with a Lewis acid catalyst, feasible or not in hydrophobic solvent, etc.). Eight distinct models were built to decide the compatibility of a Michael addition process with each considered reaction condition option, while a ninth model was aimed to predict whether the assumed Michael addition is feasible at all. Different machine-learning methods (Support Vector Machine, Naive Bayes, and Random Forest) in combination with different types of descriptors (ISIDA fragments issued from Condensed Graphs of Reactions, MOLMAP, Electronic Effect Descriptors, and Chemistry Development Kit computed descriptors) have been used. Models have good predictive performance in 3-fold cross-validation done three times: balanced accuracy varies from 0.7 to 1. Developed models are available for the users at http://infochim.u-strasbg.fr/webserv/VSEngine.html. Eventually, these were challenged to predict feasibility conditions for ∼50 novel Michael reactions from the eN

© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim This paper presents the effort of collecting and curating a data set of 15461 molecules tested against the malaria parasite, with robust activity and mode of action annotations. The set is compiled from in-house experimental data and the public ChEMBL database subsets. We illustrate the usefulness of the dataset by building QSAR models for antimalarial activity and QSPR models for modes of actions, as well as by the analysis of the chemical space with the Generative Topographic Mapping method. The GTM models perform well in prediction of both activity and mode of actions, on par with the classical SVM methods. The visualization of obtained maps helps to understand the distribution of molecules corresponding to different modes of action: molecules with similar targets are located close to each other on the map. Therefore, this analysis may suggest new modes of action for non-annotated or even annotated compounds. In perspective, this can be used as a tool for prediction of both antimalarial activity and target for novel, untested compounds.

© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim Generative Topographic Mapping (GTM) approach was successfully used to visualize, analyze and model the equilibrium constants (KT) of tautomeric transformations as a function of both structure and experimental conditions. The modeling set contained 695 entries corresponding to 350 unique transformations of 10 tautomeric types, for which KT values were measured in different solvents and at different temperatures. Two types of GTM-based classification models were trained: first, a “structural” approach focused on separating tautomeric classes, irrespective of reaction conditions, then a “general” approach accounting for both structure and conditions. In both cases, the cross-validated Balanced Accuracy was close to 1 and the clusters, assembling equilibria of particular classes, were well separated in 2-dimentional GTM latent space. Data points corresponding to similar transformations measured under different experimental conditions, are well separated on the maps. Additionally, GTM-driven regression models were found to have their predictive performance dependent on different scenarios of the selection of local fragment descriptors involving special marked atoms (proton donors or acceptors). The application of local descriptors significantly improves the model performance in 5-fold cross-validation: RMSE=0.63 and 0.82 logKT units with and without local descriptors, respectively. This trend was as well observed for SVR calculations, performed for the comparison purposes.

© 2016, Springer Science+Business Media New York.The generative topographic mapping (GTM) approach has been used both to build predictive models linking chemical structure of molecules and their ability to bind some membrane transport proteins (transporters) and to visualize a chemical space of transporters’ binders on two-dimensional maps. For this purpose, experimental data on 2958 molecules active against up to 11 transporters have been used. It has been shown that GTM-based classification (active/inactive) models display reasonable predictive performance, comparable with that of such popular machine-learning methods as Random Forest, SVM, or k-NN. Moreover, GTM offers its own models applicability domain definition which may significantly improve the models performance. GTM maps themselves represent an interesting tool of the chemical space analysis of the transporters’ ligands. Thus, with the help of class landscapes, they identify distinct zones populated by active or inactive molecules with respect to a given transporter. As demonstrated in this paper, the superposition of class landscapes describing different activities delineates the areas mostly populated by the compounds of desired pharmacological profile.

© 2016 Wiley-VCH Verlag GmbH & Co. KGaA.Halogen bonding (XB) strength assesses the ability of an electron-enriched group to be involved in complexes with polarizable electrophilic halogenated or diatomic halogen molecules. Here, we report QSPR models of XB of particular relevance for an efficient screening of large sets of compounds. The basicity is described by pKBI2, the decimal logarithm of the experimental 1 : 1 (B :I2) complexation constant K of organic compounds (B) with diiodine (I2) as a reference halogen-bond donor in alkanes at 298K. Modeling involved ISIDA fragment descriptors, using SVM and MLR methods on a set of 598 organic compounds. Developed models were then challenged to make predictions for an external test set of 11 polyfunctional compounds for which unambiguous assignment of the measured effective complexation constant to specific groups out of the putative acceptor sites is not granted. At this stage, developed models were used to predict pKBI2 of all putative acceptor sites, followed by an estimation of the predicted effective complexation constant using the ChemEqui program. The best consensus models perform well both in cross-validation (root mean squared error RMSE=0.39-0.47logKBI2 units) and external predictions (RMSE=0.49). The SVM models are implemented on our website (http://infochim.u-strasbg.fr/webserv/VSEngine.html) together with the estimation of their applicability domain and an automatic detection of potential halogen-bond acceptor atoms.

© 2016 American Chemical Society.This chapter describes Generative Topographic Mapping (GTM) -A dimensionality reduction method which can be used both to data visualization, clustering and modeling. GTM is a probabilistic extension of Kohonen maps. Its probabilistic nature can be exploited in order to build regression or classification models, to define their applicability domain, to predict activity profiles of compounds, to compare large datasets, to screen for compounds of interest, and even to identify new molecules possessing desirable properties. Thus, GTM can be seen as a sort of a multi-purpose Swiss knife, each of its blades being able to shape an answer to a specific chemoinformatics question, based on a unique map.