OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation.Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH-relevant endpoints: ToxPredict which predicts and reports on toxicities for endpoints for an input chemical structure, and ToxCreate which builds and validates a predictive toxicity model based on an input toxicology dataset. Because of the extensible nature of the standardised Framework design, barriers of interoperability between applications and content are removed, as the user may combine data, models and validation from multiple sources in a dependable and time-effective way.

lazar (lazy structure–activity relationships) is a modular framework for predictive toxicology. Similar to the read across procedure in toxicological risk assessment, lazar creates local QSAR (quantitative structure–activity relationship) models for each compound to be predicted. Model developers can choose between a large variety of algorithms for descriptor calculation and selection, chemical similarity indices, and model building. This paper presents a high level description of the lazar framework and discusses the performance of example classification and regression models.

The aim of the SEURAT‐1 (Safety Evaluation Ultimately Replacing Animal Testing‐1) research cluster, comprised of seven EU FP7 Health projects co‐financed by Cosmetics Europe, is to generate a proof‐of‐concept to show how the latest technologies, systems toxicology and toxicogenomics can be combined to deliver a test replacement for repeated dose systemic toxicity testing on animals. The SEURAT‐1 strategy is to adopt a mode‐of‐action framework to describe repeated dose toxicity, combining in vitro and in silico methods to derive predictions of in vivo toxicity responses. ToxBank is the cross‐cluster infrastructure project whose activities include the development of a data warehouse to provide a web‐accessible shared repository of research data and protocols, a physical compounds repository, reference or “gold compounds” for use across the cluster (available via wiki.toxbank.net), and a reference resource for biomaterials. Core technologies used in the data warehouse include the ISA‐Tab universal data exchange format, REpresentational State Transfer (REST) web services, the W3C Resource Description Framework (RDF) and the OpenTox standards. We describe the design of the data warehouse based on cluster requirements, the implementation based on open standards, and finally the underlying concepts and initial results of a data analysis utilizing public data related to the gold compounds.

The lazar framework for read across predictions was expanded for the prediction of nanoparticle toxicities, and a new methodology for calculating nanoparticle descriptors from core and coating structures was implemented. Nano-lazar provides a flexible and reproducible framework for downloading data and ontologies from the open eNanoMapper infrastructure, developing and validating nanoparticle read across models, open-source code and a free graphical interface for nanoparticle read-across predictions. In this study we compare different nanoparticle descriptor sets and local regression algorithms. Sixty independent crossvalidation experiments were performed for the Net Cell Association endpoint of the Protein Corona dataset. The best RMSE and r2 results originated from models with protein corona descriptors and the weighted random forest algorithm, but their 95% prediction interval is significantly less accurate than for models with simpler descriptor sets (measured and calculated nanoparticle properties). The most accurate prediction intervals were obtained with measured nanoparticle properties (no statistical significant difference (p < 0.05) of RMSE and r2 values compared to protein corona descriptors). Calculated descriptors are interesting for cheap and fast high-throughput screening purposes. RMSE and prediction intervals of random forest models are comparable to protein corona models, but r2 values are significantly lower.

BackgroundThe OpenTox Framework, developed by the partners in the OpenTox project (http://www.opentox.org), aims at providing a unified access to toxicity data, predictive models and validation procedures. Interoperability of resources is achieved using a common information model, based on the OpenTox ontologies, describing predictive algorithms, models and toxicity data. As toxicological data may come from different, heterogeneous sources, a deployed ontology, unifying the terminology and the resources, is critical for the rational and reliable organization of the data, and its automatic processing.ResultsThe following related ontologies have been developed for OpenTox: 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.OpenTox components are made available through standardized REST web services, where every compound, data set, and predictive method has a unique resolvable address (URI), used to retrieve its Resource Description Framework (RDF) representation, or to initiate the associated calculations and generate new RDF-based resources.The services support the integration of toxicity and chemical data from various sources, the generation and validation of computer models for toxic effects, seamless integration of new algorithms and scientifically sound validation routines and provide a flexible framework, which allows building arbitrary number of applications, tailored to solving different problems by end users (e.g. toxicologists).AvailabilityThe OpenTox toxicological ontology projects may be accessed via the OpenTox ontology development page http://www.opentox.org/dev/ontology; the OpenTox ontology is available as OWL at http://opentox.org/api/1 1/opentox.owl, the ToxML - OWL conversion utility is an open source resource available at http://ambit.svn.sourceforge.net/viewvc/ambit/branches/toxml-utils/

Background The consent management is an essential component for supporting the implementation of consents and withdrawals and thus, the realisation of patient’s rights. In MIRACUM, one of the four consortia of the Medical Informatics Initiative (MII), ten university hospitals intend to integrate the generic Informed Consent Service® (gICS) in their Data Integration Center (DIC). To provide a tool that supports the local workflows of the MIRACUM sites, the gICS should be improved. Methods We used three standardised questionnaires with 46 questions to elicit requirements from the ten sites. Each site answered the questions from the current and the desired future perspective. This made it possible to understand the individual processes at each site and it was possible to identify features and improvements that were generally necessary. Results The results of the survey were classified according to their impact on the gICS. Feature requests of new functionalities, improvements of already implemented functionalities and conceptual support for implementing processes were identified. This is the basis for an improved gICS release to support the ten sites’ individual consent management processes. Conclusions A release plan for the feature requests and improvements was coordinated with all sites. All sites have confirmed that the implementation of these features and enhancements will support their software-based consent management processes.

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Modular infrastructure for data storage, sharing and searching, based on open standards and semantic web technologies, minimum information standards and established security solutions.  Development of ontologies for the categorisation and characterisation of engineered nanomaterials (ENMs) in collaboration with other projects.  Creation of new computational models in nanomaterials safety through the implementation of interfaces for toxicity modelling and prediction algorithms which may process all data made available through eNanoMapper (e.g. using algorithms available from the OpenTox project or statistical/data mining software).  Meta analysis of nano-bio interactions supporting "safe-by-design" ENMs development by pursuing a Linked Data approach which integrates data and metadata originating from diverse sources within nanoscience, chemistry, biology and toxicology.  Creation of tools for the exchange, quality assurance and reporting of research protocols and data for regulatory purposes.  Creation of a community framework for interdisciplinary collaboration. Not to generate a large collection of data, but rather a collection of standards and tools to support the basis of a "smart" database with a wide variety of functions and application areas.