Barry Hardy, Nicki Douglas, Christoph Helma, Michael Rautenberg, Nina Jeliazkova, Vedrin Jeliazkov, Ivelina Nikolova, Romualdo Benigni, Olga Tcheremenskaia, Stefan Kramer, Tobias Girschick, Fabian Buchwald, Jörg Wicker, Andreas Karwath, Martin Gütlein, Andreas Maunz, Haralambos Sarimveis, Georgia Melagraki, Antreas Afantitis, Pantelis Sopasakis, David Gallagher, Vladimir Poroikov, Dmitry Filimonov, Alexey Zakharov, Alexey Langunin, Tatyana Gloriozova, Sergey Novikov, Natalia Skvortsova, Dmitri Druzhilovsky, Sunil Chawla, Indira Gosh, Surajit Ray, Hitesh Patel, and Sylvia Escher (2010)
Collaborative Development of Predictive Toxicology Applications
Journal of Cheminformatics, 2(7).
This paper provides a perspective on the growing significance of community and collaboration approaches in predictive toxicology. Key challenges to be overcome are both technical and cultural and involve progressing issues related to cross-organisational, enterprise and application interoperability, knowledge management and developing a culture and framework supporting a community-based platform and collaborative projects emerging from the community foundation. The EC-funded FP7 project ``OpenTox'' (www.opentox.org) is developing an Open Source-based predictive toxicology framework that provides a unified access to toxicological data and (Quantitative) Structure-Activity Relationship i.e., (Q)SAR models. OpenTox provides tools for the integration of data, for the generation and validation of (Q)SAR models for toxic effects, libraries for the development and integration of (Q)SAR algorithms, and scientifically sound validation routines. OpenTox will support the development of applications for non-computational specialists in addition to interfaces for risk assessors, toxicological experts and model and algorithm developers. OpenTox is relevant for the implementation of REACH as it allows risk assessors to access experimental data, (Q)SAR models and toxicological information from a unified interface that adheres to European and international regulatory requirements including OECD Guidelines for validation and reporting. The OpenTox framework is being populated initially with data and models for chronic, genotoxic and carcinogenic effects. These are the endpoints where computational methods promise the greatest potential reduction in animal testing required under REACH. Initial research has defined the essential components of the framework architecture, approach to data access, schema and management, use of controlled vocabularies and ontologies, web service and communications protocols, and selection and integration of algorithms for predictive modelling. The initial results of this research are discussed. OpenTox has been initiated as a collaborative project involving a combination of different enterprise, university and government research groups to design and build the initial framework. Additionally numerous organizations with industry, regulatory or expert interests are being included from the start in providing guidance and direction. The goal is to expand OpenTox as a community project enabling additional expert and user participants to be involved in developments in as timely a manner as possible. To this end, our mission is to carry out developments in an open and transparent manner from the early days of the project, and to open up discussions and development to the global community at large, who may either participate in developments or provide user perspectives. Cooperation on data standards, data integration, ontologies, integration of algorithm predictions from different methods, and testing and validation all have significant collaboration opportunities and benefits for the community. Additionally, practices for building effective collaborations from the OpenTox community approach are discussed.