Overcoming the challenges to reducing animal experimentation through an Open Source community-based development of in silico methods
In a recent study by the European Chemical Bureau, it has been estimated that the new EU chemical legislation REACH will require 3.9 million additional test animals, if no alternative methods are accepted. The same study shows that it is possible to reduce the number of test animals significantly by utilizing existing experimental data in conjunction with (Quantitative) Structure Activity Relationship ((Q)SAR) models. Chronic and reproductive toxicity, in vivo mutagenicity and carcinogenicity are the endpoints that will require the largest number of test animals within REACH, because no alternative in vitro assays are currently available.
Recent (Q)SAR developments allow a much more accurate prediction of complex toxicological endpoints than a few years ago. This progress has been caused by (i) the development of improved (Q)SAR algorithms and (ii) by the availability of larger and better curated public databases.
The routine application of these new generation models is however still rare, because
l Toxicity data has been collected in a variety of different databases.
l These databases use different formats, that are frequently not compatible with (Q)SAR programs
l Many databases lack important information for (Q)SAR modeling (e.g. chemical structures)
l It is hard to integrate confidential in-house data with public data for model building and validation
l (Q)SAR models have been published in a variety of different formats (ranging from simple regression based equations to full-fledged computer programs)
l There is no straightforward integration of predictions from various programs
l There is no commonly accepted framework for the validation of (Q)SAR predictions and many (Q)SAR tools provide limited support for reliable validation procedures
l The application, interpretation, and development of (Q)SAR models is still difficult for most toxicological experts. It requires a considerable amount of statistical, chemoinformatics and computer science expertise and the procedures are labor intensive and prone to human errors.
The overall objective of the FP7-funded OpenTox is to develop a framework, that provides a unified access to toxicity data, (Q)SAR models, procedures supporting validation and additional information that helps with the interpretation of (Q)SAR predictions. OpenTox will be accessible at three levels:
l A simple and intuitive interface for toxicological experts, that provides unified access to (Q)SAR predictions, toxicological data, (Q)SAR models and supporting information
l An expert interface for the streamlined development and validation of new (Q)SAR models
l An application programming interface (API) for the development, integration and validation of new (Q)SAR algorithms
The OpenTox framework is being developed as an Open Source project to optimize the dissemination and impact, to allow the inspection and review of algorithms and to attract external contributors. Our approach is to closely collaborate with related projects (e.g. OECD QSAR toolbox, CADASTER, Leadscope’s ToxML development), industry users, developers and regulatory authorities to agree on common standards and to avoid duplicated and redundant work.
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