OPENTOX - OpenTox - An Open Source Predictive Toxicology Framework 

Time period:
2008-09-01 - 2011-08-31
Collaborative project

The goal of the OpenTox project is to develop a predictive toxicology framework with a unified access to toxicological data, (Q)SAR models and supporting information. It will provide tools for the integration of data from various sources (public and confidential), for the generation and validation of (Q)SAR models, libraries for the development and integration of new (Q)SAR algorithms, and validation routines. OpenTox will attract toxicological experts without (Q)SAR expertise as well as model and algorithm developers. It will move beyond existing attempts to solve individual research issues, by providing a flexible and user friendly framework that integrates existing solutions and new developments. OpenTox will be relevant for REACH as it gives risk assessors simple access to experimental data, (Q)SAR models and toxicological information that adheres to European and international regulatory requirements. OpenTox will be published as an open source project to allow a critical evaluation of its algorithms, to promote dissemination, and to attract external developers. Facilities for the inclusion of confidential in-house data and for accessing commercial prediction systems will be included. OpenTox will contain high-quality data and (Q)SAR models for chronic, genotoxic and carcinogenic effects. These are the endpoints with the greatest potential to reduce animal testing. The impact of OpenTox will however go beyond REACH and long-term effects, because it will be straightforward to create models for other endpoints (e.g,. sensitisation, liver-toxicity, cardio-toxicity, ecotoxicity). The proposed framework will support the development of new (Q)SAR models and algorithms by automating routine tasks, providing a testing and validation environment and allowing the easy addition of new data. For this reason we expect, that OpenTox will lead to (Q)SAR models for further toxic endpoints and generally improve the acceptance and reliability of (Q)SAR models.

United Kingdom
Russian Federation
Last updated on 2014-06-17 at 16:13