Abstract
The pharmaceutical industry would benefit from the collaboration with academic groups in the development of predictive safety models using the newest computational technologies. However, this collaboration is sometimes hampered by the handling of confidential proprietary information and different working practices in both environments. In this manuscript, we propose a strategy for facilitating this collaboration, based on the use of modeling frameworks developed for facilitating the use of sensitive data, as well as the development, interchange, hosting, and use of predictive models in production. The strategy is illustrated with a real example in which we used Flame, an open-source modeling framework developed in our group, for the development of an in silico eye irritation model. The model was based on bibliographic data, refined during the company–academic group collaboration, and enriched with the incorporation of confidential data, yielding a useful model that was validated experimentally.
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Acknowledgments
This work has received funding from the eTRANSAFE project (Grant Agreement No. 777365), developed under the Innovative Medicines Initiative Joint Undertaking (IMI2), resources of which are composed of a financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contributions. The authors of this article are also involved the H2020 EU-ToxRisk project (no. 681002) and FAIRplus (no. 802750). The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), funded by ISCIII and FEDER (PT17/0009/0014). The DCEXS is a ‘Unidad de Excelencia María de Maeztu’, funded by the AEI (CEX2018-000782-M). The GRIB is also supported by the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR), Generalitat de Catalunya (2017 SGR 00519).
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Pastor, M., Sanz, F., Bringezu, F. (2022). Development of In Silico Methods for Toxicity Prediction in Collaboration Between Academia and the Pharmaceutical Industry. In: Benfenati, E. (eds) In Silico Methods for Predicting Drug Toxicity. Methods in Molecular Biology, vol 2425. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1960-5_5
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DOI: https://doi.org/10.1007/978-1-0716-1960-5_5
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