Summary
The concern to protect human health and the environment has prompted significant changes in EU regulation on chemical
substances. The European Chemicals Agency (ECHA) plays the role in implementing the Registration, Evaluation,
Authorization and Restriction of Chemicals (REACH) legislation, that requires industry to evaluate the toxicity of chemical
substances that are in use but have never been subjected to regulatory testing.
REACH regulation has also raised strong criticism and concern from society and industrials because of ethical and economic
reasons. The toxicity evaluation of chemicals requires costly, time-consuming and ethically questionable animal
experiments. As consequence, this European regulation promotes scientific innovation and encourages the use of results
generated by alternative methods, including especially non-testing methods (NTMs), also referred to as in silico tools.
Among them, “Quantitative Structure-Activity Relationships” (QSAR) methods are one of the most recognized machine
learning methods in drug design, toxicology, industrial and environmental chemistry. Nowadays, they can be included in
integrated testing strategies (ITS), to provide information for hazard and risk assessment, classification and labelling.
We propose the development of an ensemble of QSAR predictive models for several parameters related with the different
kinds of genotoxicity damage. These chemoinformatic will be implemented on a proprietary computational technological
platform. Particular attention will be also payed to the generation of models for nanomaterials, taking into account their high
and growing impact nowadays on industry in general.The QSAR models and integration algorithms will be characterized by
their reliability, and will be developed according to the rules set out by the OECD, therefore guaranteeing their validity in
REACH.
substances. The European Chemicals Agency (ECHA) plays the role in implementing the Registration, Evaluation,
Authorization and Restriction of Chemicals (REACH) legislation, that requires industry to evaluate the toxicity of chemical
substances that are in use but have never been subjected to regulatory testing.
REACH regulation has also raised strong criticism and concern from society and industrials because of ethical and economic
reasons. The toxicity evaluation of chemicals requires costly, time-consuming and ethically questionable animal
experiments. As consequence, this European regulation promotes scientific innovation and encourages the use of results
generated by alternative methods, including especially non-testing methods (NTMs), also referred to as in silico tools.
Among them, “Quantitative Structure-Activity Relationships” (QSAR) methods are one of the most recognized machine
learning methods in drug design, toxicology, industrial and environmental chemistry. Nowadays, they can be included in
integrated testing strategies (ITS), to provide information for hazard and risk assessment, classification and labelling.
We propose the development of an ensemble of QSAR predictive models for several parameters related with the different
kinds of genotoxicity damage. These chemoinformatic will be implemented on a proprietary computational technological
platform. Particular attention will be also payed to the generation of models for nanomaterials, taking into account their high
and growing impact nowadays on industry in general.The QSAR models and integration algorithms will be characterized by
their reliability, and will be developed according to the rules set out by the OECD, therefore guaranteeing their validity in
REACH.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101030422 |
| Start date: | 01-10-2021 |
| End date: | 30-09-2023 |
| Total budget - Public funding: | 172 932,48 Euro - 172 932,00 Euro |
Cordis data
Original description
The concern to protect human health and the environment has prompted significant changes in EU regulation on chemicalsubstances. The European Chemicals Agency (ECHA) plays the role in implementing the Registration, Evaluation,
Authorization and Restriction of Chemicals (REACH) legislation, that requires industry to evaluate the toxicity of chemical
substances that are in use but have never been subjected to regulatory testing.
REACH regulation has also raised strong criticism and concern from society and industrials because of ethical and economic
reasons. The toxicity evaluation of chemicals requires costly, time-consuming and ethically questionable animal
experiments. As consequence, this European regulation promotes scientific innovation and encourages the use of results
generated by alternative methods, including especially non-testing methods (NTMs), also referred to as in silico tools.
Among them, “Quantitative Structure-Activity Relationships” (QSAR) methods are one of the most recognized machine
learning methods in drug design, toxicology, industrial and environmental chemistry. Nowadays, they can be included in
integrated testing strategies (ITS), to provide information for hazard and risk assessment, classification and labelling.
We propose the development of an ensemble of QSAR predictive models for several parameters related with the different
kinds of genotoxicity damage. These chemoinformatic will be implemented on a proprietary computational technological
platform. Particular attention will be also payed to the generation of models for nanomaterials, taking into account their high
and growing impact nowadays on industry in general.The QSAR models and integration algorithms will be characterized by
their reliability, and will be developed according to the rules set out by the OECD, therefore guaranteeing their validity in
REACH.
Status
CLOSEDCall topic
MSCA-IF-2020Update Date
28-04-2024
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