Project description
Evaluating toxicity without testing on animals
The flow of toxic compounds to the environment is a global health issue. Assessing the situation is difficult. The toxicity evaluation of chemicals via animal testing is costly, time-consuming and ethically questionable. One alternative is to use the quantitative structure–activity relationship (QSAR) method. It is amongst the most recognised machine learning methods in drug design, toxicology, and industrial and environmental chemistry. Included in integrated testing strategies, QSARs can provide information for hazard and risk assessment, classification and labelling. In this context, the EU-funded GenoQSAR project will develop an ensemble of QSAR predictive models for several parameters related to the different kinds of genotoxicity damage. Particular attention will be placed on the generation of models for nanomaterials.
Objective
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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- medical and health sciences basic medicine medicinal chemistry
- engineering and technology nanotechnology nano-materials
- natural sciences computer and information sciences artificial intelligence machine learning
- medical and health sciences basic medicine toxicology
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2020
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
46018 Valencia
Spain
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.