IONTOX

Safe green solvents for the future: in silico predictive chemometric models for selected toxicity endpoints of ionic liquids

Coordinatore	THE UNIVERSITY OF MANCHESTER    more  Organization address address: OXFORD ROAD city: MANCHESTER postcode: M13 9PL contact info Titolo: Ms. Nome: Liz Cognome: Fay Email: send email Telefono: 441613000000
Nazionalità Coordinatore	United Kingdom [UK]
Totale costo	309˙235 €
EC contributo	309˙235 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2012-IIF
Funding Scheme	MC-IIF
Anno di inizio	2013
Periodo (anno-mese-giorno)	2013-08-14   -   2015-08-13

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	THE UNIVERSITY OF MANCHESTER  Organization address address: OXFORD ROAD city: MANCHESTER postcode: M13 9PL contact info Titolo: Ms. Nome: Liz Cognome: Fay Email: send email Telefono: 441613000000	UK (MANCHESTER)	coordinator	309˙235.20

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 Obiettivo del progetto (Objective)

'Ionic liquids (ILs) are a modern addition to the world of chemical compounds, deployed in areas ranging from electrochemistry, over organic synthesis, to cleaning, extraction and separation technology. Their unique negligible vapor pressure, non-flammability, enhanced thermal stability and outstanding solvation potential make them ‘‘green’’ solvents. However, their toxicity needs to be understood and controlled, which is a challenge. Indeed, countless possible cation-anion combinations create billions of possible ILs, which calls for a rapid and reliable toxicity prediction. This can only be achieved through computation. Quantitative structure-property relationships (QSPR) solve the problem created by the current stringent environmental regulations and the impossibility of costly and time consuming experimental determination. This is why the EU-implemented REACH regulation recommends valid QSPRs for predicting ecotoxicity when experimental data are not available. In the present project, ecotoxicological models for ILs will be developed in silico, obeying OECD principles, based on available toxicity data against various endpoints. Considering the ever growing interest in ILs, truly predictive QSPR models will be highly advantageous in designing desired ILs. We emphasize proper external validation, partially through new and stringent statistical measures. We combine this attitude with our two complementary strengths: physicochemical parameters rooted in quantum chemistry and rigorous chemometrics. Both the host and researcher have long experience in ecotoxicological QSAR modelling and they have expertise in complementary areas ensuring true transfer of knowledge. The project also aims to establish collaboration with experimental toxicologists of the University of Manchester for experimental validation of the developed models. This will deliver innovative QSPR models and expert systems for predicting toxicity of ILs, ready for European regulatory purposes.'