Person:
Brandt, Marc

Profilbild
E-Mail-Adresse
Geburtsdatum
Forschungsvorhaben
Organisationseinheiten
Logo der Organisationseinheit
Organisationseinheit
Berufsbeschreibung
Nachname
Brandt
Vorname
Marc
Name

Filter

20161
Filter zurücksetzen

Einstellungen

Thema: Bioakkumulation ×

Suchergebnisse

Gerade angezeigt 1 - 1 von 1
  • Vorschaubild nicht verfügbar
    Veröffentlichung
    Integrated in silico strategy for PBT assessment and prioritization under REACH
    (2016) Pizzo, Fabiola; Lombardo, Anna; Manganaro, Alberto; Brandt, Marc; Cappelli, Claudia I.; Petoumenou, Maria I.; Albanese, Federica; Roncaglioni, Alessandra; Benfenati, Emilio
    Chemicals may persist in the environment, bioaccumulate and be toxic for humans and wildlife, posing great concern. These three properties, persistence (P), bioaccumulation (B), and toxicity (T) are the key targets of the PBT-hazard assessment. The European regulation for the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) requires assessment of PBT-properties for all chemicals that are produced or imported in Europe in amounts exceeding 10 tonnes per year, checking whether the criteria set out in REACH Annex XIII are met, so the substance should therefore be considered to have properties of very high concern. Considering how many substances can fall under the REACH regulation, there is a pressing need for new strategies to identify and screen large numbers fast and inexpensively. An efficient non-testing screening approach to identify PBT candidates is necessary, as a valuable alternative to money- and time-consuming laboratory tests and a good start for prioritization since few tools exist (e.g. the PBT profiler developed by USEPA).
    The aim of this work was to offer a conceptual scheme for identifying and prioritizing chemicals for further assessment and if appropriate further testing, based on their PBT-potential, using a non-testing screening approach. We integrated in silico models (using existing and developing new ones) in a final algorithm for screening and ranking PBT-potential, which uses experimental and predicted values as well as associated uncertainties. The Multi-Criteria Decision-Making (MCDM) theory was used to integrate the different values. Then we compiled a new set of data containing known PBT and non-PBT substances, in order to check how well our approach clearly differentiated compounds labeled as PBT from those labeled as non-PBT. This indicated that the integrated model distinguished between PBT from non-PBT compounds.
    Quelle: http://www.sciencedirect.com