Directions in QPPR development to complement the predictive models used in risk assessment of nanomaterials
Research output: Contribution to journal › Article › Scientific › peer-review
|Number of pages||9|
|Publication status||Published - 1 Jul 2018|
|Publication type||A1 Journal article-refereed|
There is an increasing need for predictive risk assessment of nanomaterials (NMs) using methods that are rapid, accurate and resource efficient. To fulfill this need, the development and use of Quantitative Property Property Relationships (QPPRs) for estimating the hazard of NMs and NM-related parameters in exposure modelling seems eminent. In this study, we analyze a selection of models used for hazard and/or exposure assessment of NMs. This analysis was done by identifying all the NM-related properties used in these models related to three categories of data: (i) Intrinsic properties specific to the NM, matrix or experimental conditions, (ii) Extrinsic NM properties related to interaction between the intrinsic properties and (iii) Measured hazard or exposure data. This analysis is combined with the current state of QPPR development to recommend further development of QPPRs for predictive risk assessment of NMs. In particular, the use of descriptors related to the interaction between a NM and its surroundings, e.g. the attachment efficiency is proposed.
ASJC Scopus subject areas
- In silico, Modelling, Nanomaterial, QNAR, QPPR, Risk assessment