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Directions in QPPR development to complement the predictive models used in risk assessment of nanomaterials

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Directions in QPPR development to complement the predictive models used in risk assessment of nanomaterials. / Quik, Joris T.K.; Bakker, Martine; van de Meent, Dik; Poikkimäki, Mikko; Dal Maso, Miikka; Peijnenburg, Willie.

In: NanoImpact, Vol. 11, 01.07.2018, p. 58-66.

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Quik, Joris T.K. ; Bakker, Martine ; van de Meent, Dik ; Poikkimäki, Mikko ; Dal Maso, Miikka ; Peijnenburg, Willie. / Directions in QPPR development to complement the predictive models used in risk assessment of nanomaterials. In: NanoImpact. 2018 ; Vol. 11. pp. 58-66.

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@article{5fcc2f265af94d0788214d8f2df9a6f4,
title = "Directions in QPPR development to complement the predictive models used in risk assessment of nanomaterials",
abstract = "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.",
keywords = "In silico, Modelling, Nanomaterial, QNAR, QPPR, Risk assessment",
author = "Quik, {Joris T.K.} and Martine Bakker and {van de Meent}, Dik and Mikko Poikkim{\"a}ki and {Dal Maso}, Miikka and Willie Peijnenburg",
year = "2018",
month = "7",
day = "1",
doi = "10.1016/j.impact.2018.02.003",
language = "English",
volume = "11",
pages = "58--66",
journal = "NanoImpact",
issn = "2452-0748",
publisher = "Elsevier BV",

}

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TY - JOUR

T1 - Directions in QPPR development to complement the predictive models used in risk assessment of nanomaterials

AU - Quik, Joris T.K.

AU - Bakker, Martine

AU - van de Meent, Dik

AU - Poikkimäki, Mikko

AU - Dal Maso, Miikka

AU - Peijnenburg, Willie

PY - 2018/7/1

Y1 - 2018/7/1

N2 - 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.

AB - 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.

KW - In silico

KW - Modelling

KW - Nanomaterial

KW - QNAR

KW - QPPR

KW - Risk assessment

U2 - 10.1016/j.impact.2018.02.003

DO - 10.1016/j.impact.2018.02.003

M3 - Article

VL - 11

SP - 58

EP - 66

JO - NanoImpact

JF - NanoImpact

SN - 2452-0748

ER -