title: Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment.
abstract: In this study, a Bayesian Network (BN) was developed for the prediction of the hazard potential and biological effects with the focus on metal- and metal-oxide nanomaterials to support human health risk assessment. The developed BN captures the (inter) relationships between the exposure route, the nanomaterials physicochemical properties and the ultimate biological effects in a holistic manner and was based on international expert consultation and the scientific literature (e.g., in vitro/in vivo data). The BN was validated with independent data extracted from published studies and the accuracy of the prediction of the nanomaterials hazard potential was 72% and for the biological effect 71%, respectively. The application of the BN is shown with scenario studies for TiO2, SiO2, Ag, CeO2, ZnO nanomaterials. It is demonstrated that the BN may be used by different stakeholders at several stages in the risk assessment to predict certain properties of a nanomaterials of which little information is available or to prioritize nanomaterials for further screening.
authors.affiliation: a Wageningen University and Research, RIKILT , Wageningen , the Netherlands.
authors.forename: Hans J P
authors.initials: HJ
authors.lastname: Marvin
authors.affiliation: a Wageningen University and Research, RIKILT , Wageningen , the Netherlands.
authors.forename: Yamine
authors.initials: Y
authors.lastname: Bouzembrak
authors.affiliation: a Wageningen University and Research, RIKILT , Wageningen , the Netherlands.
authors.forename: Esmée M
authors.initials: EM
authors.lastname: Janssen
authors.affiliation: a Wageningen University and Research, RIKILT , Wageningen , the Netherlands.
authors.forename: Meike
authors.initials: M
authors.lastname: van der Zande
authors.affiliation: b Kemmy Business School, University of Limerick , Ireland.
authors.forename: Finbarr
authors.initials: F
authors.lastname: Murphy
authors.affiliation: b Kemmy Business School, University of Limerick , Ireland.
authors.forename: Barry
authors.initials: B
authors.lastname: Sheehan
authors.affiliation: b Kemmy Business School, University of Limerick , Ireland.
authors.forename: Martin
authors.initials: M
authors.lastname: Mullins
authors.affiliation: a Wageningen University and Research, RIKILT , Wageningen , the Netherlands.|c Division of Toxicology , Wageningen University , Wageningen , the Netherlands.
authors.forename: Hans
authors.initials: H
authors.lastname: Bouwmeester
mesh_terms: D001499:Bayes Theorem; D002563:Cerium / Q000737:chemistry / Q000633:toxicity; D003625:Data Collection; D015386:Hazardous Substances / Q000737:chemistry / Q000633:toxicity*; D006801:Humans; D008962:Models, Theoretical; D049329:Nanostructures / Q000737:chemistry / Q000633:toxicity*; D018570:Risk Assessment; D012822:Silicon Dioxide / Q000737:chemistry / Q000633:toxicity; D012834:Silver / Q000737:chemistry / Q000633:toxicity; D015034:Zinc Oxide / Q000737:chemistry / Q000633:toxicity
chemical_list: D015386:Hazardous Substances; D002563:Cerium; D012834:Silver; C030583:ceric oxide; D012822:Silicon Dioxide; D015034:Zinc Oxide
keywords: Bayesian networks; expert elicitation; metal nanomaterials; risk assessment; scenario studies