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dc.contributor.authorPalczewska, Anna Maria
dc.contributor.authorKovarich, S.
dc.contributor.authorCiacci, A.
dc.contributor.authorFioravanzo, E.
dc.contributor.authorBassan, A.
dc.contributor.authorNeagu, Daniel
dc.date.accessioned2019-02-06T15:54:07Z
dc.date.available2019-02-06T15:54:07Z
dc.date.issued2019
dc.identifier.citationPalczewska A, Kovarich S, Ciacci A et al (2019) Ranking strategies to support toxicity prediction: a case study on potential lxr binders. Computational Toxicity. Accepted for Publication.en_US
dc.identifier.urihttp://hdl.handle.net/10454/16786
dc.descriptionYesen_US
dc.description.abstractThe current paradigm of toxicity testing is set within a framework of Mode-of-Action (MoA)/Adverse Outcome Pathway (AOP) investigations, where novel methodologies alternative to animal testing play a crucial role, and allow to consider causal links between molecular initiating events (MIEs), further key events and an adverse outcome. In silico (computational) models are developed to support toxicity assessment within the MoA/AOP framework. This paper focuses on the evaluation of potential binding to the Liver X Receptor (LXR), as this has been identified among the MIEs leading to liver steatosis within an AOP framework addressing repeated dose and target-organ toxicity. The objective of this study was the development of a priority setting strategy, by means of in silico approaches and chemometric tools, to allow for the screening and ranking of chemicals according to their toxicity potential. As a case study, the present paper outlines the methodologies and procedures that have been developed in the context of the COSMOS/cosmetics safety assessment project [4], which developed computational methods in view of supporting cosmetics safety assessment, to rank chemicals based on their potential binding to LXR. Chemicals are ranked based on molecular and QSAR modelling outcomes. The contribution in this paper is threefold: the QSAR model for LXR dataset, an application of molecular modeling approaches, which have been developed and optimized for drug discovery, in the context of toxicology, and finally ranking chemicals based on diverse modelling outcomes. The novelty in this paper consists of the employment of linear (logistic regression) and non-linear (Random Forest) models in the context of ranking chemicals. The results show that these methods can be successfully applied for prioritization of compounds of major concern for potential liver toxicity, and that they perform better than the ranking methods reported in the literature to date (such as total ordering or data fusion).en_US
dc.description.sponsorshipEuropean Community’s Seventh Framework Program (FP7/2007-2013) COSMOS Project under grant agreement n° 266835 and from Cosmetics Europe.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1016/j.comtox.2019.01.004en_US
dc.rights© 2019 Published by Elsevier B.V. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.en_US
dc.subjectRankingen_US
dc.subjectQSARen_US
dc.subjectIn silicoen_US
dc.subjectLXRen_US
dc.subjectToxicity predictionen_US
dc.titleRanking strategies to support toxicity prediction: a case study on potential lxr bindersen_US
dc.status.refereedYesen_US
dc.date.Accepted2019-01-14
dc.date.application2019-01-19
dc.typeArticleen_US
dc.date.EndofEmbargo2020-01-20
dc.type.versionAccepted Manuscripten_US
dc.description.publicnotesThe full-text of this article will be released for public view at the end of the publisher embargo on 20 Jan 2020.en_US
refterms.dateFOA2019-02-06T15:54:07Z


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