Puzyn, T, Jeliazkova, N, Sarimveis, H et al. (10 more authors) (2018) Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology. Food and Chemical Toxicology, 112. pp. 478-494. ISSN 0278-6915
Abstract
Nanotechnology and the production of nanomaterials have been expanding rapidly in recent years. Since many types of engineered nanoparticles are suspected to be toxic to living organisms and to have a negative impact on the environment, the process of designing new nanoparticles and their applications must be accompanied by a thorough risk analysis. (Quantitative) Structure-Activity Relationship ([Q]SAR) modelling creates promising options among the available methods for the risk assessment. These in silico models can be used to predict a variety of properties, including the toxicity of newly designed nanoparticles. However, (Q)SAR models must be appropriately validated to ensure the clarity, consistency and reliability of predictions. This paper is a joint initiative from recently completed European research projects focused on developing (Q)SAR methodology for nanomaterials. The aim was to interpret and expand the guidance for the well-known “OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models”, with reference to nano-(Q)SAR, and present our opinions on the criteria to be fulfilled for models developed for nanoparticles.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Elsevier Ltd. This is an author produced version of a paper published in Food and Chemical Toxicology. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Nano-QSAR; QNTR; QNAR; QSAR; Validation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 11 Sep 2019 15:26 |
Last Modified: | 11 Sep 2019 15:26 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.fct.2017.09.037 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:150712 |
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