Literature DB >> 28943385

Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology.

Tomasz Puzyn1, Nina Jeliazkova2, Haralambos Sarimveis3, Richard L Marchese Robinson4, Vladimir Lobaskin5, Robert Rallo6, Andrea-N Richarz4, Agnieszka Gajewicz7, Manthos G Papadopulos8, Janna Hastings9, Mark T D Cronin4, Emilio Benfenati10, Alberto Fernández11.   

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.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Nano-QSAR; QNAR; QNTR; QSAR; Validation

Mesh:

Year:  2017        PMID: 28943385     DOI: 10.1016/j.fct.2017.09.037

Source DB:  PubMed          Journal:  Food Chem Toxicol        ISSN: 0278-6915            Impact factor:   6.023


  5 in total

1.  Identifying diverse metal oxide nanomaterials with lethal effects on embryonic zebrafish using machine learning.

Authors:  Richard Liam Marchese Robinson; Haralambos Sarimveis; Philip Doganis; Xiaodong Jia; Marianna Kotzabasaki; Christiana Gousiadou; Stacey Lynn Harper; Terry Wilkins
Journal:  Beilstein J Nanotechnol       Date:  2021-11-29       Impact factor: 3.649

2.  Multivariate modeling of engineered nanomaterial features associated with developmental toxicity.

Authors:  Kimberly T To; Lisa Truong; Sabrina Edwards; Robert L Tanguay; David M Reif
Journal:  NanoImpact       Date:  2019-11-01

Review 3.  Practices and Trends of Machine Learning Application in Nanotoxicology.

Authors:  Irini Furxhi; Finbarr Murphy; Martin Mullins; Athanasios Arvanitis; Craig A Poland
Journal:  Nanomaterials (Basel)       Date:  2020-01-08       Impact factor: 5.076

4.  RNA m6A Alterations Induced by Biomineralization Nanoparticles: A Proof-of-Concept Study of Epitranscriptomics for Nanotoxicity Evaluation.

Authors:  Jinbin Pan; Jiaojiao Wang; Kun Fang; Wenjing Hou; Bing Li; Jie Zhao; Xinlong Ma
Journal:  Nanoscale Res Lett       Date:  2022-02-05       Impact factor: 5.418

Review 5.  Understanding Nanoparticle Toxicity to Direct a Safe-by-Design Approach in Cancer Nanomedicine.

Authors:  Jossana A Damasco; Saisree Ravi; Joy D Perez; Daniel E Hagaman; Marites P Melancon
Journal:  Nanomaterials (Basel)       Date:  2020-11-02       Impact factor: 5.076

  5 in total

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