Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology. (February 2018)

• Record Type: Journal Article
• Authors: Puzyn, Tomasz; Jeliazkova, Nina; Sarimveis, Haralambos; Marchese Robinson, Richard L.; Lobaskin, Vladimir; Rallo, Robert; Richarz, Andrea-N.; Gajewicz, Agnieszka; Papadopulos, Manthos G.; Hastings, Janna; Cronin, Mark T.D.; Benfenati, Emilio; Fernández, Alberto
• Is Part Of: Food and chemical toxicology. Volume 112(2018)
• Page Start: 478
• Page End: 494
• Publication Date: 2018-02
• Subjects: Nano-QSAR -- QNTR -- QNAR -- QSAR -- Validation; Toxicology -- Periodicals; Food poisoning -- Periodicals; Food Poisoning -- Periodicals; Toxicology -- Periodicals; Toxicologie -- Périodiques; Intoxications alimentaires -- Périodiques; Food poisoning; Toxicology; Periodicals; Electronic journals; 615.9
• DOI: 10.1016/j.fct.2017.09.037
• Languages: English
• ISSNs: 0278-6915

Abstract

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. Graphical abstract: Image 1 Highlights: ([Q]SAR) modelling is one possibility for estimating hazard or exposure related characteristics. Classic molecular descriptors might be inappropriate for modelling nanoparticles. Every nano-(Q)SAR model should be presented and documented in a format such as Predictive Model Markup Language (PMML). Every nano-(Q)SAR model must be appropriately validated, which is crucial for ensuring its predictive accuracy. OECD Principles for (Q)SAR validation creates an appropriate framework for validating nano-(Q)SARs as well.