A Weight of Evidence approach to classify nanomaterials according to the EU Classification, Labelling and Packaging regulation criteria

TL;DR

This paper presents a novel quantitative Weight of Evidence method for classifying nanomaterials under EU CLP regulations, integrating diverse data sources to improve hazard classification accuracy.

Contribution

It introduces a systematic, probabilistic WoE approach with an R tool for nanomaterial hazard classification aligned with EU regulations, validated on aquatic toxicity data.

Findings

Method yields classifications consistent with ECHA dossiers.

Monte Carlo approach effectively integrates multiple evidence lines.

Validated on real-world nanomaterial data.

Abstract

In the context of the EU Horizon 2020 GRACIOUS project, we proposed a quantitative Weight of Evidence (WoE) approach for hazard classification of nanomaterials (NMs). This approach is based on the requirements of the European Regulation on Classification, Labelling and Packaging of Substances and Mixtures (the CLP Regulation), which implements the United Nations' Globally Harmonized System of Classification and Labelling of Chemicals (UN GHS) in the European Union. The goal of this WoE methodology is to facilitate classification of NMs according to CLP criteria, following the decision trees defined in ECHA's CLP regulatory guidance. The proposed methodology involves the following stages: (1) collection of data for different NMs related to the endpoint of interest: each study related to each NM is referred as a Line of Evidence (LoE); (2) computation of weighted scores for each LoE: each LoE is weighted by a score calculated based on agreed data quality and completeness criteria defined in the GRACIOUS project; (3) comparison and integration of the weighed LoEs for each NM: A Monte Carlo resampling approach is adopted to quantitatively and probabilistically integrate the weighted evidence; and (4) assignment of each NM to a hazard class: according to the results, each NM is assigned to one of the classes defined by the CLP regulation. Furthermore, to facilitate the integration and the classification of the weighted LoEs, an R tool was developed. Finally, the approach was tested against an endpoint relevant to CLP (Aquatic Toxicity) using data retrieved from the eNanoMapper database, results obtained were consistent to results in ECHA registration dossiers and in recent literature