A novel quantitative read-across tool designed purposefully to fill the existing gaps in nanosafety data

In the current time, the number of various engineered nanomaterials (NMs) and nanoparticles (NPs) is increasing at a steady pace due to the new developments in the field of nanotechnology. These rapid uses of NPs are allowing their entry into the environment and human body also. The small size and large surface area of these materials enhance the potential to pass through the living plasma membrane and hence create possibilities to interact with various intracellular materials. The negative impact of NPs towards human health and environmental safety has already been established. The laboratory experimentations are troublesome and ethically complicated; thus, various computational techniques (e.g., quantitative read-across predictions) are very crucial for data gap filling and risk assessments, especially in view of the limited experimental data being available. In the current study, we propose a new quantitative read-across methodology for predicting the toxicity (biological activity in general) of newly synthesized NPs based on the similarity (Euclidean distance-based similarity, Gaussian kernel function similarity, Laplacian kernel function similarity) with structural analogues. These new methods are successfully validated against three published nanotoxicity datasets. The quality of predictions depends on the selection of the distance threshold, similarity threshold, and the number of most similar training compounds. In the current study, best predictions were obtained after selecting 0.4-0.5 as the distance threshold, 0.00-0.05 as the similarity threshold, and 2-5 as the number of most similar training compounds. After toxicity prediction of test set compounds, the external validation metrics such as Q2(ext_F1), Q2(ext_F2), RMSEP were calculated. The computed metric values clearly indicate the efficiency of the new read-across method and accuracy of the generated data by the proposed algorithm. A java based program (available at https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home) has also been developed based on the proposed algorithm which can effectively predict the toxicity of unknown NPs after providing the structural information of chemical analogues. Therefore, the new algorithm and the program can be used for the data gap filling, prioritizing existing and new NPs, and in a nutshell for the risk assessments of NPs.

Automated summary

With the increasing number of engineered nanomaterials and nanoparticles in the field of nanotechnology, there is a need to predict their toxicity using quantitative read-across methodology. The new approach has been successfully validated for toxicity prediction of newly synthesized nanoparticles, demonstrating its efficiency and accuracy in risk assessments.