Nanosafety is a burning issue to modern science, and different research and academic institutions and regulatory authorities are trying to develop and implement some new approach methodologies (NAMs) for better risk assessment of the nanomaterials and nanoparticles. The experimental toxicity testing is a troublesome and time consuming process; thus now-a-days, scientists and regulatory agencies rely mostly on in silicotechniques (e.g., quantitative read-across, quantitative structure-activity relationship, structural alerts, etc.) for the filling of toxicity data and knowledge gaps. In the current study, we have stressed upon the new approach methodology and developed a new prediction oriented quantitative read-across algorithm for 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. To validate the new method, three high quality literature nanotoxicity datasets (data points &lt; 20 in each case) have been used. The quality of predictions depends on the selection of the distance threshold, similarity threshold, and the number of close training compounds used for prediction. The external validation metrics such as, predicted variance coefficients (Q2F1, Q2F2), and root mean-square error of prediction (RMSEp) have been used for the judgment of prediction quality of the new method. 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 computer program has also been developed based on the proposed algorithm (available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home) which can effectively predict the toxicity of unknown NPs after providing the structural information of chemical analogues. Therefore, the new algorithm and the developed program can provide with an efficient new approach methodology for the data gap filling, prioritizing existing and new NPs, for the risk assessments.

Quantitative read-across for nanotoxicity predictions

ACS Spring 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.