Predicting cytotoxicity of engineered nanoparticles using regularized regression models: an in silico approach

ABSTRACT

The widespread application of engineered nanoparticles (NPs) in various industries has demonstrated their effectiveness over the years. However, modifications to NPs’ physicochemical properties can lead to toxicological effects. Therefore, understanding the toxicity behaviour of NPs is crucial. In this paper, regularized regression models, such as ridge, LASSO, and elastic net, were constructed to predict the cytotoxicity of various engineered NPs. The dataset utilized in this study was compiled from several journals published between 2010 and 2022. Data exploration revealed missing values, which were addressed through listwise deletion and kNN imputation, resulting in two complete datasets. The ridge, LASSO, and elastic net models achieved F1 scores ranging from 91.81% to 92.65% during internal validation and 92.89% to 93.63% during external validation on Dataset 1. On Dataset 2, the models attained F1 scores between 92.16% and 92.43% during internal validation and 92% and 92.6% during external validation. These results indicate that the developed models effectively generalize to unseen data and demonstrate high accuracy in classifying cytotoxicity levels. Furthermore, the cell type, material, cell source, cell tissue, synthesis method, and coat or functional group were identified as the most important descriptors by the three models across both datasets.

KEYWORDS:

• Nanoparticles
• cytotoxicity
• in silico
• regularized regression
• classification task

Acknowledgements

The authors are grateful for the support of the Department of Science and Technology - Philippine Council for Industry, Energy, and Emerging Technology Research and Development with the grant no. 10185. This study was done at the Research on Environment and Nanotechnology Laboratories, Research Division, Mindanao State University at Naawan.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

The authors confirm that the data supporting the findings of this study is available at https://github.com/reylaboratories/Dataset.

Additional information

Funding

This work was supported by the Department of Science and Technology - Philippine Council for Industry, Energy, and Emerging Technology Research and Development (DOST - PCIEERD) under [grant no. 10185].