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Nano-SAR Modeling for Predicting the Cytotoxicity of Metal Oxide Nanoparticles to PaCa2.

ABSTRACT: Nowadays, the impact of engineered nanoparticles (NPs) on human health and environment has aroused widespread attention. It is essential to assess and predict the biological activity, toxicity, and physicochemical properties of NPs. Computation-based methods have been developed to be efficient alternatives for understanding the negative effects of nanoparticles on the environment and human health. Here, a classification-based structure-activity relationship model for nanoparticles (nano-SAR) was developed to predict the cellular uptake of 109 functionalized magneto-fluorescent nanoparticles to pancreatic cancer cells (PaCa2). The norm index descriptors were employed for describing the structure characteristics of the involved nanoparticles. The Random forest algorithm (RF), combining with the Recursive Feature Elimination (RFE) was employed to develop the nano-SAR model. The resulted model showed satisfactory statistical performance, with the accuracy (ACC) of the test set and the training set of 0.950 and 0.966, respectively, demonstrating that the model had satisfactory classification effect. The model was rigorously verified and further extensively compared with models in the literature. The proposed model could be reasonably expected to predict the cellular uptakes of nanoparticles and provide some guidance for the design and manufacture of safer nanomaterials.

SUBMITTER: Shi H

PROVIDER: S-EPMC8069170 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Although substantial concerns about the inflammatory effects of engineered nanomaterial (ENM) have been raised, experimentally assessing toxicity of various ENMs is challenging and time-consuming. Alternatively, quantitative structure-activity relationship (QSAR) models have been employed to assess nanosafety. However, no previous attempt has been made to predict the inflammatory potential of ENMs. OBJECTIVES:By employing metal oxide nanoparticles (MeONPs) as a model ENM, we aimed to develop QSAR models for prediction of the inflammatory potential by their physicochemical properties. METHODS:We built a comprehensive data set of 30 MeONPs to screen a proinflammatory cytokine interleukin (IL)-1 beta (IL-1?) release in THP-1 cell line. The in vitro hazard ranking was validated in mouse lungs by oropharyngeal instillation of six randomly selected MeONPs. We established QSAR models for prediction of MeONP-induced inflammatory potential via machine learning. The models were further validated against seven new MeONPs. Density functional theory (DFT) computations were exploited to decipher the key mechanisms driving inflammatory responses of MeONPs. RESULTS:Seventeen out of 30 MeONPs induced excess IL-1? production in THP-1 cells. In vivo disease outcomes were highly relevant to the in vitro data. QSAR models were developed for inflammatory potential, with predictive accuracy (ACC) exceeding 90%. The models were further validated experimentally against seven independent MeONPs (ACC=86%). DFT computations and experimental results further revealed the underlying mechanisms: MeONPs with metal electronegativity lower than 1.55 and positive ?-potential were more likely to cause lysosomal damage and inflammation. CONCLUSIONS:IL-1? released in THP-1 cells can be an index to rank the inflammatory potential of MeONPs. QSAR models based on IL-1? were able to predict the inflammatory potential of MeONPs. Our approach overcame the challenge of time- and labor-consuming biological experiments and allowed for computational assessment of MeONP inflammatory potential by characterization of their physicochemical properties. https://doi.org/10.1289/EHP6508.

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Nano-SAR Modeling for Predicting the Cytotoxicity of Metal Oxide Nanoparticles to PaCa2.

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