Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform

Area of Science:

• Nanotechnology
• Computational Toxicology
• Materials Science

Background:

• Metal oxide nanoparticles (Me_xO_y NPs) are widely used, necessitating robust methods to assess their potential health risks.
• Predicting NP cytotoxicity is crucial for safe application and regulatory compliance.
• Existing methods for cytotoxicity assessment can be time-consuming and resource-intensive.

Purpose of the Study:

• To develop and validate an in silico model for predicting the cytotoxicity of Me_xO_y NPs.
• To identify key descriptors influencing NP-induced cytotoxicity.
• To provide a publicly available tool for NP safety evaluation.

Main Methods:

• A dataset of 24 Me_xO_y NPs was curated, incorporating 15 physicochemical, structural, and assay-related descriptors.
• 62 atomistic computational descriptors were added to create a comprehensive descriptor set (77 total).
• A robust in silico model was developed and validated to predict cytotoxicity using lactate dehydrogenase (LDH) and adenosine triphosphate (ATP) assays.

Main Results:

• The model successfully predicted Me_xO_y NP cytotoxicity (cell viability).
• Seven significant descriptors were identified: NP core size, hydrodynamic size, assay type, exposure dose, conduction band energy (E_C), surface metal atom coordination number (Avg. C.N. Me atoms surface), and surface metal atom normal force vector (v⟂ Me atoms surface).
• These descriptors showed a direct correlation with cytotoxicity, providing mechanistic insights.

Conclusions:

• The developed in silico model offers a reliable method for predicting Me_xO_y NP cytotoxicity.
• The identified key descriptors enhance understanding of NP-cytotoxicity mechanisms.
• The model, available through the NanoSolveIT project, supports an Integrated Approach to Testing and Assessment (IATA) for NPs.