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Predicting the cytotoxicity of nanomaterials through explainable, extreme gradient boosting
Allegra Conti1, Luisa Campagnolo2, Stefano Diciotti3,4
1Medical Physics Section, Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.
Nanotoxicology
|December 19, 2022
Summary
Nanoparticle toxicity is complex, but key properties like size, shape, and surface area significantly predict cellular damage. Understanding these factors is crucial for developing safer nanomaterials.
Area of Science:
- Nanotechnology
- Materials Science
- Toxicology
Background:
- Nanoparticles (NPs) are widely used in industry and medicine.
- Their small size allows easy entry into the human body, potentially causing tissue damage.
- NP toxicity is influenced by physical and chemical properties, but these relationships are not fully understood.
Purpose of the Study:
- To assess the importance of NP properties and experimental conditions in determining cytotoxicity.
- To develop a predictive model for NP cytotoxicity.
- To identify key descriptors for safer-by-design nanomaterial development.
Main Methods:
- Utilized a multicenter cytotoxicity nanomaterial database (12 materials).
- Developed a regressor model using extreme gradient boosting with hyperparameter optimization.
- Employed Shapley additive explanations (SHAP) for model interpretability and performance evaluation.
Main Results:
- Achieved statistically significant Spearman correlations (0.5–0.7) between predicted and true cytotoxicity values.
- Identified key predictors of NP cytotoxicity: in situ size (>200 nm), surface area (>50 m²/g), primary particle size (<20 nm), irregular shape, and positive Z-potential.
- Highlighted the influence of lactate dehydrogenase (LDH) assays and short experimental times on prediction accuracy.
Conclusions:
- NP cytotoxicity is influenced by a complex interplay of properties and experimental conditions.
- Specific NP characteristics (size, shape, surface area, Z-potential) are major drivers of toxicity.
- Systematic experimental characterization is essential for advancing safer-by-design approaches in nanotechnology.
