Mutagenicity: QSAR - quasi-QSAR - nano-QSAR

Area of Science:

• Computational toxicology
• Cheminformatics
• Materials science

Background:

• Assessing the mutagenic potential of chemical compounds and nanomaterials is crucial for safety evaluations.
• Traditional methods for determining mutagenicity can be time-consuming and resource-intensive.
• Predictive modeling offers a promising alternative for rapid risk assessment.

Purpose of the Study:

• To model and predict the mutagenic potential of biphenyl-4-amines and multi-walled carbon nanotubes (MWCNTs).
• To utilize optimal descriptors as a predictive tool for mutagenicity endpoints, specifically TA100.
• To explore the utility of quasi-SMILES for representing influencing factors in mutagenicity prediction.

Main Methods:

• Calculation of optimal descriptors using the Monte Carlo method via CORAL software.
• Development of predictive models for mutagenic potential (TA100) based on optimal descriptors.
• Application of quasi-SMILES to represent various circumstances influencing mutagenicity.
• Validation of models using external, invisible datasets.

Main Results:

• Successful modeling of mutagenic potential for biphenyl-4-amines with statistical characteristics: n=7-11, r(2)=0.649±0.046, s=0.211±0.029.
• Accurate prediction for multi-walled carbon nanotubes (MWCNTs) with statistical characteristics: n=6, r(2)=0.804±0.107, s=0.048±0.01.
• Demonstrated the effectiveness of optimal descriptors and quasi-SMILES in predicting mutagenicity.

Conclusions:

• Optimal descriptors, calculated via Monte Carlo methods, are effective for predicting the mutagenic potential of diverse chemical structures, including biphenyl-4-amines and MWCNTs.
• The quasi-SMILES approach provides a robust framework for incorporating various influencing factors into predictive mutagenicity models.
• This computational approach offers a valuable tool for rapid screening and risk assessment of chemical substances and nanomaterials.