Quantum mechanical quantitative structure activity relationships to avoid mutagenicity in dental monomers

Area of Science:

• Computational chemistry
• Toxicology
• Materials science

Background:

• Dental materials often contain monomers with potential mutagenic properties.
• Predicting and mitigating mutagenicity is crucial for biomaterial safety.

Purpose of the Study:

• To identify chemical structures in dental monomers that influence their mutagenicity using Q-QSAR.
• To develop predictive models for dental monomer mutagenicity.

Main Methods:

• Applied semiempirical quantum mechanical calculations (AMPAC) to chemical structures.
• Developed QSAR models using mutagenicity data (Ames Test, Salmonella TA 100) and quantum chemical descriptors (CODESSA).
• Validated QSAR models by predicting mutagenicity of BISGMA, GY-281, and UVR-6105.

Main Results:

• Developed QSAR models with r2 values exceeding 0.90 for methacrylates, aromatic epoxies, and aliphatic epoxies.
• Accurately predicted the mutagenicity of BISGMA, GY-281, and UVR-6105 using Q-QSAR descriptors.
• Identified specific structural components contributing to mutagenicity and established 'dose windows' for testing.

Conclusions:

• Q-QSAR is an effective method for predicting dental monomer mutagenicity.
• This approach can guide the design of safer dental biomaterials by identifying and avoiding mutagenic structural features.
• Q-QSAR reduces the need for extensive laboratory mutagenicity testing.