Predicting algal growth inhibition toxicity: three-step strategy using structural and physicochemical properties

Area of Science:

• Environmental Toxicology
• Computational Chemistry
• Ecotoxicology

Background:

• Accurate prediction of chemical toxicity to aquatic organisms is crucial for environmental risk assessment.
• Quantitative Structure-Activity Relationships (QSARs) are valuable tools for predicting chemical toxicity.
• Existing methods may require refinement for specific endpoints like algal growth inhibition.

Purpose of the Study:

• To develop and validate a three-step strategy for predicting 72-hour algal growth inhibition toxicity.
• To utilize structural and physicochemical properties of chemicals for toxicity prediction.
• To integrate the strategy into broader Integrated Approaches to Testing and Assessment (IATA).

Main Methods:

• Developed an interspecies QSAR between algal and daphnid toxicities using log D and structural alerts for initial screening.
• Categorized chemicals using the Verhaar scheme and developed QSARs for non-polar and polar narcotic chemicals.
• Employed structural profiles for quantitative/qualitative toxicity prediction and chemical categorization (Pesticide, Reactive, Toxic, etc.).

Main Results:

• Successfully generated QSARs linking chemical properties to algal toxicity.
• Classified chemicals into toxicity categories, enabling targeted risk assessment.
• Demonstrated the strategy's utility for chemicals within its defined domain.

Conclusions:

• The proposed three-step strategy effectively predicts 72-hour algal growth inhibition toxicity for Pseudokirchneriella subcapitata.
• This approach offers a valuable component for Integrated Approaches to Testing and Assessment (IATA).
• The strategy enhances the prediction of ecotoxicological endpoints using chemical structure and properties.