Probabilistic assessment of biodegradability based on metabolic pathways: catabol system

Area of Science:

• Environmental Chemistry
• Computational Toxicology
• Biotechnology

Background:

• Assessing chemical biodegradability is crucial for environmental safety and regulatory compliance.
• Existing methods often lack quantitative mechanistic insights or predictive power for diverse chemical structures.
• The need for accurate, structure-based biodegradability prediction tools is paramount.

Purpose of the Study:

• To develop and validate a novel mechanistic modeling approach for quantitative biodegradability assessment.
• To predict oxygen yield during biodegradation tests (OECD 302 C) using chemical structure alone.
• To identify potentially persistent catabolic intermediates and their quantities.

Main Methods:

• An expert system predicting biotransformation pathways combined with a probabilistic model for transformation probabilities.
• A hierarchically ordered library of transformations and a substructure engine.
• Modeling of oxygen (O2) yield using the MITI-I database (532 chemicals) as a training set.

Main Results:

• High agreement between training data and calculated theoretical biological oxygen demand (BOD) (r2 = 0.90).
• Accurate prediction of ready biodegradability (98%) and not ready biodegradability (96%) using a 60% ThOD cutoff.
• Cross-validation yielded a Q2 of 0.88, demonstrating robust predictive performance.

Conclusions:

• The developed mechanistic modeling approach provides a reliable, quantitative method for predicting chemical biodegradability.
• The model accurately predicts biodegradation outcomes and identifies potential persistent intermediates.
• The approach has been successfully implemented in the CATABOL software for practical biodegradability prediction.