Membrane-Interaction QSAR Analysis: Application To The Estimation of Eye Irritation of Organic Compounds

Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools

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Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools

Contents

The purpose of this study was to explore a possible mechanism of eye irritation by constructing a corresponding general quantitative structure-activity relationship (QSAR) model using a genetic algorithm. The model was derived from a subset of diverse chemical structures found in the Draize eye irritation ECETOC data set. Methods: Molecular dynamic simulation (MDS) was used to generate intermolecular membrane-solute interaction properties. These intermolecular properties were combined with intramolecular physicochemical properties and features of the solute (irritant) to construct QSAR models using multi-dimensional linear regression and the Genetic Function Approximation (GFA) algorithm. Results: Significant QSAR models for estimating eye irritation potential were constructed in which solute aqueous solvation free energy and solute-membrane interaction energies are the principle correlation descriptors. These physicochemical descriptors were selected from a trial set of 95 descriptors. Conclusion: Combining intermolecular solute-membrane interaction descriptors with intramolecular solute descriptors yields statistically significant eye irritation QSAR models. The resultant QSAR models support an eye irritation mechanism of the action in which increased aqueous solubility of the irritant, and its strength of binding to the membrane, both increase eye irritation.