A QSAR - Bayesian neural network model to identify molecular properties causing eye irritation in cationic surfactants

摘要

QSAR models are frequently used to investigate and predict the toxicological effects of chemicals. Building QSAR models of the eye irritation potential of cationic surfactants is difficult, as the mechanism of action of these surfactants is still not fully understood. This report describes a data driven QSAR model to predict Maximum Average Scores (MAS in accordance to Draize) for cationic surfactants from the calculated molecular properties Log P, Log CMC and molecular volume, and the surfactant concentration. We demonstrate that a Bayesian Neural Network, a statistical non-linear regression approach that estimates the noise in the modelling data and error bars on the predictions, provided the most robust and accurate representation of the relationship between the MAS score and the molecular properties. A dataset of 20 in vivo rabbit eye irritation tests on 19 different cationic surfactants, obtained from historic in-house data and the scientific literature, was used to train the Bayesian neural network. The model was then used to simulate a large number of molecules to explore the relationship between MAS score and molecular properties. MAS vs. Log P showed bell shaped curve as expected. A higher concentration (> 20%) was required in order to elicit the eye irritancy response of molecules with a wide range of Log P. The simulated results were used to identify the range of molecular properties of cationic surfactants most likely to cause more than mild irritancy. Within the parameter space of the model, defined by the training data, the probability of causing severe irritation is highest for molecules with molecular volume < 320 A03, while -2 < Log P <13 and -6 < Log CMC < 3. The simulated results were carried out at a concentration of 40%. For molecules with larger molecular volumes, the range of Log P and Log CMC for which these molecules would cause severe irritation is narrowed. The model provides useful probabilistic predictions for the eye irritancy potential of new or untested cationic surfactants with physicochemical properties lying within the parameter space of the model.