Predicting skin permeability from complex chemical mixtures: incorporation of an expanded QSAR model
Quantitative structure–activity relationship (QSAR) models have been widely used to study the permeability of chemicals or solutes through skin. Among the various QSAR models, Abraham’s linear free-energy relationship (LFER) model is often employed. However, when the experimental
conditions are complex, it is not always appropriate to use Abraham’s LFER model with a single set of regression coefficients. In this paper, we propose an expanded model in which one set of partial slopes is defined for each experimental condition, where conditions are defined according
to solvent: water, synthetic oil, semi-synthetic oil, or soluble oil. This model not only accounts for experimental conditions but also improves the ability to conduct rigorous hypothesis testing. To more adequately evaluate the predictive power of the QSAR model, we modified the usual leave-one-out
internal validation strategy to employ a leave-one-solute-out strategy and accordingly adjust the Q2
LOO
statistic. Skin permeability was shown to have the rank order: water > synthetic > semi-synthetic > soluble oil. In addition, fitted relationships
between permeability and solute characteristics differ according to solvents. We demonstrated that the expanded model (r2
= 0.70) improved both the model fit and the predictive power when compared with the simple model (r2
= 0.21).
Keywords: Abraham’s LFER model; QSAR; adjusted Q2 LOO; expanded LFER model; leave-one-solute-out
Document Type: Research Article
Publication date: 01 September 2013
- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content