A QSAR model for predicting the corneal permeability of drugs – the application of the Monte Carlo optimization method

Abstract

Direct implementation into the eye is the most commonly used method for applying drugs for the treatment of most ocular diseases. The preferred way for improving the bioavailability of ophthalmic drugs is by increasing their corneal permeability, but evaluation of this activity is a time-consuming and expensive process carried out on experimental animals. One solution to overcome these issues is developing a QSAR model to predict corneal permeability. For developing a QSAR model, the Monte Carlo optimization method was applied, using three splits of the initial molecular data set for training and test sets. The QSAR model was developed with the application of optimal molecular descriptors based on both SMILES notation and molecular graphs. Various statistical parameters, including the correlation coefficient, cross-validated correlation coefficient, standard error of estimation, mean absolute error, Fischer ratio, root-mean-square error, R_m², MAE-based metrics, and index of ideality of correlation, were used to validate the developed QSAR model. The predictability potential and robustness of the developed QSAR model were proven to be very good by applying statistical methods. Molecular fragments, used as optimal descriptors in QSAR modeling, that have a positive and negative effect on corneal permeability were identified. The developed QSAR models for corneal permeability with the application of the Monte Carlo optimization method have very good predictivity potential. The identified molecular fragments could be useful when assessment of novel drugs' corneal permeability has to be performed.