A corneal-PAMPA-based in silico model for predicting corneal permeability

The capability to predict corneal permeability based on physicochemical parameters has always been a desirable objective of ophthalmic drug development. However, previous work has been limited to cases where either the diversity of compounds used was lacking or the performance of the models was poor. Our study provides extensive quantitative structure-property relationship (QSPR) models for corneal permeability predictions. The models involved in vitro corneal permeability measurements of 189 diverse compounds. Preliminary analysis of data showed that there is no significant correlation between corneal-PAMPA (Parallel Artificial Membrane Permeability Assay) permeability values and other pharmacokinetically relevant in silico drug transport parameters like Caco-2, jejunal permeability and blood-brain partition coefficient (logBB). Two different QSPR models were developed: one for corneal permeability and one for corneal membrane retention, based on experimental corneal-PAMPA permeability data. Partial least squares regression was applied for producing the models, which contained classical molecular descriptors and ECFP fingerprints in combination. A complex validation protocol (including internal and external validation) was carried out to provide robust and appropriate predictions for the permeability and membrane retention values. Both models had an overall fit of R² > 0.90, including R²-values not lower than 0.85 for validation runs, and provide quick and accurate predictions of corneal permeability values for a diverse set of compounds.