Prediction of Permeability Coefficients of Compounds Through Caco-2 Cell Monolayer Using Artificial Neural Network Analysis

Abstract

Artificial neural network (ANN) analysis was used to predict the permeability of selected compounds through Caco-2 cell monolayers. Previously reported models, which were shown to be useful in the prediction of permeability values, use many structural parameters. More complex equations have also been proposed using both linear and non-linear relationships, including ANN analysis and various structural parameters. But proposed models still need to be developed using different neuron patterns for more precise predictions and a better understanding of which factors affect the permeation. To develop a simple and useful model or method for easy prediction is also a general need. Permeability coefficients (log k_p) were obtained from various literature sources. Some structural parameters were calculated using computer programs. Multiple linear regression analysis (MLRA) was used to predict Caco-2 cell permeability for the set of 50 compounds (r² = 0.403). A successful ANN model was developed, and the ANN produced log kp values that correlated well with the experimental ones (r² = 0.952). The permeability of a compound, famotidine, which has not previously been studied, through the Caco-2 cell monolayer was investigated, and its permeability coefficient determined. It was then possible to compare the experimental data with that predicted using the trained ANN with previously determined Caco-2 cell permeability values and structural parameters of compounds. The model was also tested using literature values. The developed and described ANN model in this publication does not require any experimental parameters; it could potentially provide useful and precise prediction of permeability for new drugs or other penetrants.

Key Words:

• Artificial neural network
• Prediction of permeability
• Caco-2 cell
• Molecular modeling