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Abstract
Caco-2 cells are currently the most used in vitro tool for prediction of the potential
oral absorption of new drugs. The existence of computational models based on this
data may potentiate the early selection process of new drugs, but the current models
are based on a limited number of cases or on a reduced molecular space. We present
an artificial neural network based only on calculated molecular descriptors for modelling
296 in vitro Caco-2 apparent permeability (P(app)) drug values collected in the literature
using also a pruning procedure for reducing the descriptors space. LogP(app) values
were divided into a training group of 192 drugs for network optimization and a testing
group of another 59 drugs for early stop and internal validation resulting in correlations
of 0.843 and 0.702 and RMSE of 0.546 and 0.791 for the training and testing group,
respectively. External validation was made with an additional group of 45 drugs with
a correlation of 0.774 and RMSE of 0.601. The selected molecular descriptors encode
information related to the lipophilicity, electronegativity, size, shape and flexibility
characteristics of the molecules, which are related to drug absorption. This model
may be a valuable tool for prediction and simulation in the drug development process,
as it allows the in silico estimation of the in vitro Caco-2 apparent permeability.
Copyright (c) 2010. Published by Elsevier B.V.