ADME Evaluation in Drug Discovery. 5. Correlation of Caco-2 Permeation with Simple Molecular Properties

The correlations between Caco-2 permeability (logPapp) and molecular properties have been investigated. A
training set of 77 structurally diverse organic molecules was used to construct significant QSAR models for
Caco-2 cell permeation. Cellular permeation was found to depend primarily upon experimental distribution
coefficient (logD) at pH = 7.4, high charged polar surface area (HCPSA), and radius of gyration (rgyr).
Among these three descriptors, logD may have the largest impact on diffusion through Caco-2 cell because
logD shows obvious linear correlation with logPapp (r=0.703) when logD is smaller than 2.0. High polar
surface area will be unfavorable to achieve good Caco-2 permeability because higher polar surface area
will introduce stronger H-bonding interactions between Caco-2 cells and drugs. The comparison among
HCPSA, PSA (polar surface area), and TPSA (topological polar surface area) implies that high-charged
atoms may be more important to the interactions between Caco-2 cell and drugs. Besides logD and HCPSA,
rgyr is also closely connected with Caco-2 permeabilities. The molecules with larger rgyr are more difficult
to cross Caco-2 monolayers than those with smaller rgyr. The descriptors included in the prediction models
permit the interpretation in structural terms of the passive permeability process, evidencing the main role of
lipholiphicity, H-bonding, and bulk properties. Besides these three molecular descriptors, the influence of
other molecular descriptors was also investigated. From the calculated results, it can be found that introducing
descriptors concerned with molecular flexibility can improve the linear correlation. The resulting model
with four descriptors bears good statistical significance, n = 77, r = 0.82, q = 0.79, s = 0.45, F = 35.7.
The actual predictive abilities of the QSAR model were validated through an external validation test set of
23 diverse compounds. The predictions for the tested compounds are as the same accuracy as the compounds
of the training set and significantly better than those predicted by using the model reported. The good
predictive ability suggests that the proposed model may be a good tool for fast screening of logPapp for
compound libraries or large sets of new chemical entities via combinatorial chemistry synthesis.