The proton-coupled amino acid transporter hPAT1 has recently gained much 
interest due to its ability to transport small drugs thereby allowing their oral 
administration. A three-dimensional quantitative structure-activity relationship 
(3D QSAR) study has been performed on its natural and synthetic substrates 
employing comparative molecular similarity indices analysis (CoMSIA) to 
investigate the structural requirements for substrates and to derive a 
predictive model that may be used for the design of new prodrugs. The 
cross-validated CoMSIA models have been derived from a training set of 40 
compounds and the predictive ability of the resulting models has been evaluated 
against a test set of 10 compounds. Despite the relatively narrow range of 
binding affinities (K(i) values) reliable statistical models with good 
predictive power have been obtained. The best CoMSIA model in terms of a proper 
balance of all statistical terms and the overall contribution of individual 
properties has been obtained by considering steric, hydrophobic, hydrogen bond 
donor and acceptor descriptors (q(cv)(2)=0.683, r(2)=0.958 and 
r(PRED)(2)=0.666). The 3D QSAR model provides insight in the interactions 
between substrates and hPAT1 on the molecular level and allows the prediction of 
affinity constants of new compounds. A pharmacophore model has been generated 
from the training set by means of the MOE (molecular operating environment) 
program. This model has been used as a query for virtual screening to retrieve 
potential new substrates from the small-molecule, 'lead-like' databases of MOE. 
The affinities of the compounds were predicted and 11 compounds were identified 
as possible high-affinity substrates. Two selected compounds strongly inhibited 
the hPAT mediated l-[(3)H]proline uptake into Caco-2 cells constitutively 
expressing the transport protein.