PURPOSE: The human proton-coupled small peptide carrier (hPEPT1) is a low-affinity, high-capacity transporter with broad substrate specificity. We have taken an iterative in vitro and in silico approach to the discovery of molecules with hPEPT1 affinity. METHODS: A pharmacophore-based approach was taken to identifying hPEPT1 inhibitors. The well-characterized and relatively high affinity ligands Gly-Sar, bestatin, and enalapril were used to generate a common features (HIPHOP) pharmacophore. This consisted of two hydrophobic features, a hydrogen bond donor, acceptor, and a negative ionizable feature. RESULTS: The pharmacophore was used to search the Comprehensive Medicinal Chemistry (CMC) database of more than 8000 drug-like molecules and retrieved 145 virtual hits mapping to the pharmacophore features. The highest scoring compounds within this set were selected and tested in a stably transfected CHO-hPepT1 cell model. The antidiabetic repaglinide and HMG CoA reductase inhibitor fluvastatin were found to inhibit hPEPT1 with sub-millimolar potency (IC(50) 178 +/- 1.0 and 337 +/- 4 microM, respectively). The pharmacophore was also able to identify known hPEPT1 substrates and inhibitors in further database mining of more than 500 commonly prescribed drugs. CONCLUSIONS: This study demonstrates the potential of combining computational and in vitro approaches to determine the affinity of compounds for hPEPT1 and, in turn, provides insights into key molecular interactions with this transporter.
PURPOSE: The human proton-coupled small peptide carrier (hPEPT1) is a low-affinity, high-capacity transporter with broad substrate specificity. We have taken an iterative in vitro and in silico approach to the discovery of molecules with hPEPT1 affinity. METHODS: A pharmacophore-based approach was taken to identifying hPEPT1 inhibitors. The well-characterized and relatively high affinity ligands Gly-Sar, bestatin, and enalapril were used to generate a common features (HIPHOP) pharmacophore. This consisted of two hydrophobic features, a hydrogen bond donor, acceptor, and a negative ionizable feature. RESULTS: The pharmacophore was used to search the Comprehensive Medicinal Chemistry (CMC) database of more than 8000 drug-like molecules and retrieved 145 virtual hits mapping to the pharmacophore features. The highest scoring compounds within this set were selected and tested in a stably transfected CHO-hPepT1 cell model. The antidiabetic repaglinide and HMG CoA reductase inhibitor fluvastatin were found to inhibit hPEPT1 with sub-millimolar potency (IC(50) 178 +/- 1.0 and 337 +/- 4 microM, respectively). The pharmacophore was also able to identify known hPEPT1 substrates and inhibitors in further database mining of more than 500 commonly prescribed drugs. CONCLUSIONS: This study demonstrates the potential of combining computational and in vitro approaches to determine the affinity of compounds for hPEPT1 and, in turn, provides insights into key molecular interactions with this transporter.
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