Structure-based model profiles affinity constant of drugs with hPEPT1 for rapid virtual screening of hPEPT1's substrate.

The human proton-coupled peptide transporter (hPEPT1) with broad substrates is an important route for improving the pharmacokinetic performance of drugs. Thus, it is essential to predict the affinity constant between drug molecule and hPEPT1 for rapid virtual screening of hPEPT1's substrate during lead optimization, candidate selection and hPEPT1 prodrug design. Here, a structure-based in silico model for 114 compounds was constructed based on eight structural parameters. This model was built by the multiple linear regression method and satisfied all the prerequisites of the regression models. For the entire data set, the r(2) and adjusted r(2) values were 0.74 and 0.72, respectively. Then, this model was used to perform substrate/non-substrate classification. For 29 drugs from DrugBank database, all were correctly classified as substrates of hPEPT1. This model was also used to perform substrate/non-substrate classification for 18 drugs and their prodrugs; this QSAR model also can distinguish between the substrate and non-substrate. In conclusion, the QSAR model in this paper was validated by a large external data set, and all results indicated that the developed model was robust, stable, and can be used for rapid virtual screening of hPEPT1's substrate in the early stage of drug discovery.