Pharmacophore-based discovery of ligands for drug transporters

Abstract

The ability to identify ligands for drug transporters is an important step in drug discovery and development. It can both improve accurate profiling of lead pharmacokinetic properties and assist in the discovery of new chemical entities targeting transporters. In silico approaches, especially pharmacophore-based database screening methods have great potential in improving the throughput of current transporter ligand identification assays, leading to a higher hit rate by focusing in vitro testing to the most promising hits. In this review, the potential of different in silico methods in transporter ligand identification studies are compared and summarized with an emphasis on pharmacophore modeling. Various implementations of pharmacophore model generation, database compilation and flexible screening algorithms are also introduced. Recent successful utilization of database searching with pharmacophores to identify novel ligands for the pharmaceutically significant transporters hPepT1, P-gp, BCRP, MRP1 and DAT are reviewed and the challenges encountered with current approaches are discussed.

Figure 1

A schematic for pharmacophore-database searching for drug discovery.

Figure 2

Catalyst P-gp substrate pharmacophore with (A) Gleevec (B) Curcumin mapped to hydrophobic (blue), and H-bond acceptor (green) features.

Figure 3

Pharmacophore model for MRP1 inhibition was mapped to the training set compound LY402913 (A) and the retrieved positive hit sulfasalazine (B) (Yellow, ring aromatic; green, hydrogen bond acceptor; arrow, vector).

Figure 4

Pharmacophore model for BCRP inhibition was mapped to the training set compound GF120918 (A) and the retrieved positive hit docetaxol (B) (Cyan, hydrophobe; green, hydrogen bond acceptor; arrow, vector).