A new approach for the development of tracers: data base screening and in silico modeling for the identification of new ligands for SSTR2.

New ligands are needed to improve diagnostics and treatment of SSTR2 expressing tumors. We implemented a procedure to identify ligands based on computer processing methods. A multistep procedure was used. Search entries were taken from National Cancer Institute database. Application of criteria defined by the Lipinski rules reduced the initial data set. Then a pharmacophore criterion including Lys and Trp residues was the next step of the hierarchical filtering, and the ligands considered were transformed from 2D to 3D. Finally, dedicated software was applied for docking ligand studies. Our results have shown that by virtual screening and trial docking, we identified novel ligands with better scores of docked poses compared with previously reported ligands. In conclusion, the use of a focused library that incorporates an initial probe, improved the possibility of a successful virtual screening as compared with random screening and is cost efficient by further combination of trial docking.