The viral NS5B RNA-dependent RNA-polymerase (RdRp) is one of the best-studied and promising targets for the development of novel therapeutics against hepatitis C virus (HCV). Allosteric inhibition of this enzyme has emerged as a viable strategy toward blocking replication of viral RNA in cell based systems. Herein, we describe how the combination of a complete computational procedure together with biological studies led to the identification of novel molecular scaffolds, hitherto untested toward NS5B polymerase. Structure based 3-D quantitative structure-activity relationship (QSAR) models were generated employing NS5B non-nucleoside inhibitors (NNIs), whose bound conformations were readily available from the protein database (PDB). These were grouped into two training sets of structurally diverse NS5B NNIs, based on their binding to the enzyme thumb (15 NNIs) or palm (10 NNIs) domains. Ligand based (LB) and structure based (SB) alignments were rigorously investigated to assess the reliability on the correct molecular alignment for unknown binding mode modeled compounds. Both Surflex and Autodock programs were able to reproduce with minimal errors the experimental binding conformations of 24 experimental NS5B allosteric inhibitors. Eighty-one (thumb) and 223 (palm) modeled compounds taken from literature were LB and SB aligned and used as external validation sets for the development of 3-D QSAR models. Low error of prediction proved the 3-D QSARs to be useful scoring functions for the in silico screening procedure. Finally, the virtual screening of the NCI Diversity Set led to the selection for enzymatic assays of 20 top-scoring molecules for each final model. Among the 40 selected molecules, preliminary data yielded four derivatives exhibiting IC(50) values ranging between 45 and 75 microM. Binding mode analysis of hit compounds within the NS5B polymerase thumb domain showed that one of them, NSC 123526, exhibited a docked conformation which was in good agreement with the thumb training set most active compound (6).

Hsp90 continues to be an important target for pharmaceutical discovery. In this project, virtual screening (VS) for novel Hsp90 inhibitors was performed using a combination of Autodock and Surflex-Sim (LB) scoring functions with the predictive ability of 3-D QSAR models, previously generated with the 3-D QSAutogrid/R procedure. Extensive validation of both structure-based (SB) and ligand-based (LB), through realignments and cross-alignments, allowed the definition of LB and SB alignment rules. The mixed LB/SB protocol was applied to virtually screen potential Hsp90 inhibitors from the NCI Diversity Set composed of 1785 compounds. A selected ensemble of 80 compounds were biologically tested. Among these molecules, preliminary data yielded four derivatives exhibiting IC50 values ranging between 18 and 63 μM as hits for a subsequent medicinal chemistry optimization procedure.

No abstract