Discovery of Potent Inhibitors of 11ß-Hydroxysteroid Dehydrogenase Type 1 Using a Novel Growth-Based Protocol of in Silico Screening and Optimization in CONTOUR.

With the continuous progress in ultralarge virtual libraries which are readily accessible, it is of great interest to explore this large chemical space for hit identification and lead optimization using reliable structure-based approaches. In this work, a novel growth-based screening protocol has been designed and implemented in the structure-based design platform CONTOUR. The protocol was used to screen the ZINC database in silico and optimize hits to discover 11ß-HSD1 inhibitors. In contrast to molecular docking, the virtual screening process makes significant improvements in computational efficiency without losing chemical equities through partitioning 1.8 million ZINC compounds into fragments, docking fragments to form key hydrogen bonds with anchor residues, reorganizing molecules into molecular fragment trees using matched fragments and common substructures, and then regrowing molecules with the help of developed intelligent growth features inside the protein binding site to find hits. The growth-base screening approach is validated by the high hit rate. A total of 50 compounds have been selected for testing; of these, 15 hits having diverse scaffolds are found to inhibit 11ß-HSD1 with IC50 values of less than 1 µM in a biochemical enzyme assay. The best hit which exhibits an enzyme IC50 of 33 nM is further developed to a novel series of bicyclic 11ß-HSD1 inhibitors with the best inhibition of enzyme IC50 of 3.1 nM. The final lead candidate exhibits IC50 values of 7.2 and 21 nM in enzyme and adipocyte assays, respectively, displayed greater than 1000-fold of selectivity over 11ß-HSD2 and two other related hydroxysteroid dehydrogenases, and can serve as good starting points for further optimization to develop clinical candidates.

Structure-based design technology contour and its application to the design of renin inhibitors.

It is well-known that the structure-based design approach has had a measurable impact on the drug discovery process in identifying novel and efficacious therapeutic agents for a variety of disease targets. The de novo design approach has inherent potential to generate novel molecules that best fit into a protein binding site when compared to all of the computational methods applied to structure-based design. In its initial attempts, this approach did not achieve much success due to technical hurdles. More recently, the algorithmic advancements in the methodologies and clever strategies developed to design drug-like molecules have improved the success rate. We describe a state-of-the-art structure-based design technology called Contour and provide details of the algorithmic enhancements we have implemented. Contour was designed to create novel drug-like molecules by assembling synthetically viable fragments in the protein binding site using a high-resolution crystal structure of the protein. The technology consists of a sophisticated growth algorithm and a novel scoring function based on a directional model. The growth algorithm generates molecules by dynamically selecting only those fragments from the fragment library that are complementary to the binding site, and assembling them by sampling the conformational space for each attached fragment. The scoring function embodying the essential elements of the binding interactions aids in the rank ordering of grown molecules and helps identify those that have high probability of exhibiting activity against the protein target of interest. The application of Contour to identify inhibitors against human renin enzyme eventually leading to the clinical candidate VTP-27,999 will be discussed here.