Discovery of an Orally Efficacious MYC Inhibitor for Liver Cancer Using a GNMT-Based High-Throughput Screening System and Structure-Activity Relationship Analysis.

Glycine-N-methyl transferase (GNMT) downregulation results in spontaneous hepatocellular carcinoma (HCC). Overexpression of GNMT inhibits the proliferation of liver cancer cell lines and prevents carcinogen-induced HCC, suggesting that GNMT induction is a potential approach for anti-HCC therapy. Herein, we used Huh7 GNMT promoter-driven screening to identify a GNMT inducer. Compound K78 was identified and validated for its induction of GNMT and inhibition of Huh7 cell growth. Subsequently, we employed structure-activity relationship analysis and found a potent GNMT inducer, K117. K117 inhibited Huh7 cell growth in vitro and xenograft in vivo. Oral administration of a dosage of K117 at 10 mpk (milligrams per kilogram) can inhibit Huh7 xenograft in a manner equivalent to the effect of sorafenib at a dosage of 25 mpk. A mechanistic study revealed that K117 is an MYC inhibitor. Ectopic expression of MYC using CMV promoter blocked K117-mediated MYC inhibition and GNMT induction. Overall, K117 is a potential lead compound for HCC- and MYC-dependent cancers.

Homology modeling of DFG-in FMS-like tyrosine kinase 3 (FLT3) and structure-based virtual screening for inhibitor identification.

The inhibition of FMS-like tyrosine kinase 3 (FLT3) activity using small-molecule inhibitors has emerged as a target-based alternative to traditional chemotherapy for the treatment of acute myeloid leukemia (AML). In this study, we report the use of structure-based virtual screening (SBVS), a computer-aided drug design technique for the identification of new chemotypes for FLT3 inhibition. For this purpose, homology modeling (HM) of the DFG-in FLT3 structure was carried using two template structures, including PDB ID: 1RJB (DFG-out FLT3 kinase domain) and PDB ID: 3LCD (DFG-in CSF-1 kinase domain). The modeled structure was able to correctly identify known DFG-in (SU11248, CEP-701, and PKC-412) and DFG-out (sorafenib, ABT-869 and AC220) FLT3 inhibitors, in docking studies. The modeled structure was then used to carry out SBVS of an HTS library of 125,000 compounds. The top scoring 97 compounds were tested for FLT3 kinase inhibition, and two hits (BPR056, IC50 = 2.3 and BPR080, IC50 = 10.7 µM) were identified. Molecular dynamics simulation and density functional theory calculation suggest that BPR056 (MW: 325.32; cLogP: 2.48) interacted with FLT3 in a stable manner and could be chemically optimized to realize a drug-like lead in the future.

Ligand efficiency based approach for efficient virtual screening of compound libraries.

Here we report for the first time the use of fit quality (FQ), a ligand efficiency (LE) based measure for virtual screening (VS) of compound libraries. The LE based VS protocol was used to screen an in-house database of 125,000 compounds to identify aurora kinase A inhibitors. First, 20 known aurora kinase inhibitors were docked to aurora kinase A crystal structure (PDB ID: 2W1C); and the conformations of docked ligand were used to create a pharmacophore (PH) model. The PH model was used to screen the database compounds, and rank (PH rank) them based on the predicted IC50 values. Next, LE_Scale, a weight-dependant LE function, was derived from 294 known aurora kinase inhibitors. Using the fit quality (FQ = LE/LE_Scale) score derived from the LE_Scale function, the database compounds were reranked (PH_FQ rank) and the top 151 (0.12% of database) compounds were assessed for aurora kinase A inhibition biochemically. This VS protocol led to the identification of 7 novel hits, with compound 5 showing aurora kinase A IC50 = 1.29 µM. Furthermore, testing of 5 against a panel of 31 kinase reveals that it is selective toward aurora kinase A & B, with <50% inhibition for other kinases at 10 µM concentrations and is a suitable candidate for further development. Incorporation of FQ score in the VS protocol not only helped identify a novel aurora kinase inhibitor, 5, but also increased the hit rate of the VS protocol by improving the enrichment factor (EF) for FQ based screening (EF = 828), compared to PH based screening (EF = 237) alone. The LE based VS protocol disclosed here could be applied to other targets for hit identification in an efficient manner.