Drug discoverers Dmitri Kireev, Ph.D., and Xiaodong Wang, Ph.D., of the UNC Eshelman School of Pharmacy have created a data-driven strategy to be able to treat diseases such as acute myeloid leukemia and nonsmall cell lung cancer.
Kireev said controlling which particular members of a large protein family are targeted by a drug is key to achieving an effective therapeutic response in patients, and that’s what they have set out to accomplish.
“Structure-based design is a cornerstone of the modern drug discovery since the 1990s. However, it largely missed the big data revolution. The structure-based approach we propose distills big 3D and chemogenomic data to assemble a small-molecule drug directly inside its protein target,” Kireev said. “It will help to significantly shorten the so-called bench-to-bedside times to better serve the patients with unmet medical needs.”
The technology is based on an original concept of a FRAgment in Structural Environment (FRASE) that helps harness millions of data records from large structural and chemogenomic databases. Several FRASEs extracted from different protein-ligand complexes can be readily combined into a novel ligand for an orphan protein target.
The researchers applied FRASE-based strategy to design anti-tumor agents that selectively target the TYRO3, AXL and MERTK (TAM) family tyrosine kinases. Target engagement by the inhibitors designed led to disruption of oncogenic phenotypes as demonstrated in enzymatic assays and in a panel of cancer cell lines.
“Drug selectivity is always challenging due to possible toxicity from unwanted targets,” Wang said. “Our computational-assistant approach will help to achieve the desired selectivity profile of the targeted molecules.”
The researchers, both in the School’s Center for Integrative Chemical Biology and Drug Discovery, recently had their findings published in the Journal of the American Chemical Society.