- Type II kinase inhibitors include a number of drugs showing promise in both the clinic and trials
- Developing additional Type II kinase inhibitors will likely improve the quality of the current anticancer drug cache
Molecular factors that determine why important classes of cancer drugs are specific for their kinases are providing a rich data resource that can be used to create new, clinically useful kinase inhibitors—ultimately supporting precision treatment for patients.
Research published in the Journal of Medicinal Chemistry represents an interdisciplinary collaboration between Roland L. Dunbrack, Jr., PhD, and Ronald Levy, PhD, from Fox Chase Cancer Center’s Molecular Therapeutics Program, and Jeffrey Peterson, PhD, from the Biology Program.
These scientists used a “big data” approach, mining structural information for 2,500 kinases, including many cancer-associated oncogenes, from the Protein Data Bank. Kinase structures are typically classified as “DFG-in” or “DFG-out,” named for the position of the DFG sequence motif at the start of the activation loop—a part of the protein that is essential for its activity and often targeted by drugs. Dunbrack’s group determined that only a certain subset of the DFG-out structures bind Type II inhibitors.
Type II kinase inhibitors include a number of drugs showing considerable promise, such as tivozanib and foretinib, making it essential to fully understand their action. A Type II inhibitor occupies both the ATP pocket and a second pocket formed when the activation loop moves away from an active position. The structural analysis by Dunbrack and Levy determined that Type II inhibitors do not bind to all DFG-out structures, but only those with the activation loop in a very precise position, which they dubbed “classical DFG-out.” Kinases falling into this category include a number of important oncogenes, such as ALK, AKT2, FAK, and KIT.
Peterson and Levy sought to validate predictions for kinase inhibitor specificity using public databases but found that few structurally validated Type II inhibitors were also represented in publicly available biochemical profiling studies of kinase inhibitors. Hence, Peterson’s group generated kinase inhibition profiling of several additional Type II inhibitors, including 9 clinical candidates, and found that Type II inhibitors are typically more selective than other classes of kinase inhibitors.
The new study emphasizes that out of 518 defined human kinases, at least 45 are likely excellent candidates for development of Type II inhibitors—many of these have not been previously targeted. “We hope that the development of more Type II kinase inhibitors will improve the quality of our arsenal of anticancer drugs”, says Peterson.