Developing FGFR4 inhibitors as potential anti-cancer agents via in silico design, supported by in vitro and cell-based testing.

Fibroblast growth factor receptor-4 (FGFR4) is a tyrosine kinase with a range of important physiological functions. However, it is also frequently mutated in various cancers and is now generating significant interest as a potential therapeutic target. Unfortunately, biochemical characterization of its role in disease, and further evaluation as a drug target is hampered by lack of a specific inhibitor. We aimed to discover new inhibitors for FGFR4 ab initio using a strategy combining in silico, in vitro and cell-based assays. We used the homologous FGFR1 to calculate docking scores of a chemically-diverse library of approximately 2000 potential kinase inhibitors. Nineteen potential inhibitors and ten randomly- selected negative controls were taken forward for in vitro FGFR4 kinase assays. All compounds with good docking scores significantly inhibited FGFR4 kinase activity, some with sub-micromolar (most potent being V4-015 with an IC(50) of 0.04 µM). Four of these compounds also demonstrated substantial activity in cellular assays using the FGFR4- overexpressing breast carcinoma cell line, MDA-MB453. Through immunoblot assays, these compounds were shown to block the phosphorylation of the FGFR4 adaptor protein, FGFR substrate protein-2α (FRS2α). The most potent compound to date, V4-015, suppressed proliferation of MDA-MB453 cells at sub-micromolar concentrations, activated the pro-apoptotic caspases 3/7 and inhibited cellular migration. While achieving complete selectivity of this compound for FGFR4 will require further lead optimization, this study has successfully identified new chemical scaffolds with unprecedented FGFR4 inhibition capacities that will support mechanism of action studies and future anti-cancer drug design.

BrainImageJ: a Java-based framework for interoperability in neuroscience, with specific application to neuroimaging.

The Human Brain Project consortium continues to struggle with effective sharing of tools. To facilitate reuse of its tools, the Stanford Psychiatry Neuroimaging Laboratory (SPNL) has developed BrainImageJ, a new software framework in Java. The framework consists of two components-a set of four programming interfaces and an application front end. The four interfaces define extension pathways for new data models, file loaders and savers, algorithms, and visualization tools. Any Java class that implements one of these interfaces qualifies as a BrainImageJ plug-in-a self-contained tool. After automatically detecting and incorporating new plug-ins, the application front end transparently generates graphical user interfaces that provide access to plug-in functionality. New plug-ins interoperate with existing ones immediately through the front end. BrainImageJ is used at the Stanford Psychiatry Neuroimaging Laboratory to develop image-analysis algorithms and three-dimensional visualization tools. It is the goal of our development group that, once the framework is placed in the public domain, it will serve as an interlaboratory platform for designing, distributing, and using interoperable tools.