Potent GCN2 Inhibitor Capable of Reversing MDSC-Driven T Cell Suppression Demonstrates In Vivo Efficacy as a Single Agent and in Combination with Anti-Angiogenesis Therapy.

General control nonderepressible 2 (GCN2) protein kinase is a cellular stress sensor within the tumor microenvironment (TME), whose signaling cascade has been proposed to contribute to immune escape in tumors. Herein, we report the discovery of cell-potent GCN2 inhibitors with excellent selectivity against its closely related Integrated Stress Response (ISR) family members heme-regulated inhibitor kinase (HRI), protein kinase R (PKR), and (PKR)-like endoplasmic reticulum kinase (PERK), as well as good kinome-wide selectivity and favorable PK. In mice, compound engages GCN2 at levels ≥80% with an oral dose of 15 mg/kg BID.

ConGen-A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks.

An open challenge on the road to unraveling the brain's multilevel organization is establishing techniques to research connectivity and dynamics at different scales in time and space, as well as the links between them. This work focuses on the design of a framework that facilitates the generation of multiscale connectivity in large neural networks using a symbolic visual language capable of representing the model at different structural levels-ConGen. This symbolic language allows researchers to create and visually analyze the generated networks independently of the simulator to be used, since the visual model is translated into a simulator-independent language.

SynCoPa: Visualizing Connectivity Paths and Synapses Over Detailed Morphologies.

Brain complexity has traditionally fomented the division of neuroscience into somehow separated compartments; the coexistence of the anatomical, physiological, and connectomics points of view is just a paradigmatic example of this situation. However, there are times when it is important to combine some of these standpoints for getting a global picture, like for fully analyzing the morphological and topological features of a specific neuronal circuit. Within this framework, this article presents SynCoPa, a tool designed for bridging gaps among representations by providing techniques that allow combining detailed morphological neuron representations with the visualization of neuron interconnections at the synapse level.