The nuclear hormone receptor retinoic acid receptor-related orphan C2 (RORC2, also known as RORγt) is a promising target for the treatment of autoimmune diseases. A small molecule, inverse agonist of the receptor is anticipated to reduce production of IL-17, a key proinflammatory cytokine. Through a high-throughput screening approach, we identified a molecule displaying promising binding affinity for RORC2, inhibition of IL-17 production in Th17 cells, and selectivity against the related RORA and RORB receptor isoforms. Lead optimization to improve the potency and metabolic stability of this hit focused on two key design strategies, namely, iterative optimization driven by increasing lipophilic efficiency and structure-guided conformational restriction to achieve optimal ground state energetics and maximize receptor residence time. This approach successfully identified 3-cyano- N-(3-(1-isobutyrylpiperidin-4-yl)-1-methyl-4-(trifluoromethyl)-1 H-pyrrolo[2,3- b]pyridin-5-yl)benzamide as a potent and selective RORC2 inverse agonist, demonstrating good metabolic stability, oral bioavailability, and the ability to reduce IL-17 levels and skin inflammation in a preclinical in vivo animal model upon oral administration.

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This paper introduces simultaneous globally optimal hand-eye self-calibration in both its rotational and translational components. The main contributions are new feasibility tests to integrate the hand-eye calibration problem into a branch-and-bound parameter space search. The presented method constitutes the first guaranteed globally optimal estimator for simultaneous optimization of both components with respect to a cost function based on reprojection errors. The system is evaluated in both synthetic and real world scenarios. The employed benchmark dataset is published online 1 to create a common point of reference for evaluation of hand-eye self-calibration algorithms.

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