Selective Glucocorticoid Receptor Modulators of Immune Checkpoint Function
Robin R. KobylskiiD, Charles K. MiniD, Jerome C. NwachukwuiD, Marcel KoenigiD, Haotian Zhang, Lizbeth Moncada, Thomas VenablesiD, Bin-Bin SchelliD, William J. SteiniD, Claudia McCowniD, Justin ShumateiD, Emilie BonillaiD, Amin SobhiD, Arantxa Romero-Toledo, Matthew E. PipkiniD, Michalina JaniszewskaiD, Louis D. ScampaviaiD, Timothy P. SpiceriD, Jonathan D. LichtiD, Inez RogatskyiD, Theodore M. KameneckaiD, Laura A. SoltiD, K.W. NettlesiD
Abstract
Glucocorticoids (GCs) coordinate immunity, inflammation, and metabolism through allosteric regulation of the glucocorticoid receptor (GR) transcription factor. GCs are indispensable anti-inflammatory drugs yet linking specific ligand-receptor structural states to specific biological outcomes has remained a major barrier to designing safer, more selective therapies. Using structure-based design, we developed selective glucocorticoid receptor modulators (SGRMs) of immune function by extending a steroidal scaffold from the ligand-binding pocket into an adjacent solvent channel. These SGRMs suppressed T cell pro-inflammatory cytokines and promoted differentiation of memory precursor T cells while showing minimal induction of M2 macrophage polarization or T cell checkpoint proteins PD-1 and CTLA-4, all key targets of immunotherapy. Molecular dynamics simulations revealed that solvent-channel substituents function as a lever arm to drive dynamic oscillations in the steroid core, thereby allosterically tuning GR activity states. Systematic perturbation of immune cells with a graded series of ligands enabled a ligand perturbation with machine learning (LPML) framework to map coregulated responses across cell types and identified effector T cell gene networks tightly coupled with immune checkpoint induction. This approach outlines a general strategy for decoding the logic of allosteric drug action, enabling the rational design of SGRMs with tailored immunomodulatory profiles.
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