Selective Glucocorticoid Receptor Modulators of Immune Checkpoint Function
Kobyski, R. R.; Min, C. K.; Nwachukwu, J. C.; Koenig, M.; Zhang, H.; Moncada, L.; Venables, T. T.; Schell, B.-B.; Stein, W. J.; McCown, C.; Shumate, J.; Bonilla, E.; Sobh, A.; Romero-Toledo, A.; Pipkin, M. E.; Janiszewska, M.; Scampavia, L.; Spicer, T. P.; Licht, J. D.; Rogatsky, I.; Kamenecka, T. M.; Solt, L.; Nettles, K. W.
Show 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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