Discovery of Selective Nrf2 Activators from Natural Products: A Computational Screening Approach to Minimize Off-Target Effects on PXR and CYP2D6

Nuclear factor erythroid 2-related factor 2 (Nrf2) is a central regulator of cellular antioxidant responses and a highly promising therapeutic target for a range of oxidative stress-related diseases. However, the clinical translation of Nrf2 activators has been hampered by significant off-target effects—notably unintended activation of the pregnane X receptor (PXR) and inhibition of cytochrome P450 2D6 (CYP2D6)—which can lead to dangerous drug-drug interactions and metabolic complications. To overcome this critical barrier, we conducted the first large-scale computational screening of 628,898 natural products from the COCONUT database, integrating molecular docking with a rigorous three-tier selectivity strategy designed to prioritize compounds that strongly bind KEAP1 (the primary Nrf2 repressor) while minimizing interactions with PXR and CYP2D6. Our innovative approach identified 10 ultraselective candidates that demonstrate potent KEAP1 affinity, negligible PXR engagement, and only moderate CYP2D6 binding—achieving up to 12.29-fold selectivity for Nrf2 pathway activation. These top hits are structurally novel, enriched in lipid-like and nucleoside-inspired scaffolds, and exhibit promising drug-like properties. By providing both a curated set of chemically diverse, selectivity-optimized leads and a publicly accessible screening dataset, this work establishes a new foundation for the rational development of safer, more precise Nrf2-targeted therapies, bridging a crucial gap between target potential and clinical viability. By prioritizing compounds with minimal off-target effects on PXR and CYP2D6, our approach offers a scalable template for reducing drug development failures and advancing safer therapeutics for oxidative stress-related diseases.