Fragment‐Based Drug Discovery of Novel High‐affinity, Selective, and Anti‐inflammatory Inhibitors of the Keap1‐Nrf2 Protein‐Protein Interaction
Bibliographic record
Abstract
Activating the cytoprotective response of nuclear factor erythroid 2-related factor 2 (Nrf2) can reduce oxidative stress and inflammation. A promising strategy is to inhibit the protein-protein interaction between Kelch-like ECH-associated protein 1 (Keap1) and Nrf2 using noncovalent compounds that target the Keap1 Kelch domain. These compounds may be more specific than covalent Keap1-reacting Nrf2 activators. However, the development of drug-like noncovalent Keap1-Nrf2 inhibitors faces challenges due to the size and polarity of the Kelch binding pocket. Here, we present a new series of noncovalent Keap1-Nrf2 inhibitors developed from a weak fragment hit identified by crystallographic screening. A two-step growing strategy and optimization guided by several X-ray cocrystal structures led to compounds with low nanomolar affinities and complete selectivity for Keap1 in a panel of homologous Kelch domains. In cells, compounds 24 and 28 potently activated the expression of Nrf2-controlled genes and showed anti-inflammatory effects by downregulating NLRP3 inflammasome and STING signalling activation. RNA sequencing revealed activation of cytoprotective pathways and a different profile from typical covalent Nrf2 activators. This work highlights the potential of fragment-based drug discovery for challenging targets like Keap1 and introduces novel Keap1-Nrf2 inhibitors as chemical probes and drug leads.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".