Discovery of BE2012, a First-in-Class REV-ERBα/β Antagonist with Favorable Selectivity and Pharmacokinetics, and In Vivo Efficacy in Inducing Key Myogenic Factors for Muscle Repair upon Acute Muscle Injury
Bibliographic record
Abstract
REV-ERBα is a nuclear receptor transcriptional repressor involved in circadian rhythm, metabolism, inflammation, and myogenesis. Antagonizing REV-ERBα has emerged as a promising therapeutic strategy, yet few compounds with favorable pharmacokinetic profiles have been identified since SR8278. Here, we report the discovery and optimization of BE2012, a 3-aminoquinazolinone antagonist identified through high-throughput screening and refined via systematic structure–activity relationship studies. BE2012 exhibited potent REV-ERBα antagonism (EC 50 = 0.285 μM), high nuclear receptor selectivity, minimal CNS off-target interactions, and improved ADME and pharmacokinetic properties, including a 22-fold longer half-life ( t 1 / 2 = 3.79 h) than SR8278. Molecular modeling revealed key hydrophobic and hydrogen-bonding interactions within the REV-ERBα ligand-binding pocket that stabilize BE2012 and explain its enhanced potency. In a murine model of acute muscle injury, BE2012 upregulated myogenic transcription factors and promoted muscle repair. Collectively, BE2012 represents a selective, pharmacokinetically favorable REV-ERBα/β antagonist with therapeutic potential in muscle regeneration and related diseases.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".