A nitroalkene derivative of salicylate, SANA, induces creatine-dependent thermogenesis and promotes weight loss
Bibliographic record
Abstract
The emergence of glucagon-like peptide-1 agonists represents a notable advancement in the pharmacological treatment of obesity, yet complementary approaches are essential. Through phenotypic drug discovery, we developed promising nitroalkene-containing small molecules for obesity-related metabolic dysfunctions. Here, we present SANA, a nitroalkene derivative of salicylate, demonstrating notable efficacy in preclinical models of diet-induced obesity. SANA reduces liver steatosis and insulin resistance by enhancing mitochondrial respiration and increasing creatine-dependent energy expenditure in adipose tissue, functioning effectively in thermoneutral conditions and independently of uncoupling protein 1 and AMPK activity. Finally, we conducted a randomized, double-blind, placebo-controlled phase 1A/B clinical trial, which consisted of two parts, each with four arms: (A) single ascending doses (200–800 mg) in healthy lean volunteers; (B) multiple ascending doses (200–400 mg per day for 15 days) in healthy volunteers with overweight or obesity. The primary endpoint assessed safety and tolerability. Secondary and exploratory endpoints included pharmacokinetics, tolerability, body weight and metabolic markers. SANA shows good safety and tolerability, and demonstrates beneficial effects on body weight and glucose management within 2 weeks of treatment. Overall, SANA appears to be a first-in-class activator of creatine-dependent energy expenditure and thermogenesis, highlighting its potential as a therapeutic candidate for ‘diabesity’. Australian New Zealand Clinical Trials Registry registration: ACTRN12622001519741 . In this study, the authors describe SANA, a nitroalkene derivative of salicylate, as a potential activator of creatine-dependent energy expenditure and thermogenesis in adipose tissue. Preclinical and clinical data from this paper also suggest that SANA improves glucose homeostasis and promotes weight loss in mice and humans.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".