Synthesis and preclinical testing of a selective beta-subtype agonist of thyroid hormone receptor ZTA-261
Bibliographic record
Abstract
Abstract Background Thyroid hormones (TH) regulate the basal metabolic rate through their receptors THRα and THRβ. TH activates lipid metabolism via THRβ, however, an excess amount of TH can lead to tachycardia, bone loss, and muscle wasting through THRα. In recent years, TH analogs that selectively bind to THRβ have gained attention as new agents for treating dyslipidemia and obesity, which continue to pose major challenges to public health worldwide. Methods We developed a TH analog, ZTA-261, by modifying the existing THRβ-selective agonists GC-1 and GC-24. To determine the THRβ-selectivity of ZTA-261, an in vitro radiolabeled TH displacement assay was conducted. ZTA-261 was intraperitoneally injected into a mouse model of high-fat diet-induced obesity, and its effectiveness in reducing body weight and visceral fat, and improving lipid metabolism was assessed. In addition, its toxicity in the liver, heart, and bone was evaluated. Results ZTA-261 is more selective towards THRβ than GC-1. Although ZTA-261 is less effective in reducing body weight and visceral fat than GC-1, it is as effective as GC-1 in reducing the levels of serum and liver lipids. These effects are mediated by the same pathway as that of T3, a natural TH, as evidenced by similar changes in the expression of TH-induced and lipid metabolism-related genes. The bone, cardiac, and hepatotoxicity of ZTA-261 are significantly lower than those of GC-1. Conclusions ZTA-261, a highly selective and less toxic THRβ agonist, has the potential to be used as a drug for treating diseases related to lipid metabolism.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".