In Silico-Based Design and In Vivo Evaluation of an Anthranilic Acid Derivative as a Multitarget Drug in a Diet-Induced Metabolic Syndrome Model
Bibliographic record
Abstract
Metabolic syndrome (MetS) is a complex disease that affects almost a quarter of the world’s adult population. In MetS, diabetes, obesity, hyperglycemia, high cholesterol, and high blood pressure are the most common disorders. Polypharmacy is the most used strategy for managing conditions related to MetS, but it has drawbacks such as low medication adherence. Multitarget ligands have been proposed as an interesting approach to developing drugs to treat complex diseases. However, suitable preclinical models that allow their evaluation in a context closer to a clinical situation of a complex disease are needed. From molecular docking studies, compound 1b, a 5-aminoanthranilic acid derivative substituted with 4′-trifluoromethylbenzylamino and 3′,4′-dimethoxybenzamide moieties, was identified as a potential multitarget drug, as it showed high in silico affinity against targets related to MetS, including PPAR-α, PPAR-γ, and HMG-CoA reductase. It was evaluated in a diet-induced MetS rat model and simultaneously lowered blood pressure, glucose, total cholesterol, and triglyceride levels after a 14-day treatment. No toxicity events were observed during an acute lethal dose evaluation test at 1500 mg/kg. Hence, the diet-induced MetS model is suitable for evaluating treatments for MetS, and compound 1b is an attractive starting point for developing multitarget drugs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".