Antidiabetic, antioxidant and cytotoxicity activities of <i>ortho</i>- and <i>para</i>-substituted Schiff bases derived from metformin hydrochloride: Validation by molecular docking and <i>in silico</i> ADME studies
Bibliographic record
Abstract
Abstract This work evaluates the in vitro antioxidant and antidiabetic activities of two metformin hydrochloride-based Schiff bases. Moreover, the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay was used to examine the in vitro cytotoxic effects of HL1 and HL2 on the A549 lung cancer cell line. The two Schiff bases that have been previously synthesized by using two effective, green techniques, namely stirring and microwave-assisted, are N,N-dimethyl-N′-[(Z)-(2-nitrophenyl) methylidene] imidodicarbonimidic diamide and N,N-dimethyl-N′-[(Z)-(4-nitrophenyl) methylidene] imidodicarbonimidic diamide, indicated by HL1 and HL2, respectively. Studies of antidiabetic efficacy using alpha-amylase revealed that HL2 has a higher inhibition than HL1, but the results on sucrase enzyme showed that HL1 had the highest inhibitory action, whereas the outcome of the antioxidant test with the 2,2-diphenyl-1-picrylhydrazyl assay demonstrated that HL2 was the most effective antioxidant, followed by ascorbic acid and HL1. In the MTT assay, HL1 had the best result, with an IC50 value of 57.13 µg/mL compared to HL2 with an IC50 value of 76.83 µg/mL. It was observed that HL1 was the most effective against the human lung cancer cell line A459. The findings were supported by computational and pharmacokinetic studies (SwissADME). Based on empirical and computational studies, we suggest that HL1 and HL2 are promising candidates as antioxidants and antidiabetics after being examined in vivo.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".