Synthesis of nonsteroidal anti-inflammatory drug (NSAID) 2,4,5-trimethoxybenzaldehyde from Indonesian calamus oil and its in silico pharmacokinetic study
Bibliographic record
Abstract
There is a race to search for nonsteroidal anti-inflammatory painkillers due to the escalating cases of the life-threatening COVID-19 pandemic. Those current nonsteroidal anti-inflammatory drugs (NSAIDs) used as an inflammation adjunct treatment on COVID-19 patients, including paracetamol, ibuprofen, and celecoxib, are still under dispute offering emergency development of a new potent NSAID. Meanwhile, a well-known COX-2 selective anti-inflammation 2,4,5-trimethoxybenzaldehyde (TMBA) has not been developed further in terms of its synthetic methodology and its pharmacokinetic studies. Herein, the synthesis of 2,4,5-TMBA from Indonesia sweet flag ( Acorus calamus) and its pharmacokinetic properties was studied through in silico calculation. A typical Asian tetraploid calamus oil was yielded ( 90% pure) after reduced pressure distillation of the crude Indonesian sweet flag oil. Submission of that oil into a very cheap DIY ozone machine produced 95% of pure 2,4,5-TMBA just in 10 min ozonised. The in silico adsorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction using the free access ADMETlab 2.0 web server strongly recommended 2,4,5-TMBA to be an orally administered NSAID candidate.
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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) | 0.003 | 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".