Design and scale‐up of an alkylated Minisci reaction to produce ethionamide with 4‐cyanopyridine as raw materials
Bibliographic record
Abstract
Abstract Tuberculosis is a serious bacterial infection in developing countries, especially in areas with drug resistance. The development of the anti‐tuberculosis drug ethionamide (ETA) is therefore particularly important. We have designed a new route for ETA synthesis and considered the drug's amenability to large‐scale manufacture. We evaluated different routes for the synthesis of ETA and selected the most suitable process for the industrial production of this material, which involved the Minisci reaction of 4‐cyanopyridine with propionic acid using a AgNO3/(NH4)2S2O8 catalyst system under acidic conditions. The effects of different parameters on the reaction were studied, and the production of the intermediate of 2‐ethyl‐4‐cyanopyridine was twice recrystallized from methanol to obtain pure ETA. In the Minisci reaction, the system was stirred for an additional 20 min at 80 °C after the addition of ammonium persulphate solution. The subsequent formation of thioamide was conducted at 40 °C to 60 °C for 1.5 h with a 1:5:2 molar ratio of 4‐cyanopyridine, propionic acid, and ammonium sulphide. The purity of the crude ETA reached 98.77 % after two recrystallization steps. This process could be scaled up on a much larger scale (1 kg), leading to the yield of alkylation reaction and overall yield of the production process as 44.06 % and 31.26 %, respectively.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".