Development and validation of a reversed-phase HPLC method for determination of assay content of Teriflunomide by the aid of BOMD simulations
Bibliographic record
Abstract
Due to the new hopes for treatment of multiple sclerosis (MS) diseases by Teriflunomide (TFN), in this project, a cheap, robust, and fully validated method has been developed both for determination of assay content in API (active pharmaceutical ingredient), and for related impurities analysis (RIA). To operate the method, a common C18, end-capped (250 × 4.6) mm, 5µm liquid chromatography column, was applied. The mobile phase A was prepared by dissolving 2.74 g (20mM) of PDP (potassium dihydrogen phosphate) and 3.72 g (50mM) of PC (potassium chloride) in water (1000 mL). Then, pH was adjusted to 3.0 by adding OPA (ortho-phosphoric acid) 85%; while, the mobile phase B was acetonitrile (ACN) (100%). In order to confirm the experimental data about the λmax of TFN, we have used the Born-Oppenheimer molecular dynamics (BOMD) simulations, quantum mechanics (QM), and TD-DFT calculations. According to the results, the method showed a high level of suitability, specificity, linearity, accuracy, precision, repeatability, robustness, and reliable detection limit.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".