HPLC-UV Method Development and Validation for Vitamin D<sub>3</sub> (Cholecalciferol) Quantitation in Drugs and Dietary Supplements
Bibliographic record
Abstract
Introduction. An inadequate diet and living in the northern regions can lead to a lack of vitamin D3 and the development of diseases, including a decrease in immunity. To compensate for the lack of vitamin D, vitamin drugs are used that contain vitamin D in one of its active forms (usually in the form of cholecalciferol, vitamin D3).Aim. To develop and validate HPLC-UV method for the determination of vitamin D3 in vitamin drugs and to evaluate the content of cholecalciferol in selected drugs anddietary supplements presented in the Russian Federation.Materials and methods. Determination of vitamin D3 was carried out by HPLC with UV detection at a wavelength 266 nm. Sample preparation of vitamin drugs was carried out by extraction with methanol (for liquid dosage forms based on aqueous or triglyceride solutions) and extraction with an aqueous-methanol solution (for solid dosage forms based on water-soluble substances with vitamin D3) in a ratio of 2 to 8 (water-methanol).Results and discussions. The analysis methodology for the parameter "Vitamin D3 (cholecalciferol) content" in vitamin dosage forms by HPLC was validated according to the following validation parameters: specificity; accuracy; precision; linearity; range.Conclusion. The analysis methodology for the parameter "Vitamin D3 (cholecalciferol) content" in vitamin dosage forms by HPLC was developed. The method was validated according to the following validation parameters: specificity; accuracy; precision; linearity; range. The range of the method was 9,5–38 μg/ml. The method was used to determine vitamin D3 in vitamin drugs based on water-soluble forms of vitamin D3, in the form of aqueous solutions and form of fatty acids triglyceridessolutions.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".