Full Validation of an HPLC-UV Analytical Method for Azithromycin Quantification Using Comparative Approaches: Total Error and ISO-GUM for Assessment of Uncertainty
Bibliographic record
Abstract
BACKGROUND: Azithromycin is a complex molecule derived from erythromycin. The control of its dosage in conventional release tablets requires the analytical validation of its method to ensure accurate quantification and provide confidence in the reliability of the results for informed decision-making. OBJECTIVE: This study aims to validate an innovative method for azithromycin quantification using the accuracy profile. Additionally, a comparison is made between the uncertainty measurements calculated from the validation data using two formulas proposed by Feinberg et al. and Saffaj and Ihssane and contrasted with the ISO GUM approach. METHODS: A liquid chromatography system intended for azithromycin analysis equipped with a reversed-phase C18 stationary phase consisting of octadecyl silyl vinyl polymer in a UV detector operating at 210 nm at a temperature of 40°C in isocratic elution using a mobile phase of acetonitrile and dipotassium hydrogen phosphate buffer (6.7 g/L), in the fraction of (60:40, v/v) at pH = 8. RESULTS: The various accuracy profiles are illustrated to ensure that a known quantity of anticipated findings acquired through the method stand inside the tolerance interval of 95% and remain within the previously set acceptance limits of ±5%. Measurement uncertainty provides comparable values using both formulas of the total error approach. However, it was observed that the ISO-GUM approach tends to overestimate the expanded uncertainty. Specifically, while the ISO-GUM approach provides a rigorous framework, the use of the validation data offers a more empirical uncertainty estimation. CONCLUSION: The approach based on the total error grants the ability to accurately close the routine uncertainty, emphasizing a complete validation. HIGHLIGHTS: The proposed method is robust for pharmaceutical application, demonstrating good accuracy, with 95% of tolerance and uncertainty limits falling within the predefined acceptance limits of ±5%.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".