Determination and validation of tiaprofenic acid in human plasma: A detailed LC-MS/MS-based analysis following ICH M10 guidelines and the accuracy profile approach
Bibliographic record
Abstract
The validation of bioanalytical methods holds critical importance for regulatory agencies and organizations dedicated to ensuring the safety, efficacy, and quality of pharmaceuticals. In this context, the recent release of the ICH M10 guideline in May 2022 represents a significant milestone in standardizing bioanalytical method validation globally. However, this guideline lacks explicit experimental protocols for implementation. In this study, we address the practical implementation of the newly released ICH M10 guideline by providing a detailed validation protocol for a bioanalytical method. Our method specifically targets tiaprofenic acid, a widely used nonsteroidal anti-inflammatory drug. Tiaprofenic acid is a critical component in bioequivalence studies, underscoring the necessity for precise and accurate quantification within complex biological matrices. The integration of the accuracy profile approach, a statistical tool, enhances the significance of this work. This approach aids in assessing the accuracy and precision of bioanalytical methods, establishing confidence intervals around measured concentrations, and quantifying the level of accuracy and precision expected when using the validated method.
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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 | 0.006 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".