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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it