Impact of Sample Hemolysis on Drug Stability in Regulated Bioanalysis
Bibliographic record
Abstract
Eugénie-Raphaëlle Bérubé obtained a Bachelor of Science in Biochemistry from University du Québec à Montreal. She previously worked at the St-Lawrence Center of Environment Canada, conducting biomarker analysis to measure the impact of contaminants on the aquatic species. She has been working in the bioanalysis industry for the past 7 years at Algorithme Pharma, a CRO located in Laval, Canada, becoming a scientist in bioanalytical method development for the quantitation of pharmaceuticals in biological fluids. Marie-Pierre Taillon holds a Bachelor of Science in Biochemistry. She is a senior scientist in method development at Algorithme Pharma; she has been working in the bioanalysis industry for the past 11 years where she became an expert in method development, specifically in the LC–MS/MS field. Her experiences have led her to conduct robust and effective method development of bioanalytical assays. Being regulated by agencies’ guidances, the importance of a robust validated bioanalytical method is crucial as it may impact the validity of the pharmacokinetic data generated. During blood collection and processing, the presence of hemolyzed plasma samples may occur and as a result its impact must be investigated to ensure method robustness. Indeed, hemolyzed samples may affect the analyte recovery efficiency, as well as the chromatography. Furthermore, the stability of an analyte in hemolyzed plasma can be an issue as analyte degradation may occur. In this article we report two case studies where the analyte instability was a result of sample hemolysis. A description of the appropriate actions undertaken for the resolution of the issue will be discussed.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".