Severe Impact of Hemolysis on Stability of Phenolic Compounds
Bibliographic record
Abstract
Eugénie-Raphaëlle Bérubé has obtained a Bachelor of Science in Biochemistry from Université du Québec à Montréal. She previously worked at the St-Lawrence Center of Environment, Canada, conducting biomarker analysis to measure the impact of contaminants on aquatic species. She has been working in the bioanalysis industry for the past 8 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. The presence of hemolyzed plasma samples can negatively impact preclinical and clinical sample analysis. During the method development of morphine, post-extracted instability issues were encountered in human hemolyzed plasma when compared with nonhemolyzed plasma (called normal plasma for simplicity). Investigation revealed that the presence of methemoglobin using a high pH reconstitution solution led to degradation of morphine over time. The degradation probably results from radical oxidation of the ionized phenolic group promoted by the presence of methemoglobin. Pseudomorphine, the product of oxidative dimerization of morphine, was observed as one of the degradation products in hemolyzed plasma. This hypothesis was extended to raloxifene, another phenol-containing compound. On the other hand, no instability was detected for drug products bearing a masked phenol group or carboxylic acid functionality. The issue of morphine instability was resolved by using a reconstitution solution at a pH below the pKa of the phenol moiety.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".