Competitive Displacement of Clozapine from Plasma Proteins in Normolipidemic and Hyperlipidemic Plasma Samples: Clinical Implications
Bibliographic record
Abstract
OBJECTIVE: This study assesses whether competitive displacement of clozapine by warfarin affects clozapine's overall plasma distribution. METHODS: Warfarin sodium was preincubated in normolipidemic and hyperlipidemic plasma samples in varying concentrations. Following the preincubation with warfarin, [3H]clozapine mixed with unlabeled clozapine was added to the plasma samples. The plasma was separated into its lipoprotein and lipoprotein-deficient fractions by density gradient ultracentrifugation, and clozapine distribution was determined. RESULTS: When normolipidemic plasma was preincubated with various concentrations of warfarin, no significant redistribution of clozapine was noted among the various plasma lipoprotein fractions. However, in the case of the hyperlipidemic plasma, preincubating with warfarin did result in a significant redistribution of clozapine from the lipoprotein-deficient fraction to the very-low-density and low-density fractions of lipoproteins. Based on pharmacokinetic principles, the steady-state unbound concentration of clozapine in normolipidemic and hyperlipidemic plasma is not expected to change. CONCLUSION: Although no change in the steady-state unbound (active) concentration of clozapine would predict no change in clinical status, it is possible that this may only apply to the individuals with a normal lipid profile. We believe clozapine's association with lipoproteins (particularly triglycerides) may actually increase clozapine's effectiveness.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".