The Effects of Competitive Displacement on Haloperidol's Plasma Distribution in Normolipidemic and Hyperlipidemic Plasma
Bibliographic record
Abstract
PURPOSE: To assess whether dyslipidemia affects haloperidol's overall plasma distribution when it is in the presence of another highly protein bound drug that competes for plasma protein binding sites. METHODS: We performed in vitro studies in which warfarin sodium was pre-incubated in normolipidemic and hyperlipidemic plasma samples in varying concentrations. Following the pre-incubation with warfarin, [3H]-haloperidol mixed with unlabeled haloperidol was added to the plasma samples. The plasma was separated into its lipoprotein and lipoprotein deficient fractions by density gradient ultracentrifugation and haloperidol distribution was determined. RESULTS: Our results indicate that when normolipidemic plasma was pre-incubated with various concentrations of warfarin no significant redistribution of haloperidol was noted among the various plasma lipoprotein fractions. However, in the case of the hyperlipidemic plasma, pre-incubating with warfarin did result in a significant redistribution of haloperidol from the lipoprotein-deficient fraction to the very-low-density and low-density fractions of lipoproteins. CONCLUSION: Understanding how plasma lipoproteins influence competitive displacement interactions would be valuable in helping to explain and perhaps predict pharmacokinetic parameters that may affect clinical outcome. The clinical significance of competitive displacement of drugs in patients with dyslipidemia requires further study.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".