Decrease in Brain Distribution of Fluvoxamine in Experimental Hyperlipidemic Rats
Bibliographic record
Abstract
PURPOSE: Many clinical reports and trials have suggested that fluvoxamine (FLV) reduces plasma lipoprotein levels. However, few studies have reported the effect of plasma lipoproteins on FLV pharmacokinetics. The aim of the present study was to investigate the affinities of FLV to plasma lipoproteins and the effect of plasma lipoproteins on the biodistribution of FLV using an experimental hyperlipidemic (HL) rat model. METHODS: HL rats were prepared by intraperitoneal administration of Poloxamer-407 solution (1.0 g/kg). In vitro protein binding and distribution of FLV in plasma lipoproteins were determined in control and HL rats. In vivo pharmacokinetic study (intravenous administration of FLV, 5.0 mg/kg) and biodistribution analysis for brain and liver at a steady state (infusion, 1.5 mg/kg/hr, 6 hrs) were also performed. RESULTS: The plasma protein binding of FLV was around 83% and 95% in control and HL rats, respectively, whereas the FLV recoveries in triglyceride-rich lipoprotein fractions were increased in HL. Therefore, the elevation of lipoproteins was likely responsible for the increase in protein binding in HL. After intravenous administration, the area under the plasma concentration vs. time curve (AUC) in HL was 3.9-fold greater than that in control rats, whereas the distribution ratio of FLV plasma concentration to the brain at a steady state was decreased to approximately 20% of that of the control. CONCLUSIONS: FLV has an affinity to plasma lipoproteins, and their elevation might decrease the FLV biodistribution to brain; the plasma lipoprotein levels could not be found to correlate positively with the FLV pharmacokinetic effect in brain, but rather may attenuate it.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".