Characterization of the 5‐HT<sub>2C</sub> receptor agonist lorcaserin on efficacy and safety measures in a rat model of diet‐induced obesity
Bibliographic record
Abstract
The 5-HT2C receptor agonist lorcaserin (Belviq®) has been Food and Drug Administration (FDA) approved for the treatment of obesity. The present study is a back translational investigation into the effect of 28-day lorcaserin treatment in a diet-induced obesity (DIO) model using male, Sprague-Dawley rats. An assessment of drug effect on efficacy and multiple safety endpoints including cardiac function was undertaken. Lorcaserin (1-2 mg/kg SC b.i.d.) significantly reduced percentage body weight gain compared to vehicle-treated controls (VEH: 10.6 ± 0.4%; LOR 1: 7.6 ± 1.2%; LOR 2: 5.4 ± 0.6%). Measurement of body composition using quantitative magnetic resonance (QMR) imaging indicated this change was due to the selective reduction in body fat mass. Modest effects on food intake were recorded. At the completion of the treatment phase, echocardiography revealed no evidence for valvulopathy, that is, no aortic or mitral valve regurgitation. The pharmacokinetics of the present treatment regimen was determined over a 7-day treatment period; plasma C min and C max were in the range 13-160 ng/mL (1 mg/kg b.i.d.) and 34-264 ng/mL (2 mg/kg b.i.d.) with no evidence for drug accumulation. In sum, these studies show an effect of lorcaserin in the DIO model, that in the context of the primary endpoint measure of % body weight change was similar to that reported clinically (i.e., 3.0-5.2% vs. 3.2%). The present studies highlight the translational value of obesity models such as DIO, and suggest that assuming consideration is paid to nonspecific drug effects such as malaise, the DIO model has reasonable forward translational value to help predict clinical outcomes of a new chemical entity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".