Studies To Examine Potential Tolerability Differences between the 5-HT<sub>2C</sub>Receptor Selective Agonists Lorcaserin and CP-809101
Bibliographic record
Abstract
Lorcaserin (LOR) is a selective 5-HT 2C receptor agonist that has been FDA approved as a treatment for obesity. The most frequently reported side-effects of LOR include nausea and headache, which can be dose limiting. We have previously reported that in the rat, while LOR produced unconditioned signs characteristic of nausea/malaise, the highly selective 5-HT 2C agonist CP-809101 (CP) produced fewer equivalent signs. Because this may indicate a subclass of 5-HT 2C agonists having better tolerability, the present studies were designed to further investigate this apparent difference. In a conditioned gaping model, a rodent test of nausea, LOR produced significantly higher gapes compared to CP consistent with it having higher emetogenic properties. Subsequent studies were designed to identify features of each drug that may account for such differences. In rats trained to discriminate CP-809101 from saline, both CP and LOR produced full generalization suggesting a similar interoceptive cue. In vitro tests of functional selectivity designed to examine signaling pathways activated by both drugs in CHO (Chinese hamster ovary) cells expressing h5-HT 2C receptors failed to identify evidence for biased signaling differences between LOR and CP. Thus, both drugs showed similar profiles across PLC, PLA 2, and ERK signaling pathways. In studies designed to examine pharmacokinetic differences between LOR and CP, while drug plasma levels correlated with increasing dose, CSF levels did not. CSF levels of LOR increased proportionally with dose; however CSF levels of CP plateaued from 6 to 12 mg/kg. Thus, the apparently improved tolerability of CP likely reflects a limit to CNS levels attained at relatively high doses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".