Pharmacologic Profile of Naloxegol, a Peripherally Acting µ-Opioid Receptor Antagonist, for the Treatment of Opioid-Induced Constipation
Bibliographic record
Abstract
Opioid-induced constipation (OIC) is a common side effect of opioid pharmacotherapy for the management of pain because opioid agonists bind to µ-opioid receptors in the enteric nervous system (ENS). Naloxegol, a polyethylene glycol derivative of naloxol, which is a derivative of naloxone and a peripherally acting µ-opioid receptor antagonist, targets the physiologic mechanisms that cause OIC. Pharmacologic measures of opioid activity and pharmacokinetic measures of central nervous system (CNS) penetration were employed to characterize the mechanism of action of naloxegol. At the human µ-opioid receptor in vitro, naloxegol was a potent inhibitor of binding (Ki = 7.42 nM) and a neutral competitive antagonist (pA2 - 7.95); agonist effects were <10% up to 30 μM and identical to those of naloxone. The oral doses achieving 50% of the maximal effect in the rat for antagonism of morphine-induced inhibition of gastrointestinal transit and morphine-induced antinociception in the hot plate assay were 23.1 and 55.4 mg/kg for naloxegol and 0.69 and 1.14 mg/kg by for naloxone, respectively. In the human colon adenocarcinoma cell transport assay, naloxegol was a substrate for the P-glycoprotein transporter, with low apparent permeability in the apical to basolateral direction, and penetrated the CNS 15-fold slower than naloxone in a rat brain perfusion model. Naloxegol-derived radioactivity was poorly distributed throughout the rat CNS and was eliminated from most tissues within 24 hours. These findings corroborate phase 3 clinical studies demonstrating that naloxegol relieves OIC-associated symptoms in patients with chronic noncancer pain by antagonizing the µ-opioid receptor in the ENS while preserving CNS-mediated analgesia.
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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) | 0.000 | 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) | 0.002 | 0.001 |
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".