Influence of Morbid Obesity on the Pharmacokinetics of Morphine, Morphine-3-Glucuronide, and Morphine-6-Glucuronide
Bibliographic record
Abstract
Obesity is associated with many pathophysiological changes that may result in altered drug metabolism. The aim of this study is to investigate the influence of obesity on the pharmacokinetics of morphine, morphine-3-glucuronide (M3G), and morphine-6-glucuronide (M6G) through a combined analysis in morbidly obese patients and non-obese healthy volunteers. In this analysis, data from 20 morbidly obese patients [mean body mass index 49.9 kg/m 2 (range 37.6–78.6 kg/m 2 ) and weight 151.3 kg (range 112–251.9 kg)] and 20 healthy volunteers [mean weight 70.6 kg (range 58–85 kg)] were included. Morbidly obese patients received 10 mg of intravenous (I.V.) morphine after gastric bypass surgery, with additional morphine I.V. doses as needed. Healthy volunteers received an I.V. bolus of morphine of 0.1 mg/kg followed by an infusion of 0.030 mg kg −1 h −1 for 1 h. Population pharmacokinetic modeling was performed using NONMEM 7.2. In morbidly obese patients, elimination clearance of M3G and M6G was decreased substantially compared with healthy volunteers ( p < 0.001). Regarding glucuronidation, only a slight decrease in the formation of M6G and a delay in the formation of M3G was found (both p < 0.001). Obesity was also identified as a covariate for the peripheral volume of distribution of morphine ( p < 0.001). Metabolism of morphine is not altered in morbidly obese patients. However, decreased elimination of both M3G and M6G is evident, resulting in a substantial increase in exposure to these two metabolites. A rational explanation of this finding is that it results from alterations in membrane transporter function and/or expression in the liver. ClinicalTrials.gov identifier: NCT01097148.
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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) | 0.000 | 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 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".