Comments on “Effects of Obesity and Leptin Deficiency on Morphine Pharmacokinetics in a Mouse Model” by Dalesio et al, Anesth Analg. 2016;123:1611–1617
Bibliographic record
Abstract
To the Editor We read with great interest the article by Dalesio et al1 describing effects of obesity and leptin deficiency on morphine pharmacokinetics. We have some concerns we would like to share. The smallest volume of distribution possible for a drug is plasma volume (in mice2 approximately 67 μL/g) but here Vd/F of 2.9 to 4.8 pL/g were reported. By digitizing Figure 1, we found the test groups to give area under curve for 150 minutes of blood sampling time (AUC150) values that matched the data in Table 2 (<2% difference). However, the AUC150 for reference-control wild type (WT) given in Table 2 were >60% higher than the Figure 1 data. Apparently, the Figure 1 data are showing unadjusted concentrations for the WT group, making differences from WT appear bigger than they were (dose-adjusted concentrations would have been preferred). We note as well that the (x, y) zero intercepts in Figure 1 show that morphine was present in several groups of mice at time zero, which seems odd considering single doses were given. Our analysis of Figure 1 provided t½ very similar to those reported for the test groups.1 In dividing the C150 by 0.693/t½ (β) and adding to it the AUC150, we obtained AUC0–∞ values. Then, using Dose/AUC0–∞, we arrived at reasonable intraperitoneal dosed-CL/F of 79.5, 69.4, and 38.8 μL/min/g, respectively, for the LR, DIO, and ob/ob groups.3 Using (CL/F)/β, we obtained realistic Vdβ/F values of 3.8, 3.4, and 2.9 mL/g, respectively.3 This was despite the unusual manner of blood sampling, ie, repeatedly anesthetizing the mice 7 to 8 times over 2.5 hours. We also note that AUC units in Table 2 do not match those in Table 1. It would have been nice to have seen the concentration versus time data and AUC for metabolite morphine 3-glucuronide as has been presented before for lean mice.3 Under “Protocol,”1 morphine was stated to be dosed using ideal body weight. A citation specific for morphine was lacking. The references were for fentanyl-like opioids. After subcutaneous injection, morphine Cmax increases linearly from 20 to 150 mg/kg.4 Here, WT mice received doses intraperitoneal from 20 to 100 mg/kg but vagueness surrounds the description of data handling. Dalesio et al1alluded that there were no pharmacokinetic differences (data undisclosed) existing between doses of 20 and 80 mg/kg (after or before dose adjustment?) but that there were at 100 mg/kg. Thus, they combined the 20 to 80 mg/kg data for their critically important WT reference group. However, the nature of the dose versus AUC and Cmax response in the other mice is unknown, which makes compiling of WT pharmacokinetic data difficult to rationalize. Indeed, why exclude the 100 mg/kg WT as they were closer to the dose used in the treatment groups than 20 and 40 mg/kg WT? Considering the rise in Cmax after subcutaneous doses of 20 to 150 mg/kg,4 how much and why the 100 mg/kg dose differed from 80 mg/kg deserves explanation. Lastly, the abstract said the metabolite/drug ratios indicated differences in morphine metabolism in obesity. This is possible but not definitive because metabolite elimination also affects this ratio. Special studies are needed to confirm this conclusion.5 Dion R. Brocks, PhDNeal M. Davies, PhDFaculty of Pharmacy and Pharmaceutical SciencesUniversity of AlbertaEdmonton, Alberta, Canada[email protected]
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.022 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".