The application of Bayesian forecasting to explore the effects of sex and high-fat diet on the pharmacokinetics of ropivacaine in the rat
Bibliographic record
Abstract
Bayesian forecasting is commonly applied as part of therapeutic drug monitoring to obtain individual estimates of pharmacokinetic parameters in patients. Here its utility was explored in a preclinical study involving ropivacaine, in which sparse blood sampling data was available in the rat. Initially sample-population estimates of parameters were obtained by injecting male cannulated male Sprague-Dawley rats subcutaneously with ropivacaine HCl. Blood samples were serially drawn from each rat for 12 h after the dose (rich sampling); the concentrations were used with compartmental analysis to optimize model selection and obtain mean and variances of pharmacokinetic parameters. Two additional single doses, spaced by 5 days, were injected, each followed by 1 to 3 sparse blood draws. Other sparsely sampled age-matched groups of male and female rats given standard diet, and a group of males given high-fat diet, were dosed. Bayesian forecasting was conducted for each of these sparsely sampled rats to estimate pharmacokinetic parameters. Plasma was assayed using liquid-chromatographic method using mass spectrometry. For validation, the Bayesian parameter forecasts were compared to those using nonlinear mixed-effects modelling (NLMEM). Ropivacaine had a high clearance compared to hepatic blood flow, and a large volume of distribution. Excellent correlations were present between observed and estimated plasma concentrations using Bayesian forecasting, as was the relationship between those estimates and those obtained from NLMEM. The male rats given high-fat diet had a significant decrease in the weight-normalized clearance of ropivacaine, and female rats had a slower absorption rate. The effects were also identified using NLMEM. Bayesian forecasting has applicability in estimating pharmacokinetic properties of drugs in preclinical studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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 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".