Prediction of In Vivo Atenolol Removal by High-Permeability Hemodialysis Based on an In Vitro Model
Bibliographic record
Abstract
PURPOSE: In order to update our data on drug dialyzability using the high-permeability dialysis membranes, atenolol elimination by an in vitro dialysis model was compared to that observed in six patients during high-permeability hemodialysis (HD), and the predictive value of the model was evaluated. METHODS: Atenolol clearance was evaluated in six patients undergoing chronic HD. They were considered as eligible candidates if they were between 18 and 80 years of age, had a body mass index between 19 and 30 kg/m2, underwent HD and were taking atenolol on a regular basis in oral tablet form for at least 1 month before the study started. Atenolol clearance was also evaluated in three in vitro dialysis sessions with high-permeability polysulfone membrane. Atenolol was dissolved in 6 L of Krebs-Henseleit buffer with bovine serum albumin. Dialysis parameters were set to mirror as much as possible the patients' parameters (flow rate: 300 mL/min, dialyzate flow: 500 mL/min). After sample collection, drug concentrations were measured with high performance liquid chromatography. The comparison between in vivo and in vitro atenolol elimination kinetics was performed by drawing the curve fittings of concentrations vs. time on SigmaPlot 12, and adding a 95% prediction interval to each elimination curve fitting. RESULTS: Mean dialysis clearance of atenolol in vitro and in vivo was 198 ± 4 and 235 ± 53 mL/min, respectively. Atenolol was significantly removed within the study time period in both in vitro and in vivo experiments. By the end of in vitro dialysis, atenolol remaining in the drug reservoir was less than 2% of initial arterial concentration. CONCLUSION: Our study has indicated that atenolol is almost entirely cleared during high-permeability hemodialysis. Furthermore, the in vitro prediction interval of the drug elimination curve fitting could forecast its in vivo elimination especially at the end of dialysis.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".