The Effect of Reporting Methods for Dosing Times on the Estimation of Pharmacokinetic Parameters of Escitalopram
Bibliographic record
Abstract
The objective of this study was to compare population pharmacokinetic models of escitalopram developed from dosage times recorded by a medication event monitoring system (MEMS) versus the reported times from patients with diagnosed depression. Seventy-three patients were prescribed doses of 10, 15, or 20 mg escitalopram daily. Sparse blood samples were collected at weeks 4, 12, 24, and 36 with 185 blood samples obtained from the 73 patients. NONMEM was used to develop a population pharmacokinetic model based on dosing records obtained from MEMS prior to each blood sample time. A separate population pharmacokinetic analysis using NONMEM was performed for the same population using the patient-reported last dosing time and assuming a steady-state condition as the model input. Objective function values and goodness-of-fit plots were used as model selection criteria. The absolute mean difference in the last dosing time between MEMS and patient-reported times was 4.48 +/- 10.12 hours. A 1-compartment model with first-order absorption and elimination was sufficient for describing the data. Estimated oral clearance (CL/F) to escitalopram was statistically insensitive to reported dosing methods (MEMS vs patient reported: 25.5 [7.0%] vs 26.9 [6.6%] L/h). However, different dosing report methods resulted in significantly different estimates on the volume of distribution (V/F; MEMS vs patient reported: 1000 [17.3%] vs 767 [17.5%] L) and the absorption rate constant K(a) (MEMS vs patient reported: 0.74 [45.7%] vs 0.51 [35.4%] h(-1)) for escitalopram. Furthermore, the parameters estimated from the MEMS method were similar to literature reported values for V/F ( approximately 1100 L) and K(a) ( approximately 0.8-0.9 h(-1)) arising from traditional pharmacokinetic approaches.
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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 | 0.218 | 0.520 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".