Population pharmacokinetics and pharmacodynamics of oral etoposide
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIMS: To study the population pharmacokinetics and pharmacodynamics of oral etoposide in patients with solid tumours. METHODS: A prospective, open label, cross-over, bioavailability study was performed in 50 adult patients with miscellaneous, advanced stage solid tumours, who were receiving oral (100 mg capsules) etoposide for 14 days and i.v. (50 mg) etoposide on day 1 or day 7 in randomised order during the first cycle treatment. Total and unbound etoposide concentration were assayed by h.p.l.c. Population PK parameters estimation was done by using the P-Pharm software (Simed). Haematological toxicity and tumour response were the main pharmacodynamic endpoints. RESULTS: Mean clearance was 1.14 l h(-1) (CV 25%). Creatinine clearance was the only covariable to significantly reduce clearance variability (residual CV 18%). (CL = 0.74 + 0.0057 CLCR; r(2) = 0.32). Mean bioavailability was 45% (CV 22%) and mean protein binding 91.5% (CV 5%). Exposure to free, pharmacologically active etoposide (free AUC p.o.) was highly variable (mean value 2.8 mg l(-1) h; CV 64%; range 0.4-9.5). It decreased with increased creatinine clearance and increased with age which accounted for 9% of the CV. Mean free AUC p.o. was the best predictor of neutropenia. Free AUC50 (exposure producing a 50% reduction in absolute neutrophil count) was 1.80 mg l(-1) h. In patients with lung cancer, the free AUC p.o. was higher in the two patients with responsive tumour (5.9 mg l(-1) h) than in patients with stable (2.1 mg l-1 h) or progressive disease (2.3 mg l-1 h) (P = 0.01). CONCLUSIONS: Exposure to free etoposide during prolonged oral treatment is highly variable and is the main determinant of pharmacodynamic effects. The population PK model based on creatinine clearance is poorly predictive of exposure. Therapeutic drug monitoring would be necessary for dose individualization or to study the relationship between exposure and antitumour effect.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it