Population Pharmacokinetics of Troxacitabine, a Novel Dioxolane Nucleoside Analogue
Bibliographic record
Abstract
PURPOSE: To develop and validate a population pharmacokinetic model for troxacitabine, a novel l-nucleoside analogue, administered by short infusion; to characterize clinical covariates that influence pharmacokinetic variability; and to design a dosage rate for continuous infusion administration to achieve low micromolar concentrations, which may be more efficacious than shorter infusions. EXPERIMENTAL DESIGN: Plasma samples from 111 cancer patients receiving troxacitabine (0.12-12.5 mg/m(2)) as a 30-minute infusion in phase I trials were used to develop the model with NONMEM. Clinical covariates evaluated included creatinine clearance, body surface area, age, and sex. From the model, a troxacitabine dosage rate of 2.0 to 3.0 mg/m(2)/d was expected to achieve a target concentration of 0.1 micromol/L; plasma samples were obtained during the infusion from eight patients receiving troxacitabine as a 3-day infusion. RESULTS: Troxacitabine pharmacokinetics were characterized by a three-compartment linear model. The mean value for systemic clearance [interindividual variability (CV%)] from the covariate-free model was 9.1 L/h (28%). Creatinine clearance and body surface area accounted for 36% of intersubject variation in clearance. Troxacitabine 2.0 mg/m(2)/d (n = 3) and 3.0 mg/m(2)/d (n = 5) for 3 days produced mean +/- SD end of infusion concentrations of 0.12 +/- 0.03 and 0.15 +/- 0.03 micromol/L, respectively. CONCLUSIONS: Renal function and body surface area were identified as sources of troxacitabine pharmacokinetic variability. The population pharmacokinetic model model-derived dosage rates for continuous infusion administration successfully achieved predetermined target plasma concentrations. The present model may be used to optimize treatment with troxacitabine by developing a dosing strategy based on both renal function and body size.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| 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; 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".