Development and Validation of a Test Dose Strategy for Once-Daily i.v. Busulfan: Importance of Fixed Infusion Rate Dosing
Bibliographic record
Abstract
Intravenous (i.v.) busulfan (Bu) administered once daily in myeloablative transplant regimens is convenient, effective, and relatively well tolerated. Therapeutic drug monitoring is recommended as nonrelapse mortality increases when daily exposure, as determined by the area under the plasma concentration versus time curve (AUC), exceeds 6000 μM·min. We describe sequential studies to achieve accurate prediction of treatment doses of Bu based on the kinetics of a smaller test dose. A total of 335 patients with hematologic malignancies were given daily i.v. Bu 3.2 mg/kg × 4 and fludarabine 50 mg/m(2) × 5. Pharmacokinetic monitoring was conducted for both the test dose and first treatment dose of Bu (day -5). Three different test dose schedules were evaluated: 12 mg Bu administered over 20 minutes, 0.8 mg/kg over 3 hours, and 0.8 mg/kg infused at 80 mg/h. The 3.2 mg/kg treatment doses were infused over a fixed time of 3 hours for the first 2 test dose trials and at a fixed rate of 80 mg/h for the final protocol. All test dose infusions were on day -7. In the first 2 schedules, Bu administered over a fixed time had significantly higher clearance for the test dose compared with the treatment dose. However, when both the test and the treatment doses were administered at the same infusion rate, clearance of the drug between the 2 dosing days was equivalent. Predicted day -5 AUC (AUC(-5)) showed a high linear correlation (r(2) = 0.74) to the actual AUC(-5). The error of these predictions was <20% in 98% of patients and <10% in 80%. In 24 individuals, the test dose predicted an AUC >5500 μM·min; therefore, the first Bu treatment dose was reduced to a desired target AUC. All adjusted doses fell within 20% of the targeted exposure. We conclude that a test dose strategy for therapeutic drug monitoring of daily i.v. Bu is accurate if the test and treatment doses are infused at the same rate. This approach allows targeting of therapeutic doses of Bu to desired levels and the potential for improved safety and efficacy.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".