Population Pharmacokinetic Study and Individual Dose Adjustments of High‐Dose Methotrexate in Chinese Pediatric Patients With Acute Lymphoblastic Leukemia or Osteosarcoma
Bibliographic record
Abstract
) is among the first-line chemotherapeutic agents used in treating acute lymphoblastic leukemia (ALL) and osteosarcoma in children. Despite rapid hydration, leucovorin rescue, and routine therapeutic drug monitoring, severe toxicity is not uncommon. This study aimed at developing population pharmacokinetic (popPK) models of high-dose methotrexate for ALL and osteosarcoma and demonstrating the possibility and convenience of popPK model-based individual dose optimization using R and shiny, which is more accessible, efficient, and clinician-friendly than NONMEM. The final data set consists of 36 ALL (354 observations) and 16 osteosarcoma (585 observations) patients. Covariate model building and parameter estimations were done using NONMEM and Perl-speaks-NONMEM. Diagnostic Plots and bootstrapping validated the models' performance and stability. The dose optimizer developed based on the validated models can obtain identical individual parameter estimates as NONMEM. Compared to calling a NONMEM execution and reading its output, estimating individual parameters within R reduces the execution time from 8.7-12.8 seconds to 0.4-1.0 second. For each subject, the dose optimizer can recommend (1) an individualized optimal dose and (2) an individualized range of doses. For osteosarcoma, recommended optimal doses by the optimizer resemble the final doses at which the subjects were eventually stabilized. The dose optimizers developed demonstrated the potential to inform dose adjustments using a model-based, convenient, and efficient tool for high-dose methotrexate. Although the dose optimizer is not meant to replace clinical judgment, it provides the clinician with the individual pharmacokinetics perspective by recommending the (range of) optimal dose.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".