Predictive Performance of a Physiologically Based Pharmacokinetic Model of Busulfan in Children
Bibliographic record
Abstract
A physiologically based pharmacokinetic (PBPK) model of the DNA-alkylating agent busulfan was slightly modified and scaled from adults to children in order to predict the systemic busulfan drug exposure in children. Capitalizing on the recent major software release of PK-Sim®, we refined our PBPK model by implementing glutathione S transferase (GST) in 11 organs using the software integrated enzyme expression database. In addition, two irreversible binding processes (i.e., DNA and plasma protein binding) were applied by using Koff and KD values. The model was scaled from adults to children. Simulations were computed and compared to concentration-time data after intravenous (i.v.) busulfan administration to 36 children. Based on the results, an age-dependent enzyme activity and maturation ratio was tailored and evaluated with an external dataset consisting of 23 children. Initial adult to children scaling indicated lower clearance values for children in comparison to adults. Subsequent age-dependent maturation ratio resulted in three different age groups: Activity of busulfan-glutathione conjugate formation was 80%, 61%, and 89% in comparison to adults for children with an age of up to 2 years, > 2-6 years, and > 6-18 years, respectively. Patients of the evaluation dataset were simulated with a mean percentage error (MPE) for all patients of 3.9% with 3/23 children demonstrating a MPE of > ±30%. The PBPK model parameterization sufficiently described the observed concentration-time data of the validation dataset while showing an adequate predictive performance. This PBPK model could be helpful to determine the first dose of busulfan in children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".