Microdialysis-directed Intra-tumor Pharmacokinetic Modeling of Methotrexate in Mice and Humans
Bibliographic record
Abstract
PURPOSE: To develop a quantitative pharmacokinetic model to characterize the disposition of methotrexate (MTX) at tumor site in tumor-bearing mice and to predict MTX concentrations in the human tumor. METHODS: The plasma profiles of MTX were obtained from normal mice, while microdialysis technique was employed to characterize the time course of MTX in tumor from breast tumor-bearing mice. Disposition profiles of plasma and tumor were analyzed by a hybrid physiologically-based pharmacokinetic (hPBPK) model that incorporates physiologically-relevant parameters such as tumor blood flow and volume, while plasma concentrations were used as a forcing input into the vascular-interstitial spaces of the tumor. The plasma profiles were initially described by a biexponential decay model to obtain a forcing function that enters into the vascular-interstitial spaces in the tumor. Using a defined forcing function, the tumor free concentrations were fitted to the hPBPK model. Based on the model developed, sensitivity analysis was conducted with a perturbation of PK parameters to predict different scenarios of intratumoral MTX transport. The relevant physiological PK parameters from the mouse model were then scaled-up and utilized to simulate human tumor concentrations. RESULTS: The mouse hPBPK model adequately characterized the concentration-time profiles of MTX in both plasma and tumor and produced various transfer rate constants between plasma and tumor. Our model was also able to reasonably predict MTX concentrations in the human tumor when human physiological data were utilized. CONCLUSIONS: The hPBPK model was able to quantitatively characterize the atypical transport of MTX in the tumor, supporting the idea that microdialysis is a valuable tool to study tumor biodistribution of drugs and to predict tumor concentrations in humans based on the pre-clinical data. This information can ultimately aid in the development of anticancer drugs with improved PK profiles. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".