Determination of Doxorubicin Levels in Whole Tumor and Tumor Nuclei in Murine Breast Cancer Tumors
Bibliographic record
Abstract
PURPOSE: Pharmacokinetic studies on liposomal drugs have previously measured total drug levels in tumors, which include non-bioavailable drug. However, drugs must be released from liposomes to have activity. We have developed a method for measuring levels of bioavailable (released) doxorubicin in vivo in tumors that will allow therapeutic activity to be correlated with bioavailable drug levels. EXPERIMENTAL DESIGN: Mice orthotopically implanted with mammary carcinoma (4T1) were injected i.v. 10 days after implantation with free doxorubicin or formulations of liposomal doxorubicin with different drug release rates. Tumors were excised at various times after injection, and total tumor doxorubicin levels were determined by acidified isopropanol extraction of whole tumor homogenates. Bioavailable doxorubicin levels were determined by extraction of doxorubicin from isolated tumor nuclei. RESULTS: Free doxorubicin had high levels of bioavailability in tumor tissue; 95% of the total doxorubicin in tumors was bound to nuclear DNA by 24 hours after injection. Administration of Doxil, a slow release liposomal formulation of doxorubicin, gave an area under the time-versus-concentration curve (AUC) for total doxorubicin 7 days after injection that was 87-fold higher than that obtained for free doxorubicin, and 49% of the liposomal doxorubicin was bioavailable. For liposomes with a more rapid doxorubicin release rate, by 7 days after injection, the AUC(0-7 days) for total doxorubicin was only 14-fold higher than that for free doxorubicin and only 27% of liposomal doxorubicin was bioavailable. CONCLUSIONS: This technique allows correlations to be made between drug bioavailability and therapeutic activity and will help in the rational design of drug carriers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".