Quantitative CT Imaging of the Spatial and Temporal Distribution of Liposomes in a Rabbit Tumor Model
Bibliographic record
Abstract
Successful employment of noninvasive imaging techniques to quantitatively assess the in vivo pharmacokinetics and biodistribution of nanoparticle drug delivery systems will facilitate the rational design of novel targeted drug carriers. This study reports on the bulk organ/tissue (liver, kidneys, spleen, tumor and blood) and intratumoral distribution of liposomes containing iohexol and gadoteridol over a 14-day period in VX2 sarcoma-bearing New Zealand White rabbits using computed tomography (CT). The vascular half-life of the liposomes was found to be 63.6 +/- 5.8 h and the maximum tumor-to-muscle iodine concentration ratio of 11.9 +/- 6.0 was measured 7 days postinjection with 1.13 +/- 0.29% ID of liposomes accumulating at the tumor site. The liposomes achieved their highest intratumoral distribution volume ratio at 48 h postadministration, occupying 72 +/- 5% of the total tumor volume. This investigation demonstrated the feasibility of using CT to perform quantitative, volumetric and longitudinal assessment of the pharmacokinetics and biodistribution of iodinated liposomes with sensitivities in the range of microg/cm3 while maintaining the ability to identify boundaries of anatomical structures at submillimeter resolution and with imaging time of less than one minute per scan. If successfully approved for clinical adoption, the use of CT imaging to monitor nanoparticulate drug delivery will provide an opportunity for online adjustment of therapeutic regimens and implementation of personalized medicine.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".