A novel irinotecan-lipiodol nanoemulsion for intravascular administration: pharmacokinetics and biodistribution in the normal and tumor bearing rat liver
Bibliographic record
Abstract
Colorectal cancer is one of the most common cancers in the United States and treatment options are limited for patients who develop liver metastases. Several chemotherapeutic regimens have been used for transvascular liver-directed therapy in the treatment of colorectal liver metastases without clear evidence of superiority of one therapy over another. We describe the development of a novel nanoemulsion through combining irinotecan (IRI), a first line systemic agent used for the treatment of colon cancer, with lipiodol, an oily contrast medium derived from poppy seed oil, and evaluated its pharmacokinetic and biodistribution profile as a function of portal venous chemoembolization (PVCE) versus transarterial chemoembolization (TACE) delivery. The Tessari technique was used to create a stable emulsion (20 mg IRI mixed with 2 mL lipiodol) with resultant particle size ranging from 28.9 nm to 56.4 nm. Pharmacokinetic profile established through venous sampling in Buffalo rats demonstrate that the area under the curve (AUC0−∞) of IRI was significantly less after PVCE with IRI-lipiodol as compared to IRI alone (131 vs. 316 µg*min/mL, p-value = .023), suggesting significantly higher amounts of IRI retention in the liver with the IRI-lipiodol nanoemulsion via first-pass extraction. Subseqent biodistribution studies in tumor-bearing WAG/Rjj rats revealed more IRI present in the tumor following TACE versus PVCE (29.19 ± 12.33 µg/g versus 3.42 ± 1.62; p-value = .0033) or IV (29.19 ± 12.33 µg/g versus 1.05 ± 0.47; p-value = .0035). The IRI-lipiodol nanoemulsion demonstrated an acceptable hepatotoxicity profile in all routes of administration. In conclusion, the IRI-lipiodol nanoemulsion via TACE showed promise and warrants further investigation as an option for the treatment of metastatic colorectal cancer.
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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 | 0.000 | 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 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".