Single-treatment tumor ablation with photodynamic liposomal irinotecan sucrosulfate
Bibliographic record
Abstract
Irinotecan (IRI) loaded actively into PEGylated liposomes via a sucrosulfate gradient has been approved recently to treat advanced pancreatic cancer. In this study, a similar liposomal composition was developed that includes a low mole fraction (1 mol.%) of porphyrin-phospholipid (PoP), a photosensitizer that stably incorporates into liposomes, to confer light-triggered IRI release. IRI-loaded PoP liposomes containing ammonium sucrosulfate (ASOS) as a complexing agent were more stable in serum compared to liposomes employing the more conventional ammonium sulfate. Without irradiation, PoP IRI liposomes released less than 5% IRI during 8 h of incubation in bovine serum at 37 °C, but released over 90% of the drug within minutes of exposure to red light (665 nm) irradiation. A single treatment with IRI-PoP liposomes and light exposure (15 mg/kg IRI with 250 J/cm 2 ) resulted in tumor eradication in mice bearing either MIA PaCa-2 tumors or low-passage patient-derived tumor xenografts that recapitulate characteristics of the clinical disease. Analogous monotherapies of IRI or photodynamic therapy were ineffective in controlling tumor growth. Enhanced drug uptake could be visualized within laser-treated tumors by direct in situ imaging of irinotecan. Biodistribution analysis of IRI, its active metabolite (SN-38), and major metabolite (SN-38 G) showed that laser treatment significantly increased tumor accumulation of all IRI-derived molecular species. A pharmacokinetic model that hypothesized tumor vasculature permeabilization as the primary reason underlying the increased drug deposition accounted for the enhanced drug influx into tumors.
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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) | 0.001 | 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".