Enhanced liposomal retention of irinotecan is correlated to increases in plasma irinotecan AUC and improved efficacy against a model of colorectal cancer
Bibliographic record
Abstract
1393 Purpose: We have previously reported that irinotecan can be encapsulated into liposomes by generation of a secondary pH gradient following addition of the ionophore A23187 to liposomes with a pre-existing divalent metal ion gradient. A23187 exchanges divalent metal ions from the liposome interior for two protons from the external buffer thus generating a low internal pH. This environment maintains encapsulated irinotecan in the clinically active lactone form and can mediate significant gains in therapeutic effect when compared to unencapsulated irinotecan (Messerer et al., Clin. Cancer Res. 10:6638-49, 2004). In this study we sought to (i) further optimise the liposomal retention of irinotecan by use of interior solutions comprising transition metal ions at low pH and (ii) determine whether improved plasma retention of irinotecan by liposomes translates into increased therapeutic activity. Methods: Irinotecan was remote loaded into DSPC/Cholesterol liposomes comprising different internal solutions ± A23187: A (buffered Cu2+, pH 7.5), B (Cu2+, pH ∼ 3.5), C (Mn2+ + A23187) and D (Cu2+ + A23187). Mice were injected i.v. with a single treatment dose and the plasma concentrations of liposomal lipid and/or irinotecan were determined over time and used to calculate plasma irinotecan-to-lipid ratio (a measure of liposomal irinotecan retention), irinotecan area-under-the-curve (AUC) and mean residence time (MRT). The therapeutic efficacy of the treatments was investigated using a s.c. murine LS180 human colorectal cancer model. Results: Conclusion: Encapsulation of irinotecan in DSPC/Cholesterol liposomes by a remote loading procedure employing Cu2+ and A23187 results in a liposome formulation with superior plasma irinotecan retention and AUC, and improved therapeutic activity against a murine model of human 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".