CORRELATIONS BETWEEN THE RATE OF INTRACELLULAR RELEASE OF ENDOCYTOSED LIPOSOMAL DOXORUBICIN AND CYTOTOXICITY AS DETERMINED BY A NEW ASSAY
Bibliographic record
Abstract
Previously, we showed that liposomes with surface-attached anti-CD19 were internalized into human B lymphoma cells through receptor-mediated endocytosis, resulting in improved anti-tumor efficacy 1-2 . In order to further increase the efficacy of antineoplastic drug-containing liposomes, we have taken advantage of this internalization process by producing triggered release liposomes that rapidly release drug from the enzyme-rich, acidic environment of lysosomes. To analyze the effectiveness of these triggered-release formulations, we developed a nuclear accumulation assay for doxorubicin (DXR) that allows us to determine the rate of cytoplasmic drug delivery subsequent to drug release from the endosomal/lysosomal compartments by examining the rate of accumulation of drug in cellular nuclei. We demonstrate the usefulness of this assay by comparing the kinetics of cytoplasmic drug delivery for DXR-containing, pH-sensitive, triggered release liposomes versus DXR-containing, non-sensitive, liposomal formulations. We see a significant correlation between the rate of nuclear accumulation of DXR and its in vitrocytotoxicity. This indicates that pH-sensitive formulations traffic drug to the cytoplasm and the nucleus significantly more rapidly than do non-sensitive formulations. We conclude that the development of triggered release liposomes is a promising strategy for further improving the therapeutic efficacy of liposomal antineoplastic drugs targeted selectively to cancer cells by surface-attached ligands that bind to internalizing epitopes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".