Anti-CD19-Targeted Liposomal Doxorubicin Improves the Therapeutic Efficacy in Murine B-Cell Lymphoma and Ameliorates the Toxicity of Liposomes with Varying Drug Release Rates
Bibliographic record
Abstract
Some formulations of liposomal doxorubicin with intermediate rates of drug release have shown increased levels of toxicity in mice. Because antibody-mediated targeting of liposomal drugs influences the pharmacokinetics, mechanism of uptake, and selectivity of the associated drugs, we hypothesized that anti-CD19-mediated targeting of liposomal doxorubicin might moderate the toxicity of the problem formulations. Phosphatidylcholine/cholesterol liposomal formulations of doxorubicin having faster, intermediate, and slower drug release rates were prepared by altering the fatty acyl chain length or degree of saturation of the phosphatidylcholine component. Pharmacokinetic and biodistribution studies and in vivo drug release rates were determined in mice using liposomes dual labeled with [3H]cholesteryl hexadecylether and [14C]doxorubicin. Therapeutic studies were done in xenograft models of human B lymphoma (Namalwa cells). The rate of clearance of the liposomal lipid was similar for all formulations (average t1/2, 18 hours), but the rate of clearance of doxorubicin was dependent on the release rate of the formulation (t1/2, 2-315 hours). Liposomes with the slowest drug release rates showed no toxicity and exhibited therapeutic activity that was superior to the other formulations when targeted with anti-CD19; liposomes with the most rapid drug release rates also showed no toxicity but showed little therapeutic effect even when targeted. Liposomes with intermediate drug release rates exhibited varying degrees of toxicity. The toxicities could be reduced and even overcome by targeting with anti-CD19 antibodies. For these formulations, therapeutic effects were intermediate between those found for liposomes with the fastest and slowest drug release rates.
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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".