Improved Outcome When B-Cell Lymphoma Is Treated with Combinations of Immunoliposomal Anticancer Drugs Targeted to Both the CD19 and CD20 Epitopes
Bibliographic record
Abstract
PURPOSE: We have reported previously that successful immunoliposomal drug therapy with liposomal doxorubicin (DXR) against xenograft B-lymphoma models required targeting against an internalizing B-cell antigen, CD19 (P. Sapra and T. M. Allen. Cancer Res 2002;62:7190-4.). Here we compare targeting of immunoliposomal formulations of DXR with vincristine (VCR) targeted against CD19 versus a noninternalizing (CD20) epitope. We also examine the effect of targeting immunoliposomes with antibody combinations in an attempt to increase the total number of binding sites (apparent antigen density) at the target cell surface. EXPERIMENTAL DESIGN: Cell association of immunoliposomes (CD19-targeted, CD20-targeted, or combinations of the two) with human B-cell lymphoma (Namalwa) cells were studied using radiolabeled liposomes. Therapeutic efficacy of the same formulations was determined in a severe combined immunodeficient murine model. RESULTS: Therapeutic results in severe combined immunodeficient mice bearing Namalwa cells administered anti-CD20-targeted liposomal DXR were barely improved over those found for nontargeted liposomal DXR or free DXR but, surprisingly, administration of anti-CD20-targeted liposomal VCR resulted in a significantly improved therapeutic outcome compared with nontargeted liposomal VCR, free VCR, or anti-CD20-targeted liposomal DXR. Treatment of murine B lymphoma with single injections of combinations of anti-CD19- and anti-CD20-targeted liposomal VCR led to cures in 70% of mice. However, mice injected with similar combinations of liposomal DXR did not have improved survival rates over anti-CD19-targeted liposomal DXR by itself. CONCLUSIONS: The success of immunoliposomal therapy in combination regimens varies with the type of encapsulated drug and the nature of the target epitopes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".