A Two-Step Pretargeted Nanotherapy for CD20 Crosslinking May Achieve Superior Anti-Lymphoma Efficacy to Rituximab
Bibliographic record
Abstract
The use of rituximab, an anti-CD20 mAb, in combination with chemotherapy is the current standard for the treatment of B-cell lymphomas. However, because of a significant number of treatment failures, there is a demand for new, improved therapeutics. Here, we designed a nanomedicine that crosslinks CD20 and directly induces apoptosis of B-cells without the need for toxins or immune effector functions. The therapeutic system comprises a pretargeting component (anti-CD20 Fab' conjugated with an oligonucleotide1) and a crosslinking component (N-(2-hydroxypropyl)methacrylamide (HPMA) copolymer grafted with multiple complementary oligonucleotide2). Consecutive treatment with the two components resulted in CD20 clustering on the cell surface and effectively killed malignant B-cells in vivo. To enhance therapeutic efficacy, a two-step pretargeting approach was employed. We showed that the time lag between the two doses can be optimized based on pharmacokinetics and biodistribution of the Fab'-oligonucleotide1 conjugate. In a mouse model of human non-Hodgkin lymphoma (NHL), increasing the time lag from 1 h to 5 h resulted in dramatically improved tumor growth inhibition and animal survival. When the 5 h interval was used, the nanotherapy was more efficacious than rituximab and led to complete eradication of lymphoma cells with no signs of metastasis or disease recurrence. We further evaluated the nanomedicine using patient mantle cell lymphoma cells; the treatment demonstrated more potent apoptosis-inducing activity than rituximab hyper-crosslinked with secondary antibodies. In summary, our approach may constitute a novel treatment for NHL and other B-cell malignancies with significant advantages over conventional chemo-immunotherapy.
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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".