Synthesis and Biological Evaluation of Folate Receptor-Targeted Boronated PAMAM Dendrimers as Potential Agents for Neutron Capture Therapy
Bibliographic record
Abstract
Successful treatment of cancer by boron neutron capture therapy (BNCT) requires the selective delivery of (10)B to constituent cells within a tumor. The expression of the folate receptor is amplified in a variety of human tumors and potentially might serve as a molecular target for BNCT. In the present study we have investigated the possibility of targeting the folate receptor on cancer cells using folic acid conjugates of boronated poly(ethylene glycol) (PEG) containing 3rd generation polyamidoamine dendrimers to obtain (10)B concentrations necessary for BNCT by reducing the uptake of these conjugates by the reticuloendothelial system. First we covalently attached 12-15 decaborate clusters to 3rd generation polyamidoamine dendrimers. Varying quantities of PEG units with varying chain lengths were then linked to these boronated dendrimers to reduce hepatic uptake. Among all prepared combinations, boronated dendrimers with 1-1.5 PEG(2000) units exhibited the lowest hepatic uptake in C57BL/6 mice (7.2-7.7% injected dose (ID)/g liver). Thus, two folate receptor-targeted boronated 3rd generation polyamidoamine dendrimers were prepared, one containing approximately 15 decaborate clusters and approximately 1 PEG(2000) unit with folic acid attached to the distal end, the other containing approximately 13 decaborate clusters, approximately 1 PEG(2000) unit, and approximately 1 PEG(800) unit with folic acid attached to the distal end. In vitro studies using folate receptor (+) KB cells demonstrated receptor-dependent uptake of the latter conjugate. Biodistribution studies with this conjugate in C57BL/6 mice bearing folate receptor (+) murine 24JK-FBP sarcomas resulted in selective tumor uptake (6.0% ID/g tumor), but also high hepatic (38.8% ID/g) and renal (62.8% ID/g) uptake, indicating that attachment of a second PEG unit and/or folic acid may adversely affect the pharmacodynamics of this conjugate.
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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".