Potential Theranostic Boron Neutron Capture Therapy Agents as Multimodal Radiopharmaceuticals
Bibliographic record
Abstract
Boron neutron capture therapy (BNCT) has been extant for decades and continues to be practiced in many centers around the globe. Most of the active clinical trials utilize boronophenylalanine as the drug containing boron atoms. The important aspect that has been added to the BNCT practice is the use of an F-18 radiolabeled analog for ascertaining targeting and monitoring follow-up studies. The recent widespread application of therapeutic radiopharmaceuticals, especially peptides (somatostatin analogs), prostate-specific antigen-binding ligands, or immunomolecules, offers the ambit for invention of new tumor-specific BNCT agents, especially for BNCT-susceptible tumors, that is, locoregional cancers such as head and neck cancer. Such BNCT agents, when radiolabeled, can enable simultaneous imaging and/or therapeutic applications (depending on the radionuclide used) through multimodal approaches. Development of boron-rich moieties such as sodium borocaptate and neutral carboranes combined with tumor-targeting moieties can lead to a new horizon in BNCT. The review covers various aspects of drug design, tumor targeting, and possible future radiopharmaceutical development for multimodal theranostic application in humans.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".