New insights into the pretargeting approach to image and treat tumours
Bibliographic record
Abstract
Tumour pretargeting is a promising strategy for cancer diagnosis and therapy allowing for the rational use of long circulating, highly specific monoclonal antibodies (mAbs) for both non-invasive cancer radioimmunodetection (RID) and radioimmunotherapy (RIT). In contrast to conventional RID/RIT where the radionuclides and oncotropic vector molecules are delivered as presynthesised radioimmunoconjugates, the pretargeting approach is a multistep procedure that temporarily separates targeting of certain tumour-associated antigens from delivery of diagnostic or therapeutic radionuclides. In principle, unlabelled, highly tumour antigen specific mAb conjugates are, in a first step, administered into a patient. After injection, sufficient time is allowed for blood circulation, accumulation at the tumour site and subsequent elimination of excess mAb conjugates from the body. The small fast-clearing radiolabelled effector molecules with a complementary functionality directed to the prelocalised mAb conjugates are then administered in a second step. Due to its fast pharmacokinetics, the small effector molecules reach the malignant tissue quickly and bind the local mAb conjugates. Thereby, corresponding radioimmunoconjugates are formed in vivo and, consequently, radiation doses are deposited mainly locally. This procedure results in a much higher tumour/non-tumour (T/NT) ratio and is favourable for cancer diagnosis and therapy as it substantially minimises the radiation damage to non-tumour cells of healthy tissues. The pretargeting approach utilises specific non-covalent interactions (e.g. strept(avidin)/biotin) or covalent bond formations (e.g. inverse electron demand Diels-Alder reaction) between the tumour bound antibody and radiolabelled small molecules. This tutorial review descriptively presents this complex strategy, addresses the historical as well as recent preclinical and clinical advances and discusses the advantages and disadvantages of different available variations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".