IEDDA: An Attractive Bioorthogonal Reaction for Biomedical Applications
Bibliographic record
Abstract
The pretargeting strategy has recently emerged in order to overcome the limitations of direct targeting, mainly in the field of radioimmunotherapy (RIT). This strategy is directly dependent on chemical reactions, namely bioorthogonal reactions, which have been developed for their ability to occur under physiological conditions. The Staudinger ligation, the copper catalyzed azide-alkyne cycloaddition (CuAAC) and the strain-promoted [3 + 2] azide–alkyne cycloaddition (SPAAC) were the first bioorthogonal reactions introduced in the literature. However, due to their incomplete biocompatibility and slow kinetics, the inverse-electron demand Diels-Alder (IEDDA) reaction was advanced in 2008 by Blackman et al. as an optimal bioorthogonal reaction. The IEDDA is the fastest bioorthogonal reaction known so far. Its biocompatibility and ideal kinetics are very appealing for pretargeting applications. The use of a trans-cyclooctene (TCO) and a tetrazine (Tz) in the reaction encouraged researchers to study them deeply. It was found that both reagents are sensitive to acidic or basic conditions. Furthermore, TCO is photosensitive and can be isomerized to its cis-conformation via a radical catalyzed reaction. Unfortunately, the cis-conformer is significantly less reactive toward tetrazine than the trans-conformation. Therefore, extensive research has been carried out to optimize both click reagents and to employ the IEDDA bioorthogonal reaction in biomedical applications.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".