The Chemistry of Creating Chemically Programmed Antibodies (cPAbs): Site-Specific Bioconjugation of Small Molecules
Bibliographic record
Abstract
half-lives which is one of the largest impediments to the success of many potentially valuable pharmacologically active small molecules. The undesirable pharmacokinetics and pharmacology associated with some small molecules have led to the development of a new class of bioconjugates known as chemically programmed antibodies (cPAbs). cPAbs are bioconjugates in which antibodies are used to augment small molecules with effector functions and prolonged pharmacokinetic profiles, where the pharmacophore of the small molecule is harnessed for target binding and therefore biological targeting. Many different small molecules can be conjugated to large proteins such as full monoclonal antibodies (IgG), fragment crystallizable regions (Fc), or fragment antigen binding regions (Fab). In order to successfully and site-specifically conjugate small molecules to any class of antibodies (IgG, Fc, or Fab), the molecules must be derivatized with a functional group for ease of conjugation without altering the pharmacology of the small molecules. In this Review, we summarize the different synthetic or biological methods that have been employed to produce cPAbs. These unique chemistries have potential to be applied to other fields of antibody modification such as antibody drug conjugates, radioimmunoconjugates, and fluorophore-tagged antibodies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".