Antibody-Drug Conjugates Targeting Tumor-Specific Mucin Glycoepitopes
Bibliographic record
Abstract
Finding the ideal epitope to target is a key element for the development of an antibody-drug conjugate (ADC). To maximize drug delivery to tumor cells and reduce side effects, this epitope should be specific to cancer cells and spare all normal tissue. During cancer progression, glycosylation pathways are frequently altered leading to the generation of new glycosylation patterns selective to cancer cells. Mucins are highly glycosylated proteins frequently expressed on tumors and, thus, ideal presenters of altered glycoepitopes. In this review, we describe three different types of glycoepitopes that are recognized by monoclonal antibodies (mAb) and, therefore, serve as ideal scaffolds for ADC; glycan-only, glycopeptide and shielded-peptide glycoepitopes. We review pre-clinical and clinical results obtained with ADCs targeting glycoepitopes expressed on MUC1 or podocalyxin (Podxl) and two mAbs targeting glycoepitopes expressed on MUC16 or MUC5AC as potential candidates for ADC development. Finally, we discuss current limits in using glycoepitope-targeting ADCs to treat cancer and propose methods to improve their efficacy and specificity.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".