Antibody-Drug Conjugates Targeting Tumor-Specific Mucin Glycoepitopes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it