Antibody-drug Conjugates in The Management of Advanced Urothelial Carcinoma
Bibliographic record
Abstract
For decades, the cornerstone for treatment of advanced urothelial carcinoma (aUC) has consisted of platinum-based chemotherapy regimens, such as GC (gemcitabine plus cisplatin/carboplatin) or MVAC (methotrexate, vinblastine, doxorubicin, and cisplatin). Thereafter, immune checkpoint inhibitors (ICI) were incorporated into the standard of care, initially as monotherapy in subsequent-line settings and more recently as maintenance treatment with chemotherapy in the first-line setting. Recently, the development of antibody-drug conjugates (ADCs) has dramatically shifted the treatment landscape for aUC. ADCs are engineered to function as a biologic “honing missile”, with the aim of delivering its cytotoxic payload to the target cancer cell while remaining stable in circulation and minimizing off-target toxicity. Enfortumab vedotin was the first to demonstrate efficacy in urothelial carcinoma (UC), initially as monotherapy and later in combination with ICI, surpassing the decades-old standard of first-line chemotherapy. The aim of this review is to discuss the evolving field of ADCs in aUC, highlighting the main targets, clinical data, toxicities, and future opportunities. ADCs are engineered to function as a biologic “honing missile”, with the aim of delivering its cytotoxic payload to the target cancer cell while remaining stable in circulation and minimizing off-target toxicity. Enfortumab vedotin was the first to demonstrate efficacy in urothelial carcinoma (UC), initially as monotherapy and later in combination with ICI, surpassing the decades-old standard of first-line chemotherapy. The aim of this review is to discuss the evolving field of ADCs in aUC, highlighting the main targets, clinical data, toxicities, and future opportunities.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".