Enfortumab Vedotin in urothelial cancer
Bibliographic record
Abstract
The treatment landscape for metastatic urothelial cancer (mUC) beyond first-line platinum-based chemotherapy has changed significantly over the last 5 years with the recent approvals of the immune checkpoint inhibitors (ICIs), fibroblast growth factor receptor (FGFR) inhibitors and most recently Enfortumab Vedotin (EV). EV is a novel antibody-drug conjugate (ADC), that delivers monomethyl auristatin E (MMAE), a microtubule-disrupting agent, inside cells harboring the cell surface nectin-4 receptor. In mUC, EV has shown encouraging response rates and received accelerated approval from the Food and Drug Administration (FDA) in December 2019 in the post-platinum and ICI setting. EV is generally well tolerated, with the main toxicities being neuropathy, skin rash, alopecia and fatigue. Notably EV can also be administered to patients with renal dysfunction, which is commonly a concern in this patient population. EV is now being tested in combination strategies and in earlier disease settings in urothelial cancers. In this review, we will discuss its mechanism of action, clinical trials leading to FDA approval as well as ongoing trials and future directions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".