Antibody–Drug Conjugates: A Start of a New Era in Gynecological Cancers
Bibliographic record
Abstract
Antibody-drug conjugates (ADCs) are a new class of therapeutic agents designed to target specific antigens on tumor cells, combining the specificity of monoclonal antibodies with the cytotoxicity of chemotherapy agents. ADCs have been available for over a decade, but in gynecological cancers, these agents are relatively new with great promise ahead. More than 80% of ongoing trials in gynecological cancers are evaluating ADCs' safety and efficacy, of which 40% are early-phase trials. Around twenty ADCs are currently under investigation, either alone or in combination with chemotherapies or immune checkpoint inhibitors. Among them, mirvetuximab soravtansine has been recently approved by the Food and Drug Administration (FDA) in platinum-resistant ovarian cancer with high folate-α receptor expression, as a single agent or in combination. Tisotumab vedotin and trastuzumab deruxtecan are also now approved by the FDA in patients with pre-treated cervical and uterine cancers and further investigation is ongoing. Overall, the toxicity profiles of ADCs are acceptable. Ocular toxicity is one of the specific side effects of some ADCs, but most of the cases are manageable with the use of prophylactic steroids and dose adjustments. This review aims to provide an overview of the fundamental and operational features of ADCs and examine the latest and most promising data, with a particular focus on the Canadian viewpoint.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".