ADCs and TCE in SCLC Therapy: The Beginning of a New Era?
Bibliographic record
Abstract
The therapeutic landscape for small cell lung cancer (SCLC) has remained stationary for decades, with chemotherapy representing the sole treatment strategy, with a modest survival benefit. The addition of immune checkpoint inhibitors (ICIs) to standard first-line chemotherapy for SCLC was a considerable milestone. However, despite high overall response rates, this strategy failed to deliver long-term benefits for most patients, who continue to face a poor prognosis. Over the last few years, a deeper knowledge of the molecular biology of SCLC and the impressive advancements in drug development, have led to the generation of novel classes of systemic therapies that promise to revolutionize the current therapeutic scenario. Among the various therapeutic approaches in development, T-cell Engagers (TCE) and antibody-drug conjugates (ADCs) stand out due to their unique structural characteristics and mechanisms of action. These therapies represent a paradigm shift from traditional monoclonal antibody (mAb) and chemotherapy regimens, allowing direct engagement of multiple targets associated with tumor progression. In this review, we provide an overview of current drug development in SCLC, specifically focusing on these new agents, summarizing available evidence, and tracking 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".