‘Targeting’ Improved Outcomes with Antibody-Drug Conjugates in Non-Small Cell Lung Cancer—An Updated Review
Bibliographic record
Abstract
Antibody-Drug conjugates (ADCs) are a relatively new class of drugs with a promise to improve the outcomes in specific cancers. By delivering the cytotoxic agent to tumor cells expressing specific antigens, ADCs achieve a better therapeutic index and more potency. ADCs have been approved for several hematological and solid malignancies, including breast, urothelial and gastric carcinoma. Recently, trastuzumab deruxtecan (TDXd) was the first ADC approved for previously treated metastatic HER2-mutant non-small cell lung cancer (NSCLC). Many promising ADCs are in the pipeline for clinical development in non-small cell lung cancer, including sacituzumab govitecan, patritumab deruxtecan, datopotamab deruxtecan and tusamitamab ravtansine. There is a hope that these drugs would cater to the unmet need of specific patient populations, including patients with currently untargetable mutations. We hope these drugs, e.g., TROP2 targeted ADCs, will also give more options for therapy in NSCLC to improve outcomes for patients. In this comprehensive review, we will be discussing the recent evidence including targets, efficacy and the safety of newer ADC candidates in NSCLC. We will also briefly discuss the specific toxicities, novel biomarkers, overcoming resistance mechanisms, challenges and the way forward, as these new ADCs and combinations find a way into the clinical practice.
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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 | 0.002 |
| 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 | 0.001 |
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".