The Journey of Antibody–Drug Conjugates: Lessons Learned from 40 Years of Development
Bibliographic record
Abstract
Antibody-drug conjugates (ADC) represent one of the most rapidly expanding treatment modalities in oncology, with 11 ADCs approved by the FDA and more than 210 currently being tested in clinical trials. Spanning over 40 years, ADC clinical development has enhanced our understanding of the multifaceted mechanisms of action for this class of therapeutics. In this article, we discuss key insights into the toxicity, efficacy, stability, distribution, and fate of ADCs. Furthermore, we highlight ongoing challenges related to their clinical optimization, the development of rational sequencing strategies, and the identification of predictive biomarkers. Significance: The development and utilization of ADCs have allowed for relevant improvements in the prognosis of multiple cancer types. Concomitantly, the rise of ADCs in oncology has produced several challenges, including the prediction of their activity, their utilization in sequence, and minimization of their side effects, that still too often resemble those of the cytotoxic molecule that they carry. In this review, we retrace 40 years of development in the field of ADCs and delve deep into the mechanisms of action of these complex therapeutics and reasons behind the many achievements and failures observed in the field to date.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".