The Journey of Antibody–Drug Conjugates: Lessons Learned from 40 Years of Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it