Abstract IA003: Refining our understanding of ADCs: Drug development insights from 40 years of data
Bibliographic record
Abstract
Abstract Antibody drug conjugates (ADCs) have emerged as an effective and promising class of cancer therapeutics. Over the past 40 years, over 300 new ADCs have entered the clinic, culminating in 11 FDA approvals to date. Recently, the increased volume of clinical data related to ADCs has substantially advanced our understanding of this therapeutic class. Despite their rising popularity, there are three prevailing ADC dogma that are widely accepted without challenge: - ADCs widen the therapeutic window of the conjugated drug by both increasing the maximum tolerated dose (MTD) and reducing the minimum efficacious dose (MED) of the drug (the therapeutic window dogma). - A highly stable linker is paramount to the clinical success of the ADC (the stability dogma). - ADCs deliver conjugated drugs selectively to cancer cells while sparing normal cells (the magic bullet dogma). Clinical evidence does not consistently support these beliefs. An improved understanding of how ADCs work is critical to the enhanced design and development of this therapeutic class. Consequently, the canonical ADC dogma need to be refined in light of emerging clinical data. Revised therapeutic window dogma: ADCs do not significantly increase the MTD of their conjugated drugs. Instead, when dosed at or near their MTDs, ADCs exhibit higher efficacy compared to corresponding small molecules. Revised stability dogma: antibody-linker instabilities and linker-drug instabilities are common across many ADCs, including all the approved ADCs (i.e., none of the approved ADCs feature a stable linker). Unexpected toxicities have often emerged among ADCs containing overly stabilized linkers. Revised magic bullet dogma: ADCs significantly alter the exposure of the conjugated drug. Targeted drug delivery and non-targeted uptake, in combination with linker instabilities, contribute to the sustained drug concentration at the tumor site. Citation Format: Raffaele Colombo. Refining our understanding of ADCs: Drug development insights from 40 years of data [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr IA003.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".