Abstract 1538: Revisiting the dogma of antibody drug conjugates (ADCs): Emerging data challenge the benefit of linker stability and the primacy of payload delivery
Bibliographic record
Abstract
Abstract Antibody drug conjugates (ADCs) are an effective class of cancer therapeutics which have become more prominent after eight FDA approvals in the past three years. ADCs are envisioned to enable the selective delivery of a potent drug to cells that express the antibody target while sparing normal tissues, and thus to improve the therapeutic window by both increasing the maximum tolerated dose (MTD) and decreasing the minimum efficacious dose (MED) of the conjugated drug. Mounting clinical evidence does not support this simplified paradigm [1]. Herein we focus on three prominent issues in ADC development: 1. Alternative mechanisms over and above direct tumor targeting are likely to contribute to ADC therapeutic benefits in the clinic [2]. The responses to certain ADCs in patients with different levels of target antigen expression reveal some target-independent antitumor effects, suggesting the possibility that circulating payload may contribute to efficacy. 2. The preclinical optimization of ADCs is based on efficacy and tolerability in non-human animal studies. Fundamental differences in biology and translatability between species potentially overemphasize the importance of linker stability. 3. Clinical data, although limited, does not support the idea that increased ADC stability drives substantial therapeutic benefit. None of the 13 approved ADCs are completely ‘stable’ in circulation, and more stable drug-linkers have been associated with alternative toxicity profiles and no significant improvement in MTD. Novel meta-analyses and case studies assembled and integrated from >40 approved or clinically active ADCs and >70 discontinued ADCs will be presented in support of these three perspectives. The ADCs included in the analysis differ in terms of payload class, linker type, conjugation chemistry, drug-to-antibody ratio, and antibody target. Gaining a better understanding of the mechanism(s) of action of effective ADCs and the interrelationship between ADC structural components and their pharmacokinetics and pharmacodynamics is critical to inform the next generation of ADCs. References: [1] Colombo, R.; Rich, J. R., The therapeutic window of antibody drug conjugates: A dogma in need of revision. Cancer Cell 2022, 40, 1255-1263. [2] Tarcsa, E.; Guffroy, M. R.; Falahatpisheh, H.; Phipps, C.; Kalvass, J. C., Antibody-drug conjugates as targeted therapies: Are we there yet? A critical review of the current clinical landscape. Drug Discovery Today: Technologies 2020, 37, 13-22. Citation Format: Raffaele Colombo, Stuart D. Barnscher, Jamie R. Rich. Revisiting the dogma of antibody drug conjugates (ADCs): Emerging data challenge the benefit of linker stability and the primacy of payload delivery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1538.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".