Abstract IA023: Dual-drug ADC platform for effective and safe cancer treatment
Bibliographic record
Abstract
Abstract Breast tumor heterogeneity presents significant treatment challenges, particularly in overcoming drug resistance and tumor relapse driven by heterogeneous gene expression variability. Antibody-drug conjugates (ADCs) have demonstrated therapeutic success, yet intratumor heterogeneity remains a hurdle. We investigated dual-drug ADCs as a promising approach to address this clinical challenge. Our conjugates achieved potent and antigen-specific cytotoxicity, favorable pharmacokinetic properties, and high tolerability. In xenograft models with heterogeneous HER2 expression, our anti-HER2 dual-drug ADC significantly outperformed co-administration of two separate single-drug ADCs, demonstrating enhanced capacity to surmount tumor heterogeneity and resistance. Additionally, we developed a novel glutamic acid-glycine-citrulline (EGCit) linker, which enhances ADC hydrophilicity and stability in circulation. This EGCit linker also resists neutrophil protease degradation, a factor implicated in ADC-associated neutropenia. Compared to currently approved ADCs, our EGCit-based ADC exhibited reduced blood and liver toxicity, alongside superior antitumor efficacy in preclinical xenograft studies. These findings underscore the potential of our technology platform for advancing safer, more effective treatments for breast and other refractory cancers. Citation Format: Kyoji Tsuchikama. Dual-drug ADC platform for effective and safe cancer treatment [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr IA023.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".