Abstract 5482: In vitro assays for prediction of ADC hematological toxicities: contribution of antibody, linker, and payload
Bibliographic record
Abstract
Abstract Antibody-drug conjugates (ADCs) are one of the fastest-growing therapeutic modalities, with 11 FDA-approved ADCs and more than 250 different ADCs in clinical development. Despite their success, significant hurdles remain. Notably, translating preclinical findings to the clinic remains challenging. Hematological toxicities are commonly associated with many ADCs and may arise from the direct killing of hematopoietic cells by the ADC itself or indirectly from payload released elsewhere in the body. Therefore, development of in vitro assays capable of predicting clinical findings could help improve ADC development and guide the selection of optimal linkers and payloads. Different ADCs were generated by conjugating targeted and non-targeted antibodies to a common microtubule inhibitor, while varying the protease-cleavable linkers. Clinically approved ADCs spanning different payload classes were used as benchmarks. ADC cytotoxicity was evaluated in both antigen-positive and antigen-negative cancer cell lines in vitro. Lysosomal cleavage of different linkers was evaluated in-vitro. ADC linker stability was also evaluated via a neutrophil differentiation assay, using expanded and differentiated CD34+ hematopoietic stem cells, which were treated with various ADCs before assessing CD66b neutrophil maturation. Finally, in vitro off-target toxicity was evaluated using a colony forming cell (CFC) assay to assess cytotoxicity of ADCs and their payloads on erythroid, myeloid, and megakaryocyte progenitors differentiated from hematopoietic stem cells. In vitro cytotoxicity assays revealed variable potency among the ADCs tested. Some linkers (e.g., VK, FK) resulted in potent ADC cytotoxicity irrespective of the targeting antibody, while others (e.g., VA, VCit, GGFG, K) had target-dependent cytotoxicity in vitro. We identified linkers with different payload release rates in lysosomes, where GGFG and K showed the slowest rates. In vitro off-target and linker stability assays highlight how ADCs may induce toxicity in different healthy cell subsets depending on the ADC components (antibody, linker, and payload). In addition, by comparing results for ADCs and payloads, it is also possible to estimate if these toxicities are likely driven by the conjugated drug, the payload, or both. These results underlined how off-target toxicity and neutrophil differentiation assay may be useful during preclinical development to identify potential off-target effects and to improve the translation of ADC from preclinical to clinical settings. Citation Format: Yongzhao Huang, Emer Clarke, Graham A. Garnett, Manuel Lasalle, Jodi Wong, Ambroise Wu, Araba P. Sagoe-Wagner, Elena Barbosa, Catalina Suarez, Stuart D. Barnscher, Jamie R. Rich, Raffaele Colombo. In vitro assays for prediction of ADC hematological toxicities: contribution of antibody, linker, and payload [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5482.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".