57 High-throughput quantitative characterization of cytotoxic antibody-drug conjugates using spheroid models reveals important considerations in potential molecular mechanisms of ADC resistance
Bibliographic record
Abstract
Background Antibody-drug conjugates (ADCs) are a class of cancer therapeutics comprised of a linker-payload conjugated to a monoclonal antibody targeting a tumor-associated antigen (TAA), to enable the delivery of the cytotoxic payload to cancer cells. In the last few years, while several ADCs have dramatically improved treatment options and demonstrated significant improvements in both progression-free survival (PFS) and overall survival (OS) in various cancer types, many patients still progress while on these agents, and more research is needed into why this resistance occurs. Resistance to ADCs can arise through several mechanisms, including antigen downregulation, impaired ADC trafficking, and payload resistance. With this in mind, there is a need for improved in vitro models that better recapitulate in vivo tumor tissue complexity to aid in the screening and evaluation of novel ADCs during preclinical development especially to address potential mechanisms of resistance.Methods We have developed in vitro 3D models from ovarian cancer cell lines yielding spheroids in a rapid, robust and uniform manner. Specifically, spheroids were generated by seeding ovarian cancer cell lines into microtiter plates treated with ultra-low attachment coating, using automated liquid-handling robots, followed by short-term incubation under standard culturing conditions for spheroid formation. Using these spheroid models, we have developed cell-based assays to functionally evaluate the cytotoxic activity of ADCs in vitro. For further comprehensive characterization of ADC activity in our 3D cell line models, we utilized the nCounter® ADC Development Panel, a specialized gene expression tool for molecular characterization of biological function, including gene content which addresses complex questions related to mechanisms of resistance.Results and Conclusions The nCounter® ADC Development Panel directly profiles 770 genes addressing essential biological questions relevant to ADC activity, including tumor targeting and antigen expression; ADC internalization; payload release; drug mechanisms of action; target cell death; immunogenic cell death; and mechanisms of resistance. Our data highlights the combination workflows of high-throughput cell culture-based spheroid assays with comprehensive transcriptomic analysis to aid in the screening and selection of therapeutic cytotoxic ADC candidates for downstream treatment of solid tumors. Further understanding of resistance mechanisms with this approach provides direction for future research to elucidate new targets for drug development that can expand the efficacy of antibody-drug conjugates.
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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) | 0.001 | 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 | 0.000 | 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".