Abstract 3669: Screening platform for development of antibody-drug conjugates against novel targets at the National Research Council of Canada
Bibliographic record
Abstract
Abstract One of the most promising of the next generation of biologic-based cancer therapeutics builds on the molecular targeting abilities of antibodies by combining them with drugs to generate highly specific antibody-drug conjugates (ADCs). However, the development of ADCs requires time-consuming selection of the antibody for every target and cancer type. High-throughput screening technologies based on the use of conjugated secondary antibodies provide a fast and efficient surrogate assay from which to identify which antibodies are best internalized and suitable for immunoconjugate development into ADCs. As part of its integrated antibody development initiative, NRC has isolated and characterized anti- mouse Fc and anti-human Fc monoclonal antibodies to serve as very selective detective reagents for various IgG isotypes. We have shown that these secondary antibodies are species specific, selective and of high affinity. When conjugated to pH sensitive fluorophores, we have used them to specifically identify internalizing antibodies against tumor targets, which were later validated as ADCs. Furthermore, these secondary conjugates exhibit high specific potency and low background toxicity once conjugated to linkered drugs. This approach allow us to optimize the selection of an antibody for a particular target, tumor type, linker and drug for ADC development. NRC will present results of a screen of 285 mouse antibodies against 20 different targets in 7 different cancer cell lines, using either MCC-DM1 or vc-MMAE-conjugated secondary antibodies. The NRC ADC discovery platform is combining this methodology with our proprietary mRNA and DNA expression database for the selection of appropriate ADC targets. NRC Biologics and Biomanufacturing program is in the process of screening thousands of NRC antibodies generated against a variety of cancer-associated cell surface targets to deliver a steady pipeline of ADCS as part of its drug discovery efforts. This functional screening platform further promotes the integration and advancement of NRC’s capabilities and strengths in the area of biologic-based therapeutics lead candidate selection, including quality attributes and characterization and biomanufacturing. The combined expertise in cell biology, high throughput screening, antibody generation and selection, bioinformatics and expression analysis forms the foundation by which NRC can establish strategic collaborations with other Canadian or international partners to develop antibodies into novel ADC biologics. Citation Format: Maria Luz Jaramillo, Luc Meury, Patrice Bouchard, Allan Matte, Anne Marcil, Mauro Acchione, Jennifer Hill, Francois Fauteux. Screening platform for development of antibody-drug conjugates against novel targets at the National Research Council of Canada [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3669. doi:10.1158/1538-7445.AM2017-3669
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".