Abstract 1216: Assays for the selection and functional characterization of antibody-drug conjugates at the National Research Council of Canada
Bibliographic record
Abstract
Abstract One of the most promising and fastest growing classes of 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. 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 isoforms. We have shown that these secondary antibodies are species specific, selective and of high affinity. Furthermore, they exhibit high specific potency and low background toxicity after conjugating them to drugs (DM1, MMAE) or immunotoxins(saporin) in cell viability studies. By combining this methodology with our proprietary mRNA and DNA expression database for the selection of appropriate cell lines, we plan on screening thousands of NRC antibodies generated against variety of cancer associated cell surface targets for ADC development. In addition to secondary conjugate cytotoxicity assays, we have developed a suite of assays based on cellular accumulation, endosomal routing and activation (intracellular drug release) of antibody drug conjugates. These assays can all contribute to our mechanistic understanding of ADCs under development. Combined with our strength in biologic production and characterization, this expertise promotes the integration and advancement of NRC's capabilities and strengths in the area of Antibody-Drug Conjugates (ADCs), and can be used to establish strategic collaborations with other Canadian or international partners to develop external or internal NRC antibodies into novel ADC biologics. Citation Format: Maria L. Jaramillo, Luc Meury, Normand Jolicoeur, Myriam Banville, Limei Tao, Maureen O’Connor McCourt. Assays for the selection and functional characterization of antibody-drug conjugates at the National Research Council of Canada. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1216.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".