Abstract 659: Integrated therapeutic antibody development at the National Research Council of Canada
Bibliographic record
Abstract
Abstract Advances in genomics and antibody engineering have enabled the development of an innovative class of targeted therapies, namely therapeutic antibodies, for the treatment of diseases with significant unmet medical needs such as cancer. Therapeutic antibodies represent one of the largest and fastest growing classes of medications. The National Research Council of Canada (NRC) has built a chain of cutting edge technology platforms needed to discover, engineer and produce therapeutic monoclonal antibodies with the goal of partnering with industrial and academic centers to advance research and development of this important class of therapeutics. Target Identification- Tumor targets were identified using a combination of proteomics, transcriptomics and bioinformatic approaches. Out of these lists, approximately 40 tumor targets were selected and over 3,000 antibodies of mouse and camelid origin were generated against these targets. Antibody generation can be done conventionally, using the recombinant target protein produced using NRC's high efficiency cell expression platforms in CHO or HEK293 cells or by direct immunization with plasmid DNA constructs. Clone selection is carried out by ELISA and typically 50 antibodies/target are identified for further characterization. Antibody characterization and validation- The affinities of the antibodies are determined by SPR biosensor analysis. Epitope mapping can be carried out so that representative antibodies can be selected for further validation in appropriate cell-based assays (many of which are established at NRC) and animal models. Therapeutic antibody Optimization, Bioprocessing and Biomanufacturing- Therapeutic antibodies selected for development can be further optimized using antibody engineering technologies to humanize them and/or modify their glycosylation patterns to improve their effector function, pharmacokinetics, solubility and stability as well as reduce their immunogenicity. The NRC platform for large scale protein production has the capacity to manufacture up to 500 g of commercial grade antibody in a cGMP certified CHO cell line which is ready for transfer to CMOs . Citation Format: Maria L. Jaramillo, Anne Marcil, Yves Durocher, Renald Gilbert, Alaka Mullick, John Kelly, Maureen O'Connor-McCourt, Bernard Massie. Integrated therapeutic antibody development at the National Research Council of Canada. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 659. doi:10.1158/1538-7445.AM2014-659
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.016 |
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".