Abstract A248: 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 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- To establish and validate the technology platforms, tumor targets for candidate therapeutic antibody production were identified using a combination of proteomics, transcriptomics and bioinformatic approaches. Out of these lists, approximately 40 tumor targets were selected (known therapeutic antibody targets were excluded), and over 3,000 antibodies of mouse and camelid origin were then generated against these targets. Antibody generation- Once identified, the recombinant target protein of interest was produced using the NRC's high efficiency cell expression platforms in CHO or HEK293 cells and purified protein was used for immunization or panning. For targets which were difficult to express or purify, the capabilities for direct immunization with plasmid DNA constructs were utilized. Clone selection was carried out by ELISA and typically 50 antibodies/target were identified for further characterization. Antibody characterization and validation- The affinities of the antibodies were determined by SPR biosensor analysis. Reverse phase protein arrays and Western blot analysis on protein mixes and cell line extracts allowed the characterization of the specificity of the antibodies. SPR-based epitope binning was carried out in order to characterize the diversity of the antibody collections and to enable the selection of representative antibodies from each epitope bin for further analysis. Select antibodies were assessed in appropriate cell-based assays for prioritization based on function. 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 using serum free, low endotoxin media in a cGMP certified CHO cell line which is ready for transfer to CMOs or other industrial partners. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A248. 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 AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A248.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.129 | 0.048 |
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".