Abstract A248: Integrated therapeutic antibody development at the National Research Council of Canada.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,129 | 0,048 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».