Manufacturing considerations underpinning viral and non-viral platform selection
Notice bibliographique
Résumé
With an ever-increasing range of viral and non-viral technologies available to advanced biotherapeutics developers, manufacturing considerations must play a key role in the decision-making process behind platform selection. These considerations include the current level of innovation in the bioprocessing toolkit and its corresponding capability to address the specific challenges facing individual technology areas.In this article a panel of experts spanning the lentiviral vector (LVV), exosome, and oncolytic virus fields discuss the impact of manufacturing considerations on their respective platform selection and ongoing product/process development strategies, comparing the state of the art in enabling technology in each application area, and discussing related challenges, needs, and opportunities.Allison Hagerman, P.Eng., PMP, MBTVice President, Product Development, Oncolytics BiotechA Professional Engineer focused on biotechnology, Ms. Hagerman joined Oncolytics in 2010 and has been integral to the progress of its product development program ever since. Prior to being appointed as Vice President of Product Development, Ms. Hagerman was the Director, Manufacturing and Engineering from 2013-2017 and Project Manager from 2010-2013, during which time she led the process performance qualification for pelareorep drug substance. Ms. Hagerman is a Professional Engineer (P.Eng., APEGA) and Project Management Professional (PMP, PMI). She holds a Master of Biomedical Technology (MBT) degree from the University of Calgary, and B.Sc. degrees in both Chemical Engineering and Biological Sciences. She is an accomplished equestrian and spends her spare time on horseback.Aaron NoyesVice President of Development, Codiak BiosciencesAaron is a Vice President of Integrated Drug Substance Development at Codiak BioSciences where he leads a team focused on developing scalable production processes for exosomes and robust technology to load varied payloads into exosomes. At the start of his industrial career, Aaron worked at Millipore before joining Wyeth Biopharma/Pfizer Biotech for 12 years where he developed purification processes and focused on scale-up of biologics, including mAbs, recombinant proteins, ADCs, vaccines, cell therapies, and viral vectors. Aaron received a B.S in Biochemistry from the University of Massachusetts at Amherst, a M.E in Biotechnology Engineering from Tufts University and an Engineering Doctorate in Biochemical Engineering at University College London.Laurens SierkstraSenior Director, Business Leader, Purification Thermo Fisher ScientificLaurens received his PhD in biotechnology in 1994 from the University of Utrecht after studying biology at the University of Leiden. He then joined Unilever as Project Manager and Unit Leader. In 2005, after the spinout of BAC BV from Unilever, he became CEO of BAC BV and set up the business in using single- domain antibodies for affinity purification, called CaptureSelect, which was sold in 2013 to Life Technologies. Since the acquisition by Thermo Fisher Scientific, he has been the business leader for the affinity purification business within the Bioproduction Division.Dr. Vladimir SlepushkinGlobal Head of Manufacturing, MedTherapy BiotechnologyDr. Vladimir Slepushkin is Global Head of Manufacturing at MedTherapy Biotechnology. He is leading all functions associated with manufacturing of CAR-T cells and viral vectors. Previously he was Executive Director of Vector Technology at Autolus Therapeutics, leading process development for manufacturing of lentivirus vectors in suspension cell culture, guiding assay development to support process de¬velopment for lentiviral vectors, managing CMO for GMP vector production and T-cell processing. Before that, Dr. Slepushkin was directing research vector core, and providing lentiviral, retroviral and AAV vectors for Kite Pharma. Vladimir proved successful in developing novel high-quality products by managing diverse technical groups and cross-functional teams, developing first-in-class clinical product from scratch, including facilities, equipment, manufacturing process, quality systems, regulatory CMC submissions and clinical trials design. He has proven expertise in technically understanding and leading the development and improvement of cell culture and purification processes, and operations and analytical methods, adhering to customer, regulatory, safety and environmental requirements and guidelines. Vladi¬mir is experienced in identifying and resolving regulatory and manufacturing technical problems, as well as intellectual property assessment and licensing. He has authored 61 scientific papers in peer-reviewed journals and he’s an author on 14 patents and patent applications.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».