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mAbs Volume 10, 2018 - Issue 6
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Homology modeling and structure-based design improve hydrophobic interaction chromatography behavior of integrin binding antibodies

Arif JethaDepartment of Antibody Discovery and Development, Northern Biologics Inc., Toronto, Ontario, Canada
,
Nels ThorsteinsonDepartment of Scientific Services, Chemical Computing Group ULC, Montreal, Quebec, CanadaCorrespondence[email protected] [email protected]
,
Yazen JmeianDepartment of Antibody Discovery and Development, Northern Biologics Inc., Toronto, Ontario, Canada
,
Ajitha JeganathanDepartment of Antibody Discovery and Development, Northern Biologics Inc., Toronto, Ontario, Canada
,
Patricia GiblinDepartment of Antibody Discovery and Development, Northern Biologics Inc., Toronto, Ontario, Canada
&
Johan FranssonDepartment of Antibody Discovery and Development, Northern Biologics Inc., Toronto, Ontario, Canada
Pages 890-900 | Received 06 Apr 2018, Accepted 08 May 2018, Published online: 15 Aug 2018

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Andrew B. Waight, David Prihoda, Rojan Shrestha, Kevin Metcalf, Marc Bailly, Marco Ancona, Talal Widatalla, Zachary Rollins, Alan C Cheng, Danny A. Bitton & Laurence Fayadat-Dilman. (2023) A machine learning strategy for the identification of key in silico descriptors and prediction models for IgG monoclonal antibody developability properties. mAbs 15:1.
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Nels Thorsteinson, John R. Gunn, Kenneth Kelly, Will Long & Paul Labute. (2021) Structure-based charge calculations for predicting isoelectric point, viscosity, clearance, and profiling antibody therapeutics. mAbs 13:1.
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Boris Grinshpun, Nels Thorsteinson, Joao NS Pereira, Friedrich Rippmann, David Nannemann, Vanita D. Sood & Yves Fomekong Nanfack. (2021) Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies. mAbs 13:1.
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