scholarly journals Novel multivalent design of a monoclonal antibody improves binding strength to soluble aggregates of amyloid beta

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Fadi Rofo ◽  
Jos Buijs ◽  
Ronny Falk ◽  
Ken Honek ◽  
Lars Lannfelt ◽  
...  

Abstract Background Amyloid-β (Aβ) immunotherapy is a promising therapeutic strategy in the fight against Alzheimer’s disease (AD). A number of monoclonal antibodies have entered clinical trials for AD. Some of them have failed due to the lack of efficacy or side-effects, two antibodies are currently in phase 3, and one has been approved by FDA. The soluble intermediate aggregated species of Aβ, termed oligomers and protofibrils, are believed to be key pathogenic forms, responsible for synaptic and neuronal degeneration in AD. Therefore, antibodies that can strongly and selectively bind to these soluble intermediate aggregates are of great diagnostic and therapeutic interest. Methods We designed and recombinantly produced a hexavalent antibody based on mAb158, an Aβ protofibril-selective antibody. The humanized version of mAb158, lecanemab (BAN2401), is currently in phase 3 clinical trials for the treatment of AD. The new designs involved recombinantly fusing single-chain fragment variables to the N-terminal ends of mAb158 antibody. Real-time interaction analysis with LigandTracer and surface plasmon resonance were used to evaluate the kinetic binding properties of the generated antibodies to Aβ protofibrils. Different ELISA setups were applied to demonstrate the binding strength of the hexavalent antibody to Aβ aggregates of different sizes. Finally, the ability of the antibodies to protect cells from Aβ-induced effects was evaluated by MTT assay. Results Using real-time interaction analysis with LigandTracer, the hexavalent design promoted a 40-times enhanced binding with avidity to protofibrils, and most of the added binding strength was attributed to the reduced rate of dissociation. Furthermore, ELISA experiments demonstrated that the hexavalent design also had strong binding to small oligomers, while retaining weak and intermediate binding to monomers and insoluble fibrils. The hexavalent antibody also reduced cell death induced by a mixture of soluble Aβ aggregates. Conclusion We provide a new antibody design with increased valency to promote binding avidity to an enhanced range of sizes of Aβ aggregates. This approach should be general and work for any aggregated protein or repetitive target.

Glycobiology ◽  
2021 ◽  
Author(s):  
Johanna Detzner ◽  
Daniel Steil ◽  
Gottfried Pohlentz ◽  
Nadine Legros ◽  
Johannes Müthing

Abstract Real-time interaction analysis of H1 hemagglutinin from influenza A H1N1 (A/New York/18/2009) and H7 hemagglutinin from influenza A H7N7 (A/Netherlands/219/03) with sialylated neoglycolipids (neoGLs) was performed using the surface acoustic wave (SAW) technology. The produced neoGLs carried phosphatidylethanolamine (PE) as lipid anchor and terminally sialylated lactose (Lc2, Galβ1-4Glc) or neolactotetraose (nLc4, Galβ1-4GlcNAcβ1-3Galβ1-4Glc) harbouring an N-acetylneuraminic acid (Neu5Ac). Using α2–6-sialylated neoGLs, H1 and H7 exhibited marginal attachment towards II6Neu5Ac-Lc2-PE, whereas Sambucus nigra lectin (SNL) exhibited strong binding and Maackia amurensis lectin (MAL) was negative in accordance with their known binding preference towards a distal Neu5Acα2–6Gal- and Neu5Acα2–3Gal-residue, respectively. H1 revealed significant binding towards IV6Neu5Ac-nLc4-PE when compared to weak interaction of H7, while SNL showed strong and MAL no attachment corresponding to their interaction specificities. Additional controls of MAL and SNL with α2–3-sialylated II3Neu5Ac-Lc2-PE and IV3Neu5Ac-nLc4-PE underscored the reliability of the SAW technology. Pre-exposure of model membranes spiked with α2–6-sialylated neoGLs to Vibrio cholerae neuraminidase substantially reduced the binding of the hemagglutinins and the SNL reference. Collectively, the SAW technology is capable of accurate measuring binding features of hemagglutinins towards neoGL-spiked lipid bilayers, which can be easily loaded to the functionalized biosensor gold surface thereby simulating biological membranes and suggesting promising clinical application for influenza virus research.


2019 ◽  
Vol 3 ◽  
pp. S40
Author(s):  
P Van de Kerkhof ◽  
A Pinter ◽  
M Boehnlein ◽  
S Kavanagh ◽  
J.J. Crowley

Abstract not available.


2010 ◽  
Vol 9 (4) ◽  
pp. 214-219
Author(s):  
Robyn J. Barst

Drug development is the entire process of introducing a new drug to the market. It involves drug discovery, screening, preclinical testing, an Investigational New Drug (IND) application in the US or a Clinical Trial Application (CTA) in the EU, phase 1–3 clinical trials, a New Drug Application (NDA), Food and Drug Administration (FDA) review and approval, and postapproval studies required for continuing safety evaluation. Preclinical testing assesses safety and biologic activity, phase 1 determines safety and dosage, phase 2 evaluates efficacy and side effects, and phase 3 confirms efficacy and monitors adverse effects in a larger number of patients. Postapproval studies provide additional postmarketing data. On average, it takes 15 years from preclinical studies to regulatory approval by the FDA: about 3.5–6.5 years for preclinical, 1–1.5 years for phase 1, 2 years for phase 2, 3–3.5 years for phase 3, and 1.5–2.5 years for filing the NDA and completing the FDA review process. Of approximately 5000 compounds evaluated in preclinical studies, about 5 compounds enter clinical trials, and 1 compound is approved (Tufts Center for the Study of Drug Development, 2011). Most drug development programs include approximately 35–40 phase 1 studies, 15 phase 2 studies, and 3–5 pivotal trials with more than 5000 patients enrolled. Thus, to produce safe and effective drugs in a regulated environment is a highly complex process. Against this backdrop, what is the best way to develop drugs for pulmonary arterial hypertension (PAH), an orphan disease often rapidly fatal within several years of diagnosis and in which spontaneous regression does not occur?


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