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公开(公告)号:US20210284716A1
公开(公告)日:2021-09-16
申请号:US16880804
申请日:2020-05-21
Applicant: ImmunityBio, Inc.
Inventor: Kayvan Niazi , C. Anders Olson , Shiho Tanaka , John H. Lee , Wendy Higashide , Patrick Soon-Shiong
IPC: C07K14/81
Abstract: An ACE2-Fc hybrid construct is used as a therapeutic and/or analytic entity to treat an individual infected with a coronavirus or to detect a coronavirus in an analyte. In selected embodiments, the Fc portion of the hybrid construct is an IgA Fc portion, and in still further embodiments the ACE2 portion has a mutation that reduces or abolishes ACE2 catalytic activity.
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公开(公告)号:US20210284713A1
公开(公告)日:2021-09-16
申请号:US17082994
申请日:2020-10-28
Applicant: ImmunityBio, Inc.
Inventor: Kayvan Niazi , Jay Gardner Nelson , Annie Shin , Clifford Anders Olson , Shiho Tanaka
IPC: C07K14/705 , C07K14/005 , C12N15/86 , A61K39/215
Abstract: Compositions and methods are presented for prevention and/or treatment of a coronavirus disease wherein the composition comprises a recombinant entity. The recombinant entity comprises a nucleic acid that encodes a extracellular portion of CD40 ligand (CD40L) coupled by a flexible linker to a coronavirus 2 (CoV2) spike protein and/or a CoV2 nucleocapsid protein.
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公开(公告)号:US20240327484A1
公开(公告)日:2024-10-03
申请号:US18607825
申请日:2024-03-18
Applicant: ImmunityBio, Inc.
Inventor: Shiho Tanaka , Clifford Anders Olson , Wendy Higashide , Manju Saxena
IPC: C07K14/54 , C07K14/715 , C12N5/0783
CPC classification number: C07K14/5443 , C07K14/5434 , C07K14/7155 , C12N5/0646 , C07K2319/30 , C12N2501/2315 , C12N2501/39
Abstract: Provided herein are novel soluble-fusion protein complexes comprising interleukin 18 (IL-18) and interleukin 12 (IL-12) polypeptide domains. Also provided are compositions comprising the soluble-fusion protein complexes as well as methods of using the soluble-fusion protein complexes.
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公开(公告)号:US20240029820A1
公开(公告)日:2024-01-25
申请号:US18225098
申请日:2023-07-21
Applicant: Nant Holdings IP, LLC , ImmunityBio, Inc.
Inventor: Bing Song , Shiho Tanaka , Clifford Anders Olson , Phillip Yang , Patrick Soon-Shiong
Abstract: Techniques are provided for computing affinity for protein-protein interaction. 3D structure models of the first and second protein parts are generated using a trained first deep learning model. A 3D structure model of a protein-protein complex comprising the first and the second protein parts is generated using a trained second deep learning model. A low energy score state is determined for the 3D structure models of each of the first and second protein parts, and the protein-protein complex. A relax algorithm applied to amino acid side chain and backbone 3D structure models determines a low energy score state for the 3D structure models. Based on the low energy score states, an energy score is generated for the 3D structure models, and a score difference is determined between the energy scores, where the score difference defines a binding affinity score.
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