Evidence map›Paper›PMID 38168220›Full record

ArticlebioRxiv : the preprint server for biology2023

Facile generation of biepitopic antibodies with intrinsic agonism for activating receptors in the tumor necrosis factor superfamily.

Harkamal S Jhajj, John S Schardt, Namir Khalasawi, Emily L Yao, Timon S Lwo, Na-Young Kwon, Ryen L O'Meara, Alec A Desai, Peter M Tessier

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Harkamal S JhajjDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
John S SchardtDepartment of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA.
Namir KhalasawiDepartment of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA.
Emily L YaoDepartment of Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.
Timon S LwoDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Na-Young KwonDepartment of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA.
Ryen L O'MearaDepartment of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Alec A DesaiDepartment of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Peter M TessierDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.ORCID 0000-0002-3220-007X
University of Michigan · US

Funding

CD98hc Brain Shuttles for Delivering Off-the-shelf Neuroprotective Antibodies in Alzheimer's DiseaseR01AG080016 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Colin Fred Greineder, Peter M Tessier · 2023 to 2026
$3.2M
Design and Evolution of Polyvalent Domain Antibodies Specific for Tau AggregatesRF1AG059723 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KANE, RAVI S., TESSIER, PETER M · 2018 to 2022
$2.4M
Structure-guided antibody targeting of pre-selected epitopes in amyloidogenic aggregatesR35GM136300 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI TESSIER, PETER M · 2020 to 2024
$1.5M
Administrative Supplement: Activity-based Discovery and OptimizationF32GM137513 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SCHARDT, JOHN SAMUEL · 2020 to 2022
$141k
NIA NIH HHS R01 AG080016NIA NIH HHS RF1 AG059723NIGMS NIH HHS F32 GM137513NIGMS NIH HHS R35 GM136300
6 · The paper itself

Abstract

Agonist antibodies that activate cellular receptors are being pursued for therapeutic applications ranging from neurodegenerative diseases to cancer. For the tumor necrosis factor (TNF) receptor superfamily, higher-order clustering of three or more receptors is key to their potent activation. This can be achieved using antibodies that recognize two unique epitopes on the same receptor and mediate receptor superclustering. However, identifying compatible pairs of antibodies to generate biepitopic antibodies (also known as biparatopic antibodies) for activating TNF receptors typically requires animal immunization and is a laborious and unpredictable process. Here, we report a simple method for systematically identifying biepitopic antibodies that potently activate TNF receptors without the need for additional animal immunization. Our approach uses off-the-shelf, receptor-specific IgG antibodies, which lack intrinsic (Fc-gamma receptor-independent) agonist activity, to first block their corresponding epitopes. Next, we perform selections for single-chain antibodies from human nonimmune libraries that bind accessible epitopes on the same ectodomains using yeast surface display and fluorescence-activated cell sorting. The selected single-chain antibodies are finally fused to the light chains of IgGs to generate human tetravalent antibodies that engage two different receptor epitopes and mediate potent receptor activation. We highlight the broad utility of this approach by converting several existing clinical-stage antibodies against TNF receptors, including ivuxolimab and pogalizumab against OX40 and utomilumab against CD137, into biepitopic antibodies with highly potent agonist activity. We expect that this widely accessible methodology can be used to systematically generate biepitopic antibodies for activating other receptors in the TNF receptor superfamily and many other receptors whose activation is dependent on strong receptor clustering.

Indexed as

4–1BBagonistantibodybiepitopicCD137FcγRmAbOX40receptor clusteringTNFTNFR

Identifiers

PMID38168220
PMCPMC10760063
OpenAlexW4389610031

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.