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A novel anti-TNF scFv constructed with human antibody frameworks and antagonistic peptides

Overview of attention for article published in Immunologic Research, June 2015
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

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1 X user
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6 patents

Citations

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8 Dimensions

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28 Mendeley
Title
A novel anti-TNF scFv constructed with human antibody frameworks and antagonistic peptides
Published in
Immunologic Research, June 2015
DOI 10.1007/s12026-015-8667-8
Pubmed ID
Authors

Shusheng Geng, Hong Chang, Weisong Qin, Ming Lv, Yan Li, Jiannan Feng, Beifen Shen

Abstract

The introduction of TNF inhibitors has revolutionized the treatment of some chronic inflammatory diseases, e.g., rheumatoid arthritis and Crohn's disease. However, immunogenicity is one of the important mechanisms behind treatment failure, and generally, switching to another TNF inhibitor will be the first choice for patients and doctors, which results in unmet need for novel anti-TNF agents. Small antibody molecules with less number of epitope may be valuable in less immunogenicity. In this study, with the help of computer-guided molecular design, single-chain variable fragment (scFv) TSA2 was designed using consensus frameworks of human antibody variable region as scaffold to display anti-TNF antagonistic peptides. TSA2 showed evidently improved bioactivity over TSA1 (anti-TNF scFv explored before) and almost similar activity as S-Remicade (the scFv form of Remicade, anti-TNF antibody approved by FDA), especially in inhibiting TNF-induced cytotoxicity and NF-κB activation. Human antibody consensus frameworks with less immunogenicity have been used in the designing of VH domain antibody, scFv TSA1 and TSA2. A serial of TNF-related works convinced us that the novel design strategy was feasible and could be used to design inhibitors targeting more other molecules than TNF-α. More importantly, these designed inhibitors derived from computer modeling may form a virtual antibody library whose size depends on the number of candidate antagonistic peptides. It will be molecular-targeted virtual antibody library because of the specific antagonistic peptides and the potential antibodies could be determined by virtual screening and then confirmed by biologic experiments.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 25%
Researcher 6 21%
Student > Master 3 11%
Student > Bachelor 2 7%
Professor > Associate Professor 2 7%
Other 2 7%
Unknown 6 21%
Readers by discipline Count As %
Medicine and Dentistry 6 21%
Agricultural and Biological Sciences 6 21%
Pharmacology, Toxicology and Pharmaceutical Science 2 7%
Nursing and Health Professions 1 4%
Biochemistry, Genetics and Molecular Biology 1 4%
Other 5 18%
Unknown 7 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 January 2024.
All research outputs
#7,394,455
of 23,275,636 outputs
Outputs from Immunologic Research
#266
of 915 outputs
Outputs of similar age
#87,363
of 267,697 outputs
Outputs of similar age from Immunologic Research
#5
of 18 outputs
Altmetric has tracked 23,275,636 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 915 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one has gotten more attention than average, scoring higher than 69% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 267,697 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.