Evidence map›Paper›PMID 36145517›Full record

ArticlePharmaceutics2022

Computation-Aided Design of Albumin Affibody-Inserted Antibody Fragment for the Prolonged Serum Half-Life.

Na Hyun Kwon, Jae Hun Lee, Inchan Kwon

Abstract read
In one paragraph

Article in Pharmaceutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Na Hyun KwonSchool of Materials Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea.
Jae Hun LeeSchool of Materials Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea.
Inchan KwonSchool of Materials Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea.ORCID 0000-0003-0806-4116

Funding

National Research Foundation of Korea 2019R1A2C10849101
6 · The paper itself

Abstract

Single-chain variable fragments (scFvs) have been recognized as promising agents in cancer therapy. However, short serum half-life of scFvs often limits clinical application. Fusion to albumin affibody (ABD) is an effective and convenient half-life extension strategy. Although one terminus of scFv is available for fusion of ABD, it is also frequently used for fusion of useful moieties such as small functional proteins, cytokines, or antibodies. Herein, we investigated the internal linker region for ABD fusion instead of terminal region, which was rarely explored before. We constructed two internally ABD-inserted anti-HER2 4D5scFv (4D5-ABD) variants, which have short (4D5-S-ABD) and long (4D5-L-ABD) linker length respectively. The model structures of these 4D5scFv and 4D5-ABD variants predicted using the deep learning-based protein structure prediction program (AlphaFold2) revealed high similarity to either the original 4D5scFv or the ABD structure, implying that the functionality would be retained. Designed 4D5-ABD variants were expressed in the bacterial expression system and characterized. Both 4D5-ABD variants showed anti-HER2 binding affinity comparable with 4D5scFv. Binding affinity of both 4D5-ABD variants against albumin was also comparable. In a pharmacokinetic study in mice, the 4D5-ABD variants showed a significantly prolonged half-life of 34 h, 114 times longer than that of 4D5scFv. In conclusion, we have developed a versatile scFv platform with enhanced pharmacokinetic profiles with an aid of deep learning-based structure prediction.

Indexed as

albumin affibodycancerfusion proteinhalf-life extensionsingle-chain antibody fragmentstructure prediction

Identifiers

PMID36145517
PMCPMC9500697

What OpenQuestion holds

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Registered trials

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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.