Evidence map›Paper›PMID 41226509›Full record

ReviewInternational journal of molecular sciences2025

Technologies for Monoclonal Antibody Discovery and Development.

Kyung Ho Han, Yi-Chuan Li, Rabia Parveen, Srimathi Venkataraman, Chih-Wei Lin

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
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

5 authors.

Kyung Ho HanDepartment of Biological Sciences and Biotechnology, Hannam University, Daejeon 34054, Republic of Korea.ORCID 0000-0001-6907-1462
Yi-Chuan LiDepartment of Biological Science and Technology, China Medical University, Taichung 406040, Taiwan.
Rabia ParveenInternational Master Program of Biomedical Sciences, China Medical University, Taichung 406040, Taiwan.
Srimathi VenkataramanGraduate Institute of Biological Science and Technology, China Medical University, Taichung 406040, Taiwan.
Chih-Wei LinCancer Biology and Precision Therapeutics Center, China Medical University, Taichung 406040, Taiwan.

Funding

China Medical University Yingcai Scholar Fund CMU110-YTY-03
6 · The paper itself

Abstract

Monoclonal antibodies (mAbs) represent one of the most successful classes of biopharmaceuticals, with more than 100 approved for treating oncological, immunological, and infectious diseases. Antibody discovery and development have been driven by diverse methodologies. Classical strategies such as mouse hybridoma technology, phage display, transgenic mouse models, and single B cell isolation have enabled the generation of high-affinity therapeutic antibodies. Beyond binding affinity, recent innovations in combinatorial antibody libraries have facilitated the selection of functional antibodies within cellular environments, revealing their ability to act as agonists or antagonists and influence signal transduction pathways. These insights expand therapeutic applications by enabling modulation of complex cellular responses. Recent breakthroughs in artificial intelligence, involving antibody generation supported by rapidly growing antibody sequence and structure databases, are transforming computational protein design. This review highlights five major approaches (hybridoma technology, phage display, transgenic mouse models, and single B cell isolation, de novo antibody design) for antibody discovery and development. These approaches offer innovative strategies designed to accelerate the discovery process and enhance therapeutic outcomes for human diseases.

Indexed as

Antibodies, MonoclonalDrug DiscoveryAnimalsB-LymphocytesCell Surface Display TechniquesHumansHybridomasMiceMice, TransgenicPeptide LibraryAntibodies, MonoclonalPeptide Libraryantibodyantibody engineeringde novo synthesishybridomaphage display

Identifiers

PMID41226509
PMCPMC12610488

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.