Evidence map›Paper›PMID 41007619›Full record

ReviewBiomedicines2025

Advances in Techniques for the Structure and Functional Optimization of Therapeutic Monoclonal Antibodies.

Chenchen He, Weijin Huang, Xi Wu, Huanzhang Xia

Abstract readReview
In one paragraph

Review in Biomedicines, 2025. 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. 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

4 authors.

Chenchen HeSchool of Life Science and Bio-Pharmaceutics, Shenyang Pharmaceutical University, Shenyang 110016, China.
Weijin HuangState Key Laboratory of Drug Regulatory Science, Division of HIV/AIDS and Sex-Transmitted Virus Vaccines, Institute for Biological Product Control, National Institutes for Food and Drug Control (NIFDC), Beijing 102629, China.ORCID 0000-0002-4246-8889
Xi WuState Key Laboratory of Drug Regulatory Science, Division of HIV/AIDS and Sex-Transmitted Virus Vaccines, Institute for Biological Product Control, National Institutes for Food and Drug Control (NIFDC), Beijing 102629, China.
Huanzhang XiaSchool of Life Science and Bio-Pharmaceutics, Shenyang Pharmaceutical University, Shenyang 110016, China.

Funding

National Key Research and Development Program (China) 2022YFC2303404
6 · The paper itself

Abstract

Monoclonal antibodies (mAbs), as potent therapeutic agents, have been widely applied in the treatment of various major diseases, including infectious diseases, autoimmune disorders, cancers, and neurodegenerative diseases. However, early-generation mAbs were limited by high immunogenicity, short half-life, and insufficient affinity, which compromised their therapeutic efficacy. With technological advancements, novel approaches such as high-throughput screening and glycosyl modification have been introduced to improve the performance of mAbs. Furthermore, computer-aided design techniques-including molecular docking, molecular dynamics simulations, and artificial intelligence -based methods-are increasingly being employed to accelerate the optimization process. This review summarizes recent progress in the optimization of therapeutic mAbs, with a focus on technological breakthroughs and applications in affinity enhancement, development of broad-spectrum mAbs, specificity modulation, immunogenicity reduction, and stability improvement. Additionally, it discusses current challenges and future directions in antibody optimization. This review aims to provide insights and references for the development and optimization of next-generation antibody drugs, ultimately promoting the clinical application of safer and more effective mAb-based therapies.

Indexed as

antibody optimizationartificial intelligencecomputer-aided design techniquesoptimization strategiestherapeutic monoclonal antibody

Identifiers

PMID41007619
PMCPMC12467787

What OpenQuestion holds

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LicenceCC BY
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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.