Evidence map›Paper›PMID 41266728›Full record

ReviewPharmaceutical research2026

Emerging Technologies and Integrated Interdisciplinary Strategies for Mitigating Protein Aggregation in Therapeutic Formulations.

Haomin Wu, QinXi Fan, Zheng Zhang, Yuanhui Ji

Abstract readReview
PubMed Publisher
In one paragraph

Review in Pharmaceutical research, 2026. 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.

No citing paper in PubMed yet.

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.

Haomin WuJiangsu Province Hi-Tech Key Laboratory for Biomedical Research, School of Chemistry and Chemical Engineering, Southeast University, No.2 Southeast University Road, Nanjing, 211189, Jiangsu, China.
QinXi FanJiangsu Province Hi-Tech Key Laboratory for Biomedical Research, School of Chemistry and Chemical Engineering, Southeast University, No.2 Southeast University Road, Nanjing, 211189, Jiangsu, China.
Zheng ZhangCollege of Chemical Engineering, Shenyang University of Chemical Technology, 11th Street, Shenyang Economic and Technological Development Zone, Shenyang, 110142, Liaoning, China. zhengzhang@syuct.edu.cn.
Yuanhui JiJiangsu Province Hi-Tech Key Laboratory for Biomedical Research, School of Chemistry and Chemical Engineering, Southeast University, No.2 Southeast University Road, Nanjing, 211189, Jiangsu, China. yuanhui.ji@seu.edu.cn.

Funding

National Natural Science Foundation of China 22278070National Natural Science Foundation of China 22441020
6 · The paper itself

Abstract

backgroundTherapeutic proteins are playing an increasingly important role in marketed drugs and clinical candidates. However, their development still faces major challenges, particularly aggregation.

objectivesThis review explores the recent advancements, current limitations, and future directions of new research methods for therapeutic proteins.

resultsCharacterization techniques identify aggregation tendencies and elucidate underlying mechanisms, while computational chemistry provides microscopic insights into the aggregation process. Theoretical modeling and machine learning offer tools for predicting protein stability, enabling high-throughput screening in early formulation development.

conclusionFostering interdisciplinary collaboration will be essential. The integration of diverse approaches offers a more comprehensive understanding of protein aggregation and unlocks new opportunities for innovation in protein formulation development.

Indexed as

Protein AggregatesProteinsAnimalsChemistry, PharmaceuticalDrug CompoundingHumansMachine LearningProtein StabilityProtein AggregatesProteinsaggregationcharacterizationcomputational chemistrymachine learningtheoretical modeltherapeutic proteins

Identifiers

What OpenQuestion holds

Textmetadata
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.