Evidence map›Paper›PMID 40686856›Full record

ArticleMolecular therapy. Nucleic acids2025

Mass photometry as a robust method for characterizing adeno-associated virus critical quality attributes in gene therapy vector.

Guangyu Wang, Lei Yu, Xi Qin, Zexin Zhou, Yong Zhou, Chenggang Liang

Abstract read
In one paragraph

Article in Molecular therapy. Nucleic acids, 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
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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

6 authors.

Guangyu WangNational Institutes for Food and Drug Control, Beijing, China.
Lei YuNational Institutes for Food and Drug Control, Beijing, China.
Xi QinNational Institutes for Food and Drug Control, Beijing, China.
Zexin ZhouGuangzhou Packgene Biotechnology Co Ltd, Guangzhou, China.
Yong ZhouNational Institutes for Food and Drug Control, Beijing, China.
Chenggang LiangNational Institutes for Food and Drug Control, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of adeno-associated viruses (AAVs) as gene therapy vectors is growing, resulting in a pressing need for efficient, reliable AAV analysis methods. Characterizing AAV critical quality attributes (CQAs), such as the abundance of empty or partially filled capsids, is paramount for developing and producing products with high efficacy, low immunogenicity, and low cost. Available methods include analytical ultracentrifugation (AUC)-considered one of the gold standards of AAV analysis technique-and mass photometry-a newer method based on single-particle light scattering. Mass photometry is a fast, low-cost tool that uses little sample and is suitable for environments requiring good manufacturing practices (GMP) compliance. Here, we assess mass photometry's performance in AAV characterization by using it to analyze a sample of empty AAVs, to measure proportions of empty vs. partially filled vs. full AAVs, to quantify loading in samples ranging from 100% empty to nearly 100% loaded, and to measure samples containing transgenes of different lengths. Comparing to AUC data where appropriate, we find that the two methods give comparable results. Based on its accuracy, precision, measurement capabilities, and practical advantages (e.g., speed and low cost), we conclude that mass photometry is an ideal AAV analysis tool.

Indexed as

AAVAAV vectorsadeno-associated virusanalytical ultracentrifugationAUCCQAscritical quality attributesgene therapygene therapy vectorsGMPinline analyticsmass photometryMT: Delivery Strategiespartially filled particles

Identifiers

PMID40686856
PMCPMC12271428

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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.