Evidence map›Paper›PMID 40350687›Full record

ArticlemAbs2025

Assessing subvisible particle risks in monoclonal antibodies: insights from quartz crystal microbalance with dissipation, machine learning, and in silico analysis.

Yibo Wang, Alexis Hanford, Mehdi Boroumand, Cavan Kalonia, Jesse Leissa, Mitali Shah, Tony Pham, Theodore Randolph, Indira Prajapati

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in mAbs, 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

9 authors.

Yibo WangDosage Form Design and Development, BioPharmaceuticals Development, R&D, AstraZeneca, Gaithersburg, MD, USA.ORCID 0009-0001-3087-8776
Alexis HanfordDosage Form Design and Development, BioPharmaceuticals Development, R&D, AstraZeneca, Gaithersburg, MD, USA.
Mehdi BoroumandData Science and Modeling, BioPharmaceuticals Development, R&D, AstraZeneca, Gaithersburg, MD, USA.
Cavan KaloniaDosage Form Design and Development, BioPharmaceuticals Development, R&D, AstraZeneca, Gaithersburg, MD, USA.
Jesse LeissaDosage Form Design and Development, BioPharmaceuticals Development, R&D, AstraZeneca, Gaithersburg, MD, USA.
Mitali ShahDosage Form Design and Development, BioPharmaceuticals Development, R&D, AstraZeneca, Gaithersburg, MD, USA.
Tony PhamBiologics Engineering, Oncology R&D, AstraZeneca, Gaithersburg, MD, USA.
Theodore RandolphDepartment of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA.
Indira PrajapatiDosage Form Design and Development, BioPharmaceuticals Development, R&D, AstraZeneca, Gaithersburg, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Throughout the lifecycle of biopharmaceutical development and manufacturing, monoclonal antibodies (mAbs) are subjected to diverse interfacial stresses and encounter various container surfaces. These interactions can cause the formation of subvisible particles (SVPs) that complicate developability and stability assessments of the drug products. This study leverages quartz crystal microbalance with dissipation (QCM-D), an interfacial characterization technique, as well as both

Indexed as

Antibodies, MonoclonalMachine LearningQuartz Crystal Microbalance TechniquesAdsorptionComputer SimulationHumansParticle SizeAntibodies, MonoclonalDevelopabilityliquid-solid interfaceMachine learningmonoclonal antibodiesquartz crystal microbalance with dissipationsubvisible particles

Identifiers

PMID40350687
PMCPMC12077436

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.