Evidence map›Paper›PMID 24492294›Full record

ArticlemAbs

High-throughput screening for developability during early-stage antibody discovery using self-interaction nanoparticle spectroscopy.

Yuqi Liu, Isabelle Caffry, Jiemin Wu, Steven B Geng, Tushar Jain, Tingwan Sun, Felicia Reid, Yuan Cao, Patricia Estep, Yao Yu and 3 more

Open access · goldAbstract read
In one paragraph

Article in mAbs. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers.

0numbers the graph read from it
0cells of the map it votes in
87citing papers in PubMed
4.0field-weighted citation impact, top 7% of its field
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

87 citing papers in PubMed, 134 citations in OpenAlex.

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  13. A comprehensive overview of recent advances in generative models for antibodies.Computational and structural biotechnology journal · 2024
    Review
  14. Article
  15. DOTAD: A Database of Therapeutic Antibody Developability.Interdisciplinary sciences, computational life sciences · 2024
    Article
  16. Review
  17. Article
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  20. Article

27 more citing papers are in PubMed but not listed here.

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

13 authors at 3 institutions in 3 countries.

Yuqi LiuProtein Analytics; Adimab; Lebanon, NH USA.
Isabelle CaffryProtein Analytics; Adimab; Lebanon, NH USA.
Jiemin WuCenter for Biotechnology & Interdisciplinary Studies; Isermann Dept. of Chemical & Biological Engineering; Rensselaer Polytechnic Institute; Troy, NY USA.
Steven B GengCenter for Biotechnology & Interdisciplinary Studies; Isermann Dept. of Chemical & Biological Engineering; Rensselaer Polytechnic Institute; Troy, NY USA.
Tushar JainComputational Biology; Adimab; Palo Alto, CA USA.
Tingwan SunProtein Analytics; Adimab; Lebanon, NH USA.
Felicia ReidProtein Analytics; Adimab; Lebanon, NH USA.
Yuan CaoProtein Analytics; Adimab; Lebanon, NH USA.
Patricia EstepProtein Analytics; Adimab; Lebanon, NH USA.
Yao YuProtein Analytics; Adimab; Lebanon, NH USA.
Maximiliano VásquezComputational Biology; Adimab; Palo Alto, CA USA.
Peter M TessierCenter for Biotechnology & Interdisciplinary Studies; Isermann Dept. of Chemical & Biological Engineering; Rensselaer Polytechnic Institute; Troy, NY USA.
Yingda XuProtein Analytics; Adimab; Lebanon, NH USA.
Adimab (United States) · USRensselaer Polytechnic Institute · USPalo Alto Institute · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The discovery of monoclonal antibodies (mAbs) that bind to a particular molecular target is now regarded a routine exercise. However, the successful development of mAbs that (1) express well, (2) elicit a desirable biological effect upon binding, and (3) remain soluble and display low viscosity at high concentrations is often far more challenging. Therefore, high throughput screening assays that assess self-association and aggregation early in the selection process are likely to yield mAbs with superior biophysical properties. Here, we report an improved version of affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS) that is capable of screening large panels of antibodies for their propensity to self-associate. AC-SINS is based on concentrating mAbs from dilute solutions around gold nanoparticles pre-coated with polyclonal capture (e.g., anti-Fc) antibodies. Interactions between immobilized mAbs lead to reduced inter-particle distances and increased plasmon wavelengths (wavelengths of maximum absorbance), which can be readily measured by optical means. This method is attractive because it is compatible with dilute and unpurified mAb solutions that are typical during early antibody discovery. In addition, we have improved multiple aspects of this assay for increased throughput and reproducibility. A data set comprising over 400 mAbs suggests that our modified assay yields self-interaction measurements that are well-correlated with other lower throughput assays such as cross-interaction chromatography. We expect that the simplicity and throughput of our improved AC-SINS method will lead to improved selection of mAbs with excellent biophysical properties during early antibody discovery.

Indexed as

Antibodies, ImmobilizedAntibodies, MonoclonalAntibody AffinityCells, CulturedDrug DiscoveryGoldHigh-Throughput Screening AssaysHumansImmunotherapyMetal NanoparticlesProtein MultimerizationReproducibility of ResultsSpectrum AnalysisSurface Plasmon ResonanceAntibodies, ImmobilizedAntibodies, MonoclonalGoldaggregationantibody developabilitycross-interactionhigh-throughput screeningnanoparticleself-associationself-interaction

Identifiers

PMID24492294
PMCPMC3984336
OpenAlexW2021160162

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.