Evidence map›Paper›PMID 39381002›Full record

ReviewFrontiers in immunology2024

Advancements in mammalian display technology for therapeutic antibody development and beyond: current landscape, challenges, and future prospects.

Peter Slavny, Manjunath Hegde, Achim Doerner, Kothai Parthiban, John McCafferty, Stefan Zielonka, Rene Hoet

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Protein engineering: status report.Protein engineering, design & selection : PEDS · 2026
    Review
  6. Dosa: A method to covalently barcode proteins for high-throughput biochemistry.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  7. Review
  8. Review
  9. Review
  10. Review
  11. Review
  12. Article
  13. Article
  14. From code to cure: AI-Driven innovation in monoclonal antibody development.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2025
    Article
  15. Article
  16. Review
  17. Article
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

7 authors.

Peter Slavny *Discovery & Engineering Division, Iontas Ltd./FairJourney Biologics, Cambridge, United Kingdom.
Manjunath Hegde *Technology Division, Iontas/FairJourney Biologics, Cambridge, United Kingdom.
Achim DoernerAntibody Discovery & Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany.
Kothai ParthibanDiscovery & Engineering Division, Iontas Ltd./FairJourney Biologics, Cambridge, United Kingdom.
John McCaffertyMaxion Therapeutics, Cambridge, United Kingdom.
Stefan ZielonkaAntibody Discovery & Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany.
Rene HoetTechnology Division, Iontas/FairJourney Biologics, Cambridge, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The evolving development landscape of biotherapeutics and their growing complexity from simple antibodies into bi- and multi-specific molecules necessitates sophisticated discovery and engineering platforms. This review focuses on mammalian display technology as a potential solution to the pressing challenges in biotherapeutic development. We provide a comparative analysis with established methodologies, highlighting key aspects of mammalian display technology, including genetic engineering, construction of display libraries, and its pivotal role in hit selection and/or developability engineering. The review delves into the mechanisms underpinning developability-driven selection via mammalian display and their broader implications. Applications beyond antibody discovery are also explored, alongside advancements towards function-first screening technologies, precision genome engineering and AI/ML-enhanced libraries, situating them in the context of mammalian display. Overall, the review provides a comprehensive overview of the current mammalian display technology landscape, underscores the expansive potential of the technology for biotherapeutic development, addresses the critical challenges for the full realisation of this potential, and examines advances in related disciplines that might impact the future application of mammalian display technologies.

Indexed as

Genetic EngineeringAnimalsAntibodies, MonoclonalCell Surface Display TechniquesHumansMammalsAntibodies, Monoclonalantibody engineeringantibody librariesbiologics discovery technologiesdevelopability screeningfunctional screeninggenetic engineering

Identifiers

PMID39381002
PMCPMC11459229

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.