Evidence map›Paper›PMID 35735359›Full record

ArticleAntibodies (Basel, Switzerland)2022

Discovering Novel Small Molecule Compound for Prevention of Monoclonal Antibody Self-Association.

Lok Hin Lui, Christopher F van der Walle, Steve Brocchini, Ajoy Velayudhan

Abstract read
In one paragraph

Article in Antibodies (Basel, Switzerland), 2022. 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. Article
  2. Article
  3. 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

4 authors.

Lok Hin LuiUCL School of Pharmacy, University College London, London WC1N 1AX, UK.ORCID 0000-0002-5611-2277
Christopher F van der WalleDosage Form Design and Development, R&D BioPharmaceutical Development, AstraZeneca, Aaron Klug Building, Granta Park, Cambridge CB21 6GH, UK.
Steve BrocchiniUCL School of Pharmacy, University College London, London WC1N 1AX, UK.
Ajoy VelayudhanDepartment of Biochemical Engineering, University College London, London WC1E 6BT, UK.

Funding

Engineering and Physical Sciences Research Council EP/L01646X/1
6 · The paper itself

Abstract

Designing an antibody with the desired affinity to the antigen is challenging, often achieved by lengthening the hydrophobic CDRs, which can lead to aggregation and cause major hindrance to the development of successful biopharmaceutical products. Aggregation can cause immunogenicity, viscosity and stability issues affecting both the safety and quality of the product. As the hydrophobic residues on the CDR are required for direct binding to antigens, it is not always possible to substitute these residues for aggregation-reduction purposes. Therefore, discovery of specific excipients to prevent aggregation is highly desirable for formulation development. Here, we used a combination of in silico screening methods to identify aggregation-prone regions on an aggregation-prone therapeutic antibody. The most aggregation-prone region on the antibody was selected to conduct virtual screening of compounds that can bind to such regions and act as an aggregation breaker. The most promising excipient candidate was further studied alongside plain buffer formulations and formulations with trehalose using coarse-grained molecular dynamics (CGMD) simulations with MARTINI force field. Mean interaction value between two antibody molecules in each formulation was calculated based on 1024 replicates of 512 ns of such CGMD simulations. Corresponding formulations with an excipient:antibody ratio of 1:5 were compared experimentally by measuring the diffusion interaction parameter

Indexed as

aggregationantibodiesexcipientformulation designmolecular dynamicsvirtual screening

Identifiers

PMID35735359
PMCPMC9219649

What OpenQuestion holds

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Registered trials

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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.