Evidence map›Paper›PMID 38607316›Full record

ArticleBiotechnology progress

A systemic approach to identifying sequence frameworks that decrease mAb production in a transient Chinese hamster ovary cell expression system.

Alana C Szkodny, Kelvin H Lee

Open access · bronzeAbstract read
In one paragraph

Article in Biotechnology progress. 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
0.2field-weighted citation impact, top 46% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

2 authors at 1 institution in 1 country.

Alana C SzkodnyDepartment of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware, USA.ORCID 0000-0002-9071-9363
Kelvin H LeeDepartment of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware, USA.ORCID 0000-0003-0908-2981
University of Delaware · US

Funding

Predictive Modeling & Optimal Control Framework for Model-Based Epidemic Response in DelawareP20GM103446 · NIGMS · UNIVERSITY OF DELAWARE · PI Anjana Narayan Bhat · 2012 to 2026
$67.2M
Delaware Biotechnology InstituteNational Science Foundation 1624698NIGMS NIH HHS P20 GM103446NIGMS NIH HHS P20GM103446NIST DOC 70NANB17H002State of Delaware
6 · The paper itself

Abstract

Monoclonal antibodies (mAbs) are often engineered at the sequence level for improved clinical performance yet are rarely evaluated prior to candidate selection for their "developability" characteristics, namely expression, which can necessitate additional resource investments to improve the manufacturing processes for problematic mAbs. A strong relationship between primary sequence and expression has emerged, with slight differences in amino acid sequence resulting in titers differing by up to an order of magnitude. Previous work on these "difficult-to-express" (DTE) mAbs has shown that these phenotypes are driven by post-translational bottlenecks in antibody folding, assembly, and secretion processes. However, it has been difficult to translate these findings across cell lines and products. This work presents a systematic approach to study the impact of sequence variation on mAb expression at a larger scale and under more industrially relevant conditions. The analysis found 91 mutations that decreased transient expression of an IgG

Indexed as

Antibodies, MonoclonalCricetulusAmino Acid SequenceAnimalsCHO CellsCricetinaeImmunoglobulin GMutationProtein EngineeringAntibodies, MonoclonalImmunoglobulin GCHO cellsdevelopabilitydifficult‐to‐expressmonoclonal antibodiestransient expression

Identifiers

PMID38607316
PMCPMC11470104
OpenAlexW4394750270

What OpenQuestion holds

Textmetadata
LicenceTDM
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.