Evidence map›Paper›PMID 36346594›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

Structure-Based Optimization of Antibody-Based Biotherapeutics for Improved Developability: A Practical Guide for Molecular Modelers.

Nels Thorsteinson, Stephen R Comeau, Sandeep Kumar

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. 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. How can we discover developable antibody-based biotherapeutics?Frontiers in molecular biosciences · 2023
    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

3 authors.

Nels ThorsteinsonScientific Services Manager, Biologics, Chemical Computing Group ULC, Montreal, QC, Canada.
Stephen R ComeauComputational Biochemistry and Bioinformatics Group, Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc., Ridgefield, CT, USA.
Sandeep KumarComputational Biochemistry and Bioinformatics Group, Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc., Ridgefield, CT, USA. Sandeep_2.Kumar@boehringer-ingelheim.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A great effort to avoid known developability risks is now more often being made earlier during the lead candidate discovery and optimization phase of biotherapeutic drug development. Predictive computational strategies, used in the early stages of antibody discovery and development, to mitigate the risk of late-stage failure of antibody candidates, are highly valuable. Various structure-based methods exist for accurately predicting properties critical to developability, and, in this chapter, we discuss the history of their development and demonstrate how they can be used to filter large sets of candidates arising from target affinity screening and to optimize lead candidates for developability. Methods for modeling antibody structures from sequence and detecting post-translational modifications and chemical degradation liabilities are also discussed.

Indexed as

AntibodiesDrug DevelopmentAntibodiesAggregationAntibodyChemical degradationDevelopabilityPhysicochemical properties

Identifiers

What OpenQuestion holds

Textmetadata
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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.