Evidence map›Paper›PMID 36759378›Full record

ArticleThe AAPS journal2023

Best Practices in mAb and Soluble Target Assay Selection for Quantitative Modelling and Qualitative Interpretation.

David Fairman, Huaping Tang

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Article in The AAPS journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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.

David FairmanClinical Pharmacology Modelling and Simulation, GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire, SG1 2NY, UK.
Huaping TangBioanalysis Immunogenicity and Biomarkers, GSK Research, 1250 South Collegeville Road, Pennsylvania, 19426, Collegeville, USA. huaping.x.tang@gsk.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biologics, especially monoclonal antibodies (mAbs), are an increasingly important part of the drug discovery and development portfolio across the pharmaceutical industry. To enable robust demonstration of pillars 1 and 2 [1] for mAbs, specialised assays are required to measure the complex interactions between mAb and target. This is especially important for the interpretation of soluble target interactions. In some instances, multiple assays with overlapping purposes (e.g., developing both complex and total assays) have been developed. In retrospect, these efforts may have led to excessive time and resources spent in assay development and the generation of data that is contradictory or misleading. Our recommendation is to invest resources early into the development of total assays for both mAb and target. Free target assay data may be inaccurate and report higher levels of free target than are present in the sample at collection due to re-equilibrium during measurement. Total assay formats are inherently less sensitive to the effects of sample preparation, assay conditions, and re-equilibration than free or complex assays. It is acknowledged that pathology/pharmacology is ultimately driven by the free target and knowledge of its dynamics are critical. However, generation of appropriate total target data and using model-based estimation of free target concentrations is a more robust approach than utilisation of direct assay derived estimates. Where free data are utilised, the potential biases should be prospectively considered when developing the assay and utilising the data for quantitative analyses.

Indexed as

Antibodies, MonoclonalBiological ProductsDrug DiscoveryAntibodies, MonoclonalBiological Productsfree assayligand binding assayPK/PD modellingtarget engagementtotal assay

Identifiers

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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.