Evidence map›Paper›PMID 39174718›Full record

ArticlePharmaceutical research2024

Generic Enrichment Method for Liquid Chromatography-Multiple Reaction Monitoring-Mass Spectrometry Assay for Quantitative Measurement of Biological Therapeutics in Serum.

Sisi Zhang, Hui Xiao, Ning Li

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Article in Pharmaceutical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Sisi ZhangRegeneron Pharmaceuticals Inc, 777 Old Saw Mill River Road, Tarrytown, New York, 10,591-6706, United States.
Hui XiaoRegeneron Pharmaceuticals Inc, 777 Old Saw Mill River Road, Tarrytown, New York, 10,591-6706, United States. hui.xiao@regeneron.com.
Ning LiRegeneron Pharmaceuticals Inc, 777 Old Saw Mill River Road, Tarrytown, New York, 10,591-6706, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe study aims to leverage the capabilities of Liquid Chromatography-Multiple Reaction Monitoring Mass Spectrometry (LC-MRM), a key technique in quantifying therapeutic proteins in pharmacokinetic studies. The focus is on demonstrating an enrichment method using ProteoMiner beads, which can be integrated with LC-MRM to detect low-abundance biotherapeutics in serum, such as monoclonal antibodies and gene therapy products.

methodsThe ProteoMiner enrichment method was employed and integrated with LC-MRM. The lower limit of quantification of serum drug substance concentrations was compared with that achievable with immuno-enrichment. The method used commercially available reagents, eliminating the need for assay-specific antibodies and reducing potential bias and development time.

resultsThe ProteoMiner enrichment method showed comparable performance to immuno-enrichment, meeting traditional assay requirements in terms of precision, accuracy, and specificity.

conclusionsThe ProteoMiner enrichment method, when combined with LC-MRM, offers a reliable and efficient alternative to immuno-enrichment for detecting and quantifying low-abundance biotherapeutics in serum. This approach, which uses commercially available reagents, can eliminate the bias and time associated with the development of assay-specific antibodies. It holds significant potential for accelerating pharmacokinetic analysis in both early and late stages of pharmaceutical development.

Indexed as

Antibodies, MonoclonalAnimalsBiological ProductsChromatography, LiquidHumansLimit of DetectionMass SpectrometryTandem Mass SpectrometryAntibodies, MonoclonalBiological Productsliquid chromatography-multiple reaction monitoring mass spectrometrypharmacokinetic studiesproteominer beads-based enrichment method

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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.