Evidence map›Paper›PMID 40492479›Full record

ArticleBioanalysis2025

Design of experiments for the optimization of sample preparation for bottom-up targeted protein LC-MS/MS workflows.

Szabolcs Szarka, Margret Thorsteinsdottir, Robert Wheller

Abstract read
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Article in Bioanalysis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Szabolcs SzarkaResolian, Bioanalytics, Fordham, Cambridgeshire, UK.ORCID 0000-0002-6216-6757
Margret ThorsteinsdottirFaculty of Pharmaceutical Sciences, University of Iceland, Reykjavik, Iceland.ORCID 0000-0002-0649-8503
Robert WhellerResolian, Bioanalytics, Fordham, Cambridgeshire, UK.ORCID 0000-0002-8853-1070

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsDesign of experiments (DOE) is a versatile and efficient approach to tackle complex scientific problems. We aimed to assess its feasibility in the optimization of the multistep, involved bottom-up sample preparation for the UPLC-MS/MS analysis of proteins. MATERIALS AND

methodsThe model analyte was a human IgG1 monoclonal antibody which was spiked into rat plasma and processed further by reduction, alkylation, and digestion for the subsequent UPLC-MS/MS analysis. The Modde Go software was used for the generation of experimental designs and for processing, analyzing and the interpretation of the data.

resultsDOE screening revealed that urea made the biggest improvement on the surrogate peptide responses, while guanidine significantly suppressed them. DOE optimization resulted in a 2-, 10-, 10- and 50-fold response increase for the respective DTLM, FNWY, TPEV and VVSV surrogate peptide even after a short, <3-h sample preparation, when compared to a legacy method that required 2-day preparation.

conclusionsThe DOE approach was applied successfully for the comprehensive optimization of eight denaturation, reduction and digestion parameters. DOE was found to be an efficient tool for protein sample preparation optimization, and the predictive power of the DOE models was successfully demonstrated.

Indexed as

Research DesignTandem Mass SpectrometryAnimalsAntibodies, MonoclonalChromatography, High Pressure LiquidChromatography, LiquidHumansImmunoglobulin GLiquid Chromatography-Mass SpectrometryRatsWorkflowAntibodies, MonoclonalImmunoglobulin GdenaturationDesign of experiments (DOE)digestionIgG1PTMsreductionresponse surface methodologyUPLC-MS/MS

Identifiers

PMID40492479
PMCPMC12160604

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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.