Evidence map›Paper›PMID 29276835›Full record

ArticleAnalytical chemistry2018

Sensitive, High-Throughput, and Robust Trapping-Micro-LC-MS Strategy for the Quantification of Biomarkers and Antibody Biotherapeutics.

Ming Zhang, Bo An, Yang Qu, Shichen Shen, Wei Fu, Yuan-Ju Chen, Xue Wang, Rebeccah Young, John M Canty, Joseph P Balthasar and 8 more

Abstract readEvaluation Study
In one paragraph

Article in Analytical chemistry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 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

18 authors.

Ming ZhangDepartment of Pharmaceutical Sciences, University at Buffalo, State University of New York , Buffalo, New York 14214, United States.
Bo AnDepartment of Pharmaceutical Sciences, University at Buffalo, State University of New York , Buffalo, New York 14214, United States.
Yang QuDepartment of Pharmaceutical Sciences, University at Buffalo, State University of New York , Buffalo, New York 14214, United States.
Shichen ShenDepartment of Pharmaceutical Sciences, University at Buffalo, State University of New York , Buffalo, New York 14214, United States.
Wei FuDepartment of Pharmaceutical Sciences, University at Buffalo, State University of New York , Buffalo, New York 14214, United States.
Yuan-Ju ChenDepartment of Pharmaceutical Sciences, University at Buffalo, State University of New York , Buffalo, New York 14214, United States.
Xue WangDepartment of Pharmaceutical Sciences, University at Buffalo, State University of New York , Buffalo, New York 14214, United States.
Rebeccah YoungDivision of Cardiovascular Medicine, Western New York Department of Veterans of Affairs Medical Center , Buffalo, New York 14203, United States.
John M CantyDivision of Cardiovascular Medicine, Western New York Department of Veterans of Affairs Medical Center , Buffalo, New York 14203, United States.
Joseph P BalthasarDepartment of Pharmaceutical Sciences, University at Buffalo, State University of New York , Buffalo, New York 14214, United States.
Keeley MurphyThermo Scientific , San Jose, California 95134, United States.
Debadeep BhattacharyyaThermo Scientific , San Jose, California 95134, United States.
Jonathan JosephsThermo Scientific , San Jose, California 95134, United States.
Luca FerrariRoche Pharmaceutical Research and Early Development, Roche Innovation Center Basel , Basel CH-4070, Switzerland.
Shaolian ZhouRoche Pharmaceutical Research and Early Development, Roche Innovation Center Basel , Basel CH-4070, Switzerland.
Surendra BansalRoche Pharmaceutical Research and Early Development, Roche Innovation Center New York , New York, New York 10016, United States.
Faye VazvaeiRoche Pharmaceutical Research and Early Development, Roche Innovation Center New York , New York, New York 10016, United States.
Jun QuDepartment of Pharmaceutical Sciences, University at Buffalo, State University of New York , Buffalo, New York 14214, United States.ORCID 0000-0002-1346-6809

Funding

University of Buffalo Clinical and Translational Science Institute - Supplement SchulyerUL1TR001412 · NCATS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI MURPHY, TIMOTHY F · 2015 to 2024
$33.8M
Systems Biology of GlycosylationR01HL103411 · NHLBI · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI NEELAMEGHAM, SRIRAM · 2011 to 2025
$7.1M
Metabolic Adaptation and Functional Recovery of Hibernating MyocardiumR01HL061610 · NHLBI · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI CANTY, JOHN M · 2000 to 2018
$6.0M
CHRONIC ADAPTATIONS TO MYOCARDIAL ISCHEMIAR01HL055324 · NHLBI · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI CANTY, JOHN M · 1996 to 2014
$4.2M
Synergistic Pharmacologicjntervention for Prevention of ROP (SPIPROP STUDY)U54HD071594 · NICHD · SUNY DOWNSTATE MEDICAL CENTER · PI BEHARRY, KAY · 2011 to 2015
$3.6M
Transcription mechanism of Myrf for central nervous system myelinationR01NS094181 · NINDS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI PARK, YUNGKI · 2015 to 2019
$1.8M
Posttranslational modification of the regulatory RNA binding protein, ZFP3R21AI125746 · NIAID · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI READ, LAURIE K. · 2016 to 2017
$439k
Pathogenesis of Myelin Protein Zero Neuropathies in Transgenic MiceR56NS096104 · NINDS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI WRABETZ, LAWRENCE · 2016 to 2016
$396k
DRUG DISCOVERY PLATFORM FOR PROTEIN ARGININE METHYLTRANSFERASE INHIBITORSR41GM121174 · NIGMS · CH3 BIOSYSTEMS, LLC · PI ALETTA, JOHN MICHAEL · 2016 to 2016
$158k
Preventing and Reversing Interstitial Fibrosis in HFpEFI01BX002659 · VA · VA WESTERN NEW YORK HEALTHCARE SYSTEM · PI Brian Raymond Weil · 2016 to 2026
–
BLRD VA I01 BX002659NCATS NIH HHS UL1 TR001412NHLBI NIH HHS R01 HL055324NHLBI NIH HHS R01 HL061610NHLBI NIH HHS R01 HL103411NIAID NIH HHS R21 AI125746NICHD NIH HHS U54 HD071594NIGMS NIH HHS R41 GM121174NINDS NIH HHS R01 NS094181NINDS NIH HHS R56 NS096104
6 · The paper itself

Abstract

For LC-MS-based targeted quantification of biotherapeutics and biomarkers in clinical and pharmaceutical environments, high sensitivity, high throughput, and excellent robustness are all essential but remain challenging. For example, though nano-LC-MS has been employed to enhance analytical sensitivity, it falls short because of its low loading capacity, poor throughput, and low operational robustness. Furthermore, high chemical noise in protein bioanalysis typically limits the sensitivity. Here we describe a novel trapping-micro-LC-MS (T-μLC-MS) strategy for targeted protein bioanalysis, which achieves high sensitivity with exceptional robustness and high throughput. A rapid, high-capacity trapping of biological samples is followed by μLC-MS analysis; dynamic sample trapping and cleanup are performed using pH, column chemistry, and fluid mechanics separate from the μLC-MS analysis, enabling orthogonality, which contributes to the reduction of chemical noise and thus results in improved sensitivity. Typically, the selective-trapping and -delivery approach strategically removes >85% of the matrix peptides and detrimental components, markedly enhancing sensitivity, throughput, and operational robustness, and narrow-window-isolation selected-reaction monitoring further improves the signal-to-noise ratio. In addition, unique LC-hardware setups and flow approaches eliminate gradient shock and achieve effective peak compression, enabling highly sensitive analyses of plasma or tissue samples without band broadening. In this study, the quantification of 10 biotherapeutics and biomarkers in plasma and tissues was employed for method development. As observed, a significant sensitivity gain (up to 25-fold) compared with that of conventional LC-MS was achieved, although the average run time was only 8 min/sample. No appreciable peak deterioration or loss of sensitivity was observed after >1500 injections of tissue and plasma samples. The developed method enabled, for the first time, ultrasensitive LC-MS quantification of low levels of a monoclonal antibody and antigen in a tumor and cardiac troponin I in plasma after brief cardiac ischemia. This strategy is valuable when highly sensitive protein quantification in large sample sets is required, as is often the case in typical biomarker validation and pharmaceutical investigations of antibody therapeutics.

Indexed as

Amino Acid SequenceAnimalsAntibodies, MonoclonalBiomarkersChromatography, LiquidEquipment DesignHigh-Throughput Screening AssaysHumansImmunoglobulin GLimit of DetectionMass SpectrometryMicePeptidesProteinsRatsSwineAntibodies, MonoclonalBiomarkersImmunoglobulin GPeptidesProteins

Identifiers

PMID29276835
PMCPMC5960441

What OpenQuestion holds

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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.