Literature DB >> 34906325

Data-independent acquisition (DIA): An emerging proteomics technology for analysis of drug-metabolizing enzymes and transporters.

Jiapeng Li1, Logan S Smith1, Hao-Jie Zhu2.   

Abstract

Data-independent acquisition (DIA) proteomics is a recently-developed global mass spectrometry (MS)-based proteomics strategy. In a DIA method, precursor ions are isolated into pre-defined isolation windows and fragmented; all fragmented ions in each window are then analyzed by a high-resolution mass spectrometer. DIA proteomics analysis is characterized by a broad protein coverage, high reproducibility, and accuracy, and its combination with advances in other techniques such as sample preparation and computational data analysis could lead to further improvements in assay performances. DIA technology has been increasingly utilized in various proteomics studies, including quantifying drug-metabolizing enzymes and transporters. Quantitative proteomics study of drug-metabolizing enzymes and transporters could lead to a better understanding of pharmacokinetics and pharmacodynamics and facilitate drug development. This review summarizes the application of DIA technology in proteomic analysis of drug-metabolizing enzymes and transporters.
Copyright © 2021 Elsevier Ltd. All rights reserved.

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Year:  2021        PMID: 34906325      PMCID: PMC8674493          DOI: 10.1016/j.ddtec.2021.06.006

Source DB:  PubMed          Journal:  Drug Discov Today Technol        ISSN: 1740-6749


  36 in total

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Authors:  Tujin Shi; Ehwang Song; Song Nie; Karin D Rodland; Tao Liu; Wei-Jun Qian; Richard D Smith
Journal:  Proteomics       Date:  2016-08       Impact factor: 3.984

3.  Large-scale multiplex absolute protein quantification of drug-metabolizing enzymes and transporters in human intestine, liver, and kidney microsomes by SWATH-MS: Comparison with MRM/SRM and HR-MRM/PRM.

Authors:  Kenji Nakamura; Mio Hirayama-Kurogi; Shingo Ito; Takuya Kuno; Toshihiro Yoneyama; Wataru Obuchi; Tetsuya Terasaki; Sumio Ohtsuki
Journal:  Proteomics       Date:  2016-07-08       Impact factor: 3.984

4.  Comparative Proteomics Analysis of Human Liver Microsomes and S9 Fractions.

Authors:  Xinwen Wang; Bing He; Jian Shi; Qian Li; Hao-Jie Zhu
Journal:  Drug Metab Dispos       Date:  2019-11-07       Impact factor: 3.922

5.  Multiplexed, Scheduled, High-Resolution Parallel Reaction Monitoring on a Full Scan QqTOF Instrument with Integrated Data-Dependent and Targeted Mass Spectrometric Workflows.

Authors:  Birgit Schilling; Brendan MacLean; Jason M Held; Alexandria K Sahu; Matthew J Rardin; Dylan J Sorensen; Theodore Peters; Alan J Wolfe; Christie L Hunter; Michael J MacCoss; Bradford W Gibson
Journal:  Anal Chem       Date:  2015-09-30       Impact factor: 6.986

6.  Absolute protein quantification of clinically relevant cytochrome P450 enzymes and UDP-glucuronosyltransferases by mass spectrometry-based targeted proteomics.

Authors:  C Gröer; D Busch; M Patrzyk; K Beyer; A Busemann; C D Heidecke; M Drozdzik; W Siegmund; S Oswald
Journal:  J Pharm Biomed Anal       Date:  2014-08-17       Impact factor: 3.935

7.  Comparison of protein expression between human livers and the hepatic cell lines HepG2, Hep3B, and Huh7 using SWATH and MRM-HR proteomics: Focusing on drug-metabolizing enzymes.

Authors:  Jian Shi; Xinwen Wang; Lingyun Lyu; Hui Jiang; Hao-Jie Zhu
Journal:  Drug Metab Pharmacokinet       Date:  2018-03-10       Impact factor: 3.614

Review 8.  Advancing the sensitivity of selected reaction monitoring-based targeted quantitative proteomics.

Authors:  Tujin Shi; Dian Su; Tao Liu; Keqi Tang; David G Camp; Wei-Jun Qian; Richard D Smith
Journal:  Proteomics       Date:  2012-04       Impact factor: 3.984

9.  Enhanced differential expression statistics for data-independent acquisition proteomics.

Authors:  Tomi Suomi; Laura L Elo
Journal:  Sci Rep       Date:  2017-07-19       Impact factor: 4.379

Review 10.  Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS)-Based Proteomics of Drug-Metabolizing Enzymes and Transporters.

Authors:  Jiapeng Li; Hao-Jie Zhu
Journal:  Molecules       Date:  2020-06-11       Impact factor: 4.411

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Authors:  Miao Zhang; Ning Wang; Xiang-Shen Guo; Lin-Lin Wang; Peng-Fei Wang; Zhi-Peng Cao; Fu-Yuan Zhang; Zi-Wei Wang; Da-Wei Guan; Rui Zhao
Journal:  Int J Legal Med       Date:  2022-09-29       Impact factor: 2.791

2.  Proteomics Combined with RNA Sequencing to Screen Biomarkers of Sepsis.

Authors:  Chenglin Wang; Yang Li; Shilin Li; Muhu Chen; Yingchun Hu
Journal:  Infect Drug Resist       Date:  2022-09-21       Impact factor: 4.177

  2 in total

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