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Toward a Consensus on Applying Quantitative Liquid Chromatography-Tandem Mass Spectrometry Proteomics in Translational Pharmacology Research: A White Paper.


ABSTRACT: Quantitative translation of information on drug absorption, disposition, receptor engagement, and drug-drug interactions from bench to bedside requires models informed by physiological parameters that link in vitro studies to in vivo outcomes. To predict in vivo outcomes, biochemical data from experimental systems are routinely scaled using protein quantity in these systems and relevant tissues. Although several laboratories have generated useful quantitative proteomic data using state-of-the-art mass spectrometry, no harmonized guidelines exit for sample analysis and data integration to in vivo translation practices. To address this gap, a workshop was held on September 27 and 28, 2018, in Cambridge, MA, with 100 experts attending from academia, the pharmaceutical industry, and regulators. Various aspects of quantitative proteomics and its applications in translational pharmacology were debated. A summary of discussions and best practices identified by this expert panel are presented in this "White Paper" alongside unresolved issues that were outlined for future debates.

SUBMITTER: Prasad B 

PROVIDER: S-EPMC6692196 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Toward a Consensus on Applying Quantitative Liquid Chromatography-Tandem Mass Spectrometry Proteomics in Translational Pharmacology Research: A White Paper.

Prasad Bhagwat B   Achour Brahim B   Artursson Per P   Hop Cornelis E C A CECA   Lai Yurong Y   Smith Philip C PC   Barber Jill J   Wisniewski Jacek R JR   Spellman Daniel D   Uchida Yasuo Y   Zientek Michael A MA   Unadkat Jashvant D JD   Rostami-Hodjegan Amin A  

Clinical pharmacology and therapeutics 20190726 3


Quantitative translation of information on drug absorption, disposition, receptor engagement, and drug-drug interactions from bench to bedside requires models informed by physiological parameters that link in vitro studies to in vivo outcomes. To predict in vivo outcomes, biochemical data from experimental systems are routinely scaled using protein quantity in these systems and relevant tissues. Although several laboratories have generated useful quantitative proteomic data using state-of-the-ar  ...[more]

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