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Related Concept Videos

MALDI-TOF Mass Spectrometry01:19

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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.
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MetIDfyR: An Open-Source R Package to Decipher Small-Molecule Drug Metabolism through High-Resolution Mass

Vivian Delcourt1, Agnès Barnabé1, Benoit Loup1

  • 1GIE-LCH, Laboratoire des Courses Hippiques, 15 rue de Paradis, 91370 Verrières-le-Buisson, France.

Analytical Chemistry
|September 14, 2020
PubMed
Summary
This summary is machine-generated.

MetIDfyR is a new R package that automates drug metabolite identification using in silico biotransformation prediction and mass spectrometry data mining. This open-source tool simplifies metabolite discovery in toxicology research.

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Area of Science:

  • Analytical Chemistry
  • Pharmacology
  • Computational Biology

Background:

  • Liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS) is crucial for metabolite discovery.
  • Manual searching of LC-HRMS/MS data for drug metabolites is time-consuming and challenging.
  • Limited availability of open-source software hinders efficient metabolite identification.

Purpose of the Study:

  • To introduce MetIDfyR, an open-source R package for in silico drug biotransformation prediction.
  • To facilitate mass-spectrometric data mining for metabolite discovery.
  • To provide a user-friendly and cross-platform solution for toxicologists.

Main Methods:

  • Development of an R package (MetIDfyR) for in silico prediction of Phase I/II drug biotransformations.
  • Integration of mass-spectrometric data mining capabilities within the package.
  • Validation of MetIDfyR's performance in analyzing complex biological mixtures.

Main Results:

  • MetIDfyR enables accurate in silico prediction of drug biotransformations.
  • The package effectively mines mass-spectrometric data for metabolite identification.
  • Demonstrated efficacy in identifying metabolites from in vitro and in vivo drug studies.

Conclusions:

  • MetIDfyR streamlines and enhances drug metabolite discovery and identification.
  • The open-source nature of MetIDfyR promotes accessibility and adoption in research.
  • This tool addresses the need for efficient, automated metabolite analysis in toxicology.