MetWorks™ 1.1.0 Metabolite Identification Software Launched

Chromatography

MetWorks™ 1.1.0 Metabolite Identification Software Launched

22 Apr, 2008

Published over 18 years ago. See the latest and most current information on Chromatography.

Thermo Fisher Scientific Inc, has launched MetWorksâ„¢ 1.1.0, an updated version of its proven metabolite identification software. Thermo Scientific Metworksâ„¢ 1.1.0 software facilitates automated acquisition, processing and reporting of LC/MSn data in support of biotransformation studies. MetWorks software is compatible with all Thermo Scientific mass spectrometers capable of performing MS/MS and MSn fragmentation.
In addition, MetWorks allows high resolution accurate mass data from the Thermo Scientific LTQ Orbitrapâ„¢ and LTQ FTâ„¢ instruments to be fully leveraged.
MetWorks™ 1.1.0 software features enhanced tools that facilitate distinguishing xenobiotic components from endogenous biological matrix interferences in LC/MSn chromatograms and spectra. Key improvements to the software include: Automatic generation of a Data-Dependent Parent Mass Table (DDPT). Component filtering to exclude duplicates originating from adducts and isotopic contributions. Flexibility to apply up to six multiple mass defect filters (MMDF’s), which are
based on high-resolution, exact mass and mass deficiencies of the parent-drug and its putative metabolites. Chromatograms and spectra filtered using MMDF’s contain fewer endogenous compounds and excipient materials, thereby reducing the manual intervention of eliminating false positives from the results. Isotopic spectral pattern recognition techniques, as well as MSn spectral correlation procedures are used to identify common productions and mass shifts related to specific fragment neutral losses. The structure column in the MetView™ table can be used to attach a molecular structure drawing (.mol file) to a potential putative candidate. Integration of Mass Frontier™ predictive fragmentation software within the MetWorks™ 1.1.0 workflow process has drastically improved the assessment for automated metabolite identification studies.

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