metabolite identification

Metabolite Identification at XenoGesis – avoiding the potential pitfalls of using software

In this modern day and age of sophisticated computer technologies and more complex MS functionalities, there is an increasing reliance on computer software to analyse and process data for metabolite identification compared to 25-30 years ago when it was a highly skilled analyst with an LC/MS/MS instrument. In the early days this was a triple quadrupole instrument before the advent of the Q-ToF, which provided accurate mass capability, and subsequently the Orbitrap instrument. The latest instruments have significantly more capability, being able to carry out full scan MS and some form of non-targeted MS/MS within the same run saving on analysis time.

Instrument manufacturers are also providing sophisticated software packages which can go through the MS data to flag possible metabolites, both the usual expected phase 1 and 2 biotransformations but increasingly using the structure and a suitable control sample to flag possible unexpected “metabolites” that may be due to cleavage products or just peaks not present in the control. However, these have a number of advantages and disadvantages.


  • Can pick out expected metabolites quite easily and quickly.
  • Using the structure and accurate mass MS/MS data can quickly suggest possible structures for the MS/MS fragments.


  • Any software package is only as good as how it is set up.
    • If settings are too open they can generate a very long list of possible metabolites, sometimes in the hundreds, of which the majority are not related wasting significant time filtering them out and identifying relevant peaks.
    • If settings are too restrictive, to reduce the number of false positives, then there is the danger of missing unusual metabolites and the list of possible metabolites can still be quite long.
  • Some packages filter the data, not allowing the raw data to be seen as it has decided what is relevant.
  • Due to the complexity of the software it can take some considerable processing time, while a highly skilled and experienced analyst can go through the data faster generating a smaller but more relevant list of possible metabolites.
  • With vendors recommending their software and an increasing use of and reliance on the software within the industry, there is the danger of that skill and expertise not being developed. This provides the increasing risk of metabolites being missed as reports are generated based on what the software has found and identified rather than a skilled analyst.

At XenoGesis we have two highly skilled people in metabolite identification with over 40 years’ combined experience in carrying out metabolite identification studies using a variety of instruments. Metabolites are identified using their skill and experience along with the extensive capabilities of our Thermo Scientific Q Exactive Focus Hybrid Quadrupole-Orbitrap mass spectrometer to process the data manually on a scan by scan basis, using suitable control samples to identify metabolites in the test samples, rather than using metabolite identification software packages.

As well as providing a number of options for metabolite profiling in support of projects in Discovery, we have also provided support for clinical metabolite profiling studies in man by creating a single AUC0-24h pooled blood or plasma sample for identification of metabolites in support of the MIST guidelines.

We can offer a range of tailored services in order to meet your needs. This can be the identification of the major metabolic route (to support the chemistry design stage), cross-species hepatocyte comparisons for identification of appropriate species for safety studies or animal in vivo studies at the later stages of the drug discovery process for potential drug candidates. We offer different reports depending on the level of detail required from a simple table of proposed structures based on the MS/MS data to provision of extracted ion chromatograms, MS and MS/MS spectra with an interpretation of the data for each metabolite.

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