Enhanced metabolite identification with MS(E) and a semi-automated software for structural elucidation

Area of Science:

• Analytical Chemistry
• Metabolomics
• Drug Discovery

Background:

• Metabolite identification is crucial in drug discovery and development.
• Current methods like liquid chromatography/tandem mass spectrometry (LC/MSMS) are time-consuming.
• Advancements in mass spectrometry and software are needed to streamline this process.

Purpose of the Study:

• To compare the efficacy of quadrupole time-of-flight (QTOF) MS(E) and MS/MS data for metabolite rationalization.
• To evaluate the performance of Mass-MetaSite software for semi-automated metabolite identification.
• To assess the potential of MS(E) and Mass-MetaSite in reducing analysis time and workload.

Main Methods:

• Utilized QTOF-generated MS(E) and MS/MS data for metabolite identification.
• Employed Mass-MetaSite software, which combines MS raw data with predicted metabolism sites.
• Evaluated metabolite identification in human liver microsomes for 19 commercial and 15 in-house compounds.

Main Results:

• MS(E) data yielded the same metabolite structure assignments as MS/MS data for 96% of identified metabolites.
• Mass-MetaSite correctly assigned the metabolite structure in the first rank for approximately 80% of cases compared to manual inspection.
• The use of MS(E) demonstrated a significant potential to reduce overall analysis time.

Conclusions:

• MS(E) is a viable and time-efficient alternative to MS/MS for metabolite identification.
• Mass-MetaSite software effectively assists biotransformation scientists by automating structure assignment, reducing workload.
• The combined approach of MS(E) and Mass-MetaSite offers a powerful solution for accelerating metabolite identification in drug development.