Opportunities and Limitations for Untargeted Mass Spectrometry Metabolomics to Identify Biologically Active Constituents in Complex Natural Product Mixtures
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Abstract
Compounds derived from natural sources represent the majority of small-molecule drugs
utilized today. Plants, owing to their complex biosynthetic pathways, are poised to
synthesize diverse secondary metabolites that selectively target biological macromolecules.
Despite the vast chemical landscape of botanicals, drug discovery programs from these
sources have diminished due to the costly and time-consuming nature of standard practices
and high rates of compound rediscovery. Untargeted metabolomics approaches that integrate
biological and chemical datasets potentially enable the prediction of active constituents
early in the fractionation process. However, data acquisition and data processing
parameters may have major impacts on the success of models produced. Using an inactive
botanical mixture spiked with known antimicrobial compounds, untargeted mass spectrometry-based
metabolomics data were combined with bioactivity data to produce selectivity ratio
models subjected to a variety of data acquisition and data processing parameters.
Selectivity ratio models were used to identify active constituents that were intentionally
added to the mixture, along with an additional antimicrobial compound, randainal (
5 ), which was masked by the presence of antagonists in the mixture. These studies
found that data-processing approaches, particularly data transformation and model
simplification tools using a variance cutoff, had significant impacts on the models
produced, either masking or enhancing the ability to detect active constituents in
samples. The current study highlights the importance of the data processing step for
obtaining reliable information from metabolomics models and demonstrates the strengths
and limitations of selectivity ratio analysis to comprehensively assess complex botanical
mixtures.