Opportunities and Limitations for Untargeted Mass Spectrometry Metabolomics to Identify Biologically Active Constituents in Complex Natural Product Mixtures

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
data sets 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.