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

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.

Innovative omics-based approaches for prioritisation and targeted isolation of natural products – new strategies for drug discovery

“…For pipelines integrating complex multilayer information, the use of validated bioactive reference standards spiked in extracts is highly recommended to evaluate the efficiency of the prioritisation strategy envisioned. 6 When prioritisation is not directly connected to given bioactivity results but aims at nding novel chemodiverse NPs, the virtual screening of scaffolds (docking, modelling chemical space, etc.) obtained may represent an alternative means to valorise the new chemical entities discovered.…”

“…This shows that data-processing approaches (data transformation and model simplication tools using a variance cut-off) have signicant impacts on model capacities to detect active constituents in CNEs. 6 Applications of such methods reveal their interesting potential as well as their current limitations. This also highlights the fact that they must be validated prior to implementation in prioritisation workows to assess complex botanical mixtures.…”

“…This highlight article does not claim to exhaustively list all methodologies developed and used by chemists and biologists for the discovery of new bioactive metabolites. We instead discuss the most relevant and/or innovative approaches based on their design bioactivity, chemical composition and/or genetic data mainly by means of chemometric/ biochemometric/chemogenomic methods 5,6 and show through select examples how they can accelerate the discovery of new bioactive NPs. We also discuss the most effective methods enabling the targeted isolation of NPs from metabolite proling data for a rapid, efficient and complete characterisation of bioactive and/or novel NPs.…”

Oligosaccharides and Complex Carbohydrates: A New Paradigm for Cranberry Bioactivity

“…For example, approximately~6500 individual compounds, 55-57% of the total compounds detected by a UPLC-TOF-MS metabolomics study [90], have been reported to be similar across multiple cranberry cultivars. These values are likely to be underestimates, however, as this study was limited by the inherent limitations of all metabolomics methods [91] and therefore focused on a relatively small subset of all possible phytoconstituents. Comparisons of chemical complexity in relation to bioactivity are further influenced by the disregard typically given to primary metabolites and compounds of relatively high MW (>1000 Da), and by the relative inability of any one detection or analysis method to identify or characterize all chemical components present [92].…”

“…The detection of soluble oligosaccharides in urine is further complicated by structural features and solution behaviors that require the application of specialized detection techniques such as those used previously for this chemical class [158,172,176,180,184]. Many modern, standard, high-throughput metabolomics techniques are currently unable to detect soluble neutral oligosaccharides (DP 3-10) in biofluids such as urine or plasma due to methodological and parameter limitations [91,92,172,185,186]. Such platforms are typically focused on small molecule (<1000 Da) isolation, and underivatized neutral oligosaccharides are invisible to detection methods such as UV and are poorly ionized in GC-MS or LC-ESI-MS [28,172].…”