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Rajalahti, Riikka

doi:10.1596/9780821386842_fm

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“…Statistical Comparison of Protocols.-Partial least squares regression followed by target projection 33 and calculation of selectivity ratios 24 was used for calculating all models predicting biological activity from mass spectral profiles. Double cross validation 39 was used to determine the number of PLS components for each model.…”

Section: Hierarchical Cluster Analysis and Chromatogrammentioning

confidence: 99%

“…22 In recent years, multiple different groups have sought to guide active constituent identification by integrating metabolomics data (chemical profiles) with biological activity data (biological activity profiles), enabling isolation efforts to be targeted towards active rather than abundant constituents. 9,[23][24][25] Approaches that employ multivariate statistics to interpret combined chemical and biological datasets are broadly referred to as "biochemometrics. "…”

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confidence: 99%

“…Several strategies have been developed for deciphering the meaning of PLS datasets. 9,[23][24][25] Two graphical representations, the S-plot and the selectivity ratio plot, can be employed to visualize the information in PLS models and determine which components are likely to contribute to an observed biological activity.…”

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confidence: 99%

“…However, the criterion of high covariance favors the identification of abundant compounds, while trace bioactive constituents may go undetected. 32 Identifying points of interest can also become challenging due to the large number of spectral variables. 9 The selectivity ratio plot 33 overcomes the abundance bias inherent to the S-plot by transforming the PLS components to enable quantification and ranking of each variable's impact on the modeled response, i.e.…”

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confidence: 99%

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

et al. 2019

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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.

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Section: Hierarchical Cluster Analysis and Chromatogrammentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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