Abstract
The recent explosion in gut microbiome research has demonstrated the importance of metabolite-target interactions in the development of different pathologies. This suggest that gut-targeted drugs modulating these interactions would provide a new drug modality besides that of systemically bioavailable small molecules, that could tap from this growing knowledge, and would have little distribution and safety issues. In the present work we analyze a large set of gut metabolites in comparison with serum metabolites and drugs. We find structural and physicochemical similarity between the serum metabolites and the drug sets, and dissimilarity with the gut metabolite set. In addition, we find that the inclusion of chemical class is necessary in order to appropriately understand gut permanence, in contrast to classical oral permeation models (e.g. rule-of-five). To help in gut-targeted drug design, we provide a simple scoring scheme for use in medicinal chemistry, plus a machine learning model to use in cheminformatic applications.
Supplementary materials
Title
Figures S1-S10
Description
Distribution of physicochemical properties for the whole set and for each chemical class of gut and serum metabolites. Both violin plots and boxplots (without outliers, for clarity purposes) are displayed.
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Title
Table S1
Description
Distribution of physicochemical properties for each chemical class in gut and serum metabolites. For each combination, the median and IQR is displayed for both gut and serum metabolites, plus the CES, and p-value. For ionization classes, the first and second most abundant are displayed for both gut and serum metabolites.
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