Discovering Health Benefits of Phytochemicals with Integrated Analysis of the Molecular Network, Chemical Properties and Ethnopharmacological Evidence

Abstract

Identifying the health benefits of phytochemicals is an essential step in drug and functional food development. While many in vitro screening methods have been developed to identify the health effects of phytochemicals, there is still room for improvement because of high cost and low productivity. Therefore, researchers have alternatively proposed in silico methods, primarily based on three types of approaches; utilizing molecular, chemical or ethnopharmacological information. Although each approach has its own strength in analyzing the characteristics of phytochemicals, previous studies have not considered them all together. Here, we apply an integrated in silico analysis to identify the potential health benefits of phytochemicals based on molecular analysis and chemical properties as well as ethnopharmacological evidence. From the molecular analysis, we found an average of 415.6 health effects for 591 phytochemicals. We further investigated ethnopharmacological evidence of phytochemicals and found that on average 129.1 (31%) of the predicted health effects had ethnopharmacological evidence. Lastly, we investigated chemical properties to confirm whether they are orally bio-available, drug available or effective on certain tissues. The evaluation results indicate that the health effects can be predicted more accurately by cooperatively considering the molecular analysis, chemical properties and ethnopharmacological evidence.

Keywords: chemical property; ethnopharmacology; health benefits; herbal medicine; molecular analysis; network medicine; phytochemical.

Figure 1

A systematic pipeline for the prediction of the health effects of phytochemicals. (a) Phenotype values of a phytochemical were obtained by calculating the propagated effects on the molecular network. In the molecular network, the random walk with restart (RWR) algorithm was performed based on direct targets (star) and indirect targets (triangle) of a phytochemical, in which the RWR results are shown as colored nodes. Based on gene-phenotype associations, sums of gene values are mapped to phenotypes. (b) For all phytochemicals, chemical properties, including physicochemical properties and physiological effects, were calculated. (c) Plants containing the phytochemical were extracted. For each extracted plant, we calculated the semantic similarity between the predicted health effect of the phytochemical and the ethnopharmacological effects of the plant. To do this, we constructed phenotypic network and calculated the shortest path length between phenotype pairs and depth of the phenotypes. Plants with the similarity score larger than the user-defined threshold were selected.

Figure 2

An overview of the findings of the ethnopharmacological use of phytochemicals. (a) From public databases, we collected ethnopharmacological evidence of medicinal plants. We then extracted phenotype-associated terms from the narrative text of the collected information by applying the MetaMap tool. (b) For a queried phytochemical, plants containing the phytochemical were extracted. (c) For each extracted plant, we mapped its ethnopharmacological effects to the phenotypic network (blue circle). Then, we calculated semantic similarities between all possible pairs of predicted health effects of phytochemicals and ethnopharmacological effects of the plant. In this example, the semantic similarity between stroke and nephrosis is 0.57, based on the semantic similarity formula, because the depth of lcs is 2, the shortest path length between nephrosis and lcs is 1 and the shortest path length between stroke and lcs is 2. Plants with a similarity score larger than 0.8 were selected.

Figure 3

The distribution of the number of predicted health effects. The distribution of the number of predicted health effects by molecular network analysis (red violin plot). The mean of predicted health effects is 415.6 ± 27.3. Next, we investigated the intersection between predicted health effects of the phytochemicals and ethnopharmacological use of the plant containing the phytochemicals. The distribution of the number of predicted health effects by molecular network analysis and ethnopharmacological use evidence (blue violin plot). The mean of predicted health effects is 129.1 ± 11.4.