Modern drug discovery using ethnobotany: A large-scale cross-cultural analysis of traditional medicine reveals common therapeutic uses

Abstract

For millennia, numerous cultures and civilizations have relied on traditional remedies derived from plants to treat a wide range of conditions and ailments. Here, we systematically analyzed ethnobotanical patterns across taxonomically related plants, demonstrating that congeneric medicinal plants are more likely to be used for treating similar indications. Next, we reconstructed the phytochemical space covered by medicinal plants to reveal that (i) taxonomically related medicinal plants cover a similar phytochemical space, and (ii) chemical similarity correlates with similar therapeutic usage. Lastly, we present several case scenarios illustrating how mining this information can be used for drug discovery applications, including: (i) investigating taxonomic hotspots around particular indications, (ii) exploring shared patterns of congeneric plants located in different geographic areas, but which have been used to treat the same indications, and (iii) showing the concordance between ethnobotanical patterns among non-taxonomically related plants and the presence of shared bioactive phytochemicals.

Keywords: Drug delivery system; Health sciences; Therapeutics.

Graphical abstract

Figure 1

Medicinal usage similarity between taxonomically related and unrelated plants (A and B) Each raincloud plot illustrates the distribution of medicinal plants used to treat a disease from one of the 23 disease categories (see Table S2). Taxonomic levels are ordered from left to right based on taxonomic similarity, where plants belonging to the same genus are the most similar, and “random plants” contain 100,000 randomly selected medicinal plant pairs from a different family. We show the same analysis performed by extracting the medicinal usage vectors from either (A) scientific literature or (B) curated ethnobotanical databases. Legend: ∗∗∗∗ = q-value < 1.00e⁻⁰⁴; ns = non-significant.

Figure 2

Landscape of the therapeutic uses of medicinal plants Schematic phylogenetic tree of the genera containing medicinal plants and their associated indication areas. The heatmap is colored by Relative Citation Count (RCC), which corresponds to the total number of literature citations per indication in each genus (i.e., sum of the disease vectors for all species in a genus) normalized by the total number of citations in the given genus. We zoom in on a heatmap that corresponds to genera present in the Phaseoleae tribe.

Figure 3

Medicinal usage similarity between plants of the same genus located in common and distinct geographic areas The set of medicinal plants used corresponds to the ones reported by ethnobotanical databases. We grouped plants based on the relative number of overlapping countries they are located in using the Szymkiewicz-Simpson coefficient (SSC). While plant pairs of the same genus that do not come from any common country are grouped in the “No overlap” group, plant pairs with at least one common country were aggregated into three groups based on their geographical similarity: (i) “Low geographical similarity” for 0 < SSC < 0.4, (ii) “Medium geographical similarity” for 0.4 < SSC < 0.8, and (iii) “High geographical similarity” for 0.8 < SSC. Each raincloud plot illustrates the distribution of medicinal plants used to treat a disease from one of the 23 disease categories (see Table S2). Legend: ∗∗ = 1.00e⁻⁰³ < q-value < 1.00e⁻⁰²; ns = non-significant.

Figure 4

Chemical composition similarity between taxonomically and non-taxonomically related medicinal plants (A) With chemicals and (B) with Murcko scaffolds for chemicals. As outlined in section in-depth exploration of ethnobotanical patterns for drug discovery, chemical similarity is defined by the Szymkiewicz-Simpson Coefficient (SSC) between the set of chemicals or Murcko scaffolds contained in two plants. The group Random corresponds to 100,000 pairs of plants not belonging to the same family (i.e., non-taxonomically related plants). Legend: ∗∗∗∗ = q-value < 1.00e⁻⁰⁴.

Figure 5

Medicinal usage similarity between medicinal plants with low and high chemical similarity Each raincloud plot illustrates the distribution of Pearson correlation coefficients for the two groups of medicinal plants (i.e., congeneric plants with high and low chemical similarity) used to treat a disease from one of the 23 disease categories (see Table S2). The disease vectors are populated based on the count of plant-disease associations in the scientific literature dataset. Legend: ∗∗ 1.00e⁻⁰³ < q-value < 1.00e^−02.

Figure 6

Illustrative examples of exploring ethnobotanical patterns for drug discovery (A) Subset of plants traditionally used to treat asthma visualized in a subset of the taxonomic tree. Due to the large number of species associated with asthma (591), only species associated with the disease in more than 15 publications are highlighted in green in the outer ring of the tree with their respective binomial names displayed. (B) Geographical location of the plants presented in the first case scenario. Color tones used to represent the main locations of each plant pair: Taraxacum (red), Sambucus (yellow), Liquidambar (green), Coptis (violet), Boswellia (blue). We would like to note the circle marker does not represent the entire geographical location where the plant is currently present but an approximate location of its native region according to Royal Botanic Gardens, Kew (https://www.kew.org/). (C) Hierarchical clustering of the plants of the genus based on their reported indication areas. Species names highlighted in bold correspond to the plants known to contain atropine. (D) Evaluating chemical diversity between non-medicinal and medicinal plants. The plotted distribution corresponds to the number of unique Murcko scaffolds obtained from 10,000 randomly generated sets of phytochemicals drawn from non-medicinal plants. The orange vertical bar represents the number of unique scaffolds obtained from the same number of phytochemicals selected from the top 100 medicinal plants with the most reported therapeutic uses. It is worth noting that all of the numbers of unique scaffolds plotted in the distribution are derived from sets of phytochemicals of equal size.