Pharmacognosy in the digital era: shifting to contextualized metabolomics

Abstract

Humans have co-evolved alongside numerous other organisms, some having a profound effect on health and nutrition. As the earliest pharmaceutical subject, pharmacognosy has evolved into a meta-discipline devoted to natural biomedical agents and their functional properties. While the acquisition of expanding data volumes is ongoing, contextualization is lagging. Thus, we assert that the establishment of an integrated and open databases ecosystem will nurture the discipline. After proposing an epistemological framework of knowledge acquisition in pharmacognosy, this study focuses on recent computational and analytical approaches. It then elaborates on the flux of research data, where good practices could foster the implementation of more integrated systems, which will in turn help shaping the future of pharmacognosy and determine its constitutional societal relevance.

Figure 1

Proposed epistemological framework of knowledge acquisition methods in pharmacognosy. At the bottom are depicted the main subjects of study of the discipline in order of increasing systems complexity: single chemical entities, natural product extracts, genetic material, proteins, microorganisms, plants, animals (including humans), the whole planet ecosystem and their possible interactions. Above are schematized knowledge acquisition methods in the discipline. They were divided four ways according to their tendency to rely on: holistic and experimental approaches (Corner A, e.g. traditional medicines), reductionist and experimental approaches (Corner B, e.g. bio-guided fractionation), reductionist and computational approaches (Corner C, e.g. in silico fragmentation of a single chemical entity) and holistic and computational approaches (Corner D, a hypothetical ecosystem of open databases aggregating pharmacognostic knowledge). Grey links represent examples of information fluxes (solid lines as existing fluxes and dashed lines as potential ones). Actually, these knowledge acquisition methods are not mutually exclusive. For example: the use of an in silico annotated molecular network organizing fragmentation data acquired on plants selected trough an ethnopharmacological survey to highlight metabolites involved in the reported traditional use. This approach would feed from three knowledge acquisition methods (Corner A, B and C).

Figure 2

Representation of the different types of data handled in pharmacognosy (non exhaustive). The blue network that circles the graphic represents the production and consumption processes of data in order to predict and annotate existing data or entities. The transparent red items on top of Bioactivity data, Source organism and Single Chemical Entity represent some of the different issues that can complicate the interpretation or identification of these elements. See Box. 1 for details