Massive metabolite profiling of natural extracts for a rational prioritization of bioactive natural products: A paradigm shift in pharmacognosy (2020)

Whether in food, dietary supplements, and phytomedicine, micronutrients play an important role in health.1 To understand the effects of these often very complex mixtures of secondary metabolites, it is necessary to develop metabolite profiling methods that provide a very detailed view of the chemical composition of the natural extracts that are ingested. This is a very complex task because the micronutrients are often metabolized and may have synergistic effects or present as prodrugs. This type of investigation is important, not only for health aspects, but also for toxicity and quality control and it is increasingly important to have a holistic view of all the constituents to understand the effects of a natural extract. With the development of modern metabolite profiling methods and metabolomics,2 it becomes possible to obtain valuable information on the chemical composition of ingredients quickly and in detail. In complement to such efficient approaches, MS-targeted isolation of natural products can be performed with high chromatographic resolution in a single step for a full de novo identification and pharmacological evaluation.3 This changes the paradigms in natural product research where typically bioactive compounds are identified at the end of lengthy bioactivity-guided fractionation studies. Thus it now becomes possible to have full or partial structure of all the possible metabolites in a given natural extract at different quantitative levels and this approach opens the possibility to perform pharmacognosy studies from a more holistic perspective. Today the increasing amount of accurate metabolome data that can be acquired from massive sample sets, notably through high-resolution mass spectrometry data-dependent MS/MS analyses (HRMS/MS), allows mapping of natural extracts at an unprecedented precision level.2 While the acquisition of larger volumes of data is ongoing, contextualizing these data is a lagging process. To overcome this challenge, the creation of open, integrated databases could be extremely valuable in fuelling pharmacognosy in the years to come, and ultimately lead to positive outcomes for society.1 Fast progresses in this field are foreseen bringing together recent computational/analytical approaches linking invaluable published knowledge brought by pharmacognosy mainly based on bioactivity-guided isolation studies. In this context, our laboratory has pushed forward the development of UHPLC–HRMS/MS molecular network (MN) approaches for in depth natural extract metabolite profiling at large scale.4,5 Since metabolite identification remains challenging, enhanced annotation confidence level is obtained through multiple scores. This is performed by comparison of the MS/MS spectra of each metabolites with in silico MS fragmentation of most published natural product structures,6 taxonomy information,7 and MN cluster structural consistency assessment. Benchmarking of such approaches is currently performed by profiling mixtures of herbs with well-studied composition. Different recent applications of our metabolomics and phytochemical investigations have illustrated these aspects, especially in the context of bioactive natural product prioritization.2,4,5 We believe that the massive accumulation of compositional data on a large number of plant extracts makes it possible to align and compare their metabolome by taking into account the structural similarity of their constituents. With the development of biochemometrics and artificial intelligence tools,8 correlations of such organized data with bioactivity assays at the extract level, clinical outcome data, or epidemiological observations on the use of certain nutrients or medicinal plants are becoming feasible.1 This opens new avenues for the identification of bioactive compounds and can provide evidence-based efficacy for the use of natural extracts with standardized compositions.