BACKGROUND: Natural deep eutectic solvents (NADES) have emerged as interesting extractants to develop botanical ingredients. They are nontoxic and biodegradable, nonflammable, easy to prepare, and able to solubilize a wide range of molecules. However, NADES extracts remain difficult to analyze because the metabolites of interest stay highly diluted in the nonvolatile viscous NADES matrix.
OBJECTIVE: This study presents a robust analytical workflow for the chemical profiling of NADES extracts. It is applied to Hypericum perforatum aerial parts extracted with the neutral mixture fructose/glycerol/water (3/1/1, w/w/w), and compared to the chemical profiling of a classical dry methanol extract.
METHODS: Exploiting polarity differences between metabolites, the H. perforatum NADES extract was partitioned in a liquid-liquid solvent system to trap the hydrophilic NADES constituents in the lower phase. The upper phase, containing a diversity of secondary metabolites from H. perforatum, was fractionated by centrifugal partition chromatography. All fractions were chemically investigated using a 13 C NMR dereplication method which involves hierarchical clustering analysis of the whole NMR dataset, a natural metabolite database for metabolite identification, and 2D NMR analyses for validation. Liquid chromatography-mass spectrometry (LC-MS) analyses were also performed to complete the identification process.
RESULTS: A range of 21 metabolites were unambiguously identified, including glycosylated flavonols, lactones, catechins, phenolic acids, lipids, and simple sugars, and 15 additional minor extract constituents were annotated by LC-MS based on exact mass measurements.
CONCLUSIONS: The proposed identification process is rapid and nondestructive and provides good prospects to deeply characterize botanical extracts obtained in nonvolatile and viscous NADES systems.

Quercetin (QR) is a natural flavonol compound widely distributed in the plant kingdom with extensive pharmacological effects. To find the potential clinical indications of QR, 156 differentially expressed genes (DEGs) regulated by QR were obtained from the Gene Expression Omnibus database, and new potential pharmacological effects and clinical indications of QR were repurposed by integrating compounds with similar gene perturbation signatures and associated-disease signatures to QR based on the Connectivity Map and Coexpedia platforms. The results suggested QR has mainly potential therapeutic effects on multiple sclerosis (MS), osteoarthritis, type 2 diabetes mellitus, and acute leukemia. Then, MS was selected for subsequent animal experiments as a representative potential indication, and it found that QR significantly delays the onset time of classical MS model animal mice and ameliorates the inflammatory infiltration and demyelination in the central nervous system. Combined with network pharmacology technology, the therapeutic mechanism of QR on MS was further demonstrated to be related to the inhibition of the expression of inflammatory cytokines (TNF-α, IL-6, IL-1β, IFN-γ, IL-17A, and IL-2) related to TNF-α/TNFR1 signaling pathway. In conclusion, this study expanded the clinical indications of QR and preliminarily confirmed the therapeutic effect and potential mechanism of QR on MS.

Natural products remain one of the major sources of coveted, biologically active compounds. Each isolated compound undergoes biological testing, and its structure is usually established using a set of spectroscopic techniques (NMR, MS, UV-IR, ECD, VCD, etc.). However, the number of erroneously determined structures remains noticeable. Structure revisions are very costly, as they usually require extensive use of spectroscopic data, computational chemistry, and total synthesis. The cost is particularly high when a biologically active compound is resynthesized and the product is inactive because its structure is wrong and remains unknown. In this paper, we propose using Computer-Assisted Structure Elucidation (CASE) and Density Functional Theory (DFT) methods as tools for preventive verification of the originally proposed structure, and elucidation of the correct structure if the original structure is deemed to be incorrect. We examined twelve real cases in which structure revisions of natural products were performed using total synthesis, and we showed that in each of these cases, time-consuming total synthesis could have been avoided if CASE and DFT had been applied. In all described cases, the correct structures were established within minutes of using the originally published NMR and MS data, which were sometimes incomplete or had typos.

Pregnane X receptor (PXR), extensively expressed in human tissues related to digestion and metabolism, is responsible for recognizing and detoxifying diverse xenobiotics encountered by humans. To comprehend the promiscuous nature of PXR and its ability to bind a variety of ligands, computational approaches, viz., quantitative structure-activity relationship (QSAR) models, aid in the rapid dereplication of potential toxicological agents and mitigate the number of animals used to establish a meaningful regulatory decision. Recent advancements in machine learning techniques accommodating larger datasets are expected to aid in developing effective predictive models for complex mixtures (viz., dietary supplements) before undertaking in-depth experiments. Five hundred structurally diverse PXR ligands were used to develop traditional two-dimensional (2D) QSAR, machine-learning-based 2D-QSAR, field-based three-dimensional (3D) QSAR, and machine-learning-based 3D-QSAR models to establish the utility of predictive machine learning methods. Additionally, the applicability domain of the agonists was established to ensure the generation of robust QSAR models. A prediction set of dietary PXR agonists was used to externally-validate generated QSAR models. QSAR data analysis revealed that machine-learning 3D-QSAR techniques were more accurate in predicting the activity of external terpenes with an external validation squared correlation coefficient (R2) of 0.70 versus an R2 of 0.52 in machine-learning 2D-QSAR. Additionally, a visual summary of the binding pocket of PXR was assembled from the field 3D-QSAR models. By developing multiple QSAR models in this study, a robust groundwork for assessing PXR agonism from various chemical backbones has been established in anticipation of the identification of potential causative agents in complex mixtures.

Computer-assisted synthesis planning represents a growing area of research, especially for complex molecule synthesis. Here, we present a case study involving the pupukeanane natural products, which are complex, marine-derived, natural products with unique tricyclic scaffolds. Proposed routes to members of each skeletal class informed by pathways generated using the program Synthia™ are compared to previous syntheses of these molecules. In addition, novel synthesis routes are proposed to pupukeanane congeners that have not been prepared previously.

A new AAPH-Incubating HPLC-DAD-HR MS/MS method was developed for the rapid and high-throughput screening of antioxidants directly in natural products and applied to Gardenia jasminoides fruit. This method was assumed that the peak areas of compounds with potential antioxidant activity in HPLC chromatograms would be significantly reduced or disappeared after incubating with the AAPH which can release ROO at physiological conditions (37 °C, pH 7.4). Additionally, the activity of antioxidants can be evaluated by comparing the peak reduction rates and the screened components can be further identified by HRMS/MS. Then, 17 potential natural antioxidants from the crude extract of GJF was screened. Among them, three major components including crocin I, crocin II and crocetin showed excellent ROO scavenging activity, which were further validated by the ORAC assay. In conclusion, our study provided a simple and effective strategy to rapidly screen antioxidants in natural products.

Many human diseases including cancer, degenerative and autoimmune disorders, diabetes and others are multifactorial. Pharmaceutical agents acting on a single target do not provide their efficient curation. Multitargeted drugs exhibiting pleiotropic pharmacological effects have certain advantages due to the normalization of the complex pathological processes of different etiology. Extracts of medicinal plants (EMP) containing multiple phytocomponents are widely used in traditional medicines for multifactorial disorders\' treatment. Experimental studies of pharmacological potential for multicomponent compositions are quite expensive and time-consuming. In silico evaluation of EMP the pharmacological potential may provide the basis for selecting the most promising directions of testing and for identifying potential additive/synergistic effects. Multiphytoadaptogen (MPhA) containing 70 major phytocomponents of different chemical classes from 40 medicinal plant extracts has been studied in vitro, in vivo and in clinical researches. Antiproliferative and anti-tumor activities have been shown against some tumors as well as evidence-based therapeutic effects against age-related pathologies. In addition, the neuroprotective, antioxidant, antimutagenic, radioprotective, and immunomodulatory effects of MPhA were confirmed. Analysis of the PASS profiles of the biological activity of MPhA phytocomponents showed that most of the predicted anti-tumor and anti-metastatic effects were consistent with the results of laboratory and clinical studies. Antimutagenic, immunomodulatory, radioprotective, neuroprotective and anti-Parkinsonian effects were also predicted for most of the phytocomponents. Effects associated with positive effects on the male and female reproductive systems have been identified too. Thus, PASS and PharmaExpert can be used to evaluate the pharmacological potential of complex pharmaceutical compositions containing natural products.

Fungal secondary metabolites (SMs) have captured the interest of natural products researchers in academia and industry for decades. In recent years, the high rediscovery rate of previously characterized metabolites is making it increasingly difficult to uncover novel compounds. Additionally, the vast majority of fungal SMs reside in genetically intractable fungi or are silent under normal laboratory conditions in genetically tractable fungi. The fungal natural products community has broadly overcome these barriers by altering the physical growth conditions of the fungus and heterologous/homologous expression of biosynthetic gene cluster regulators or proteins. The protocols described here summarize vital methodologies needed when researching SM production in fungi. We also summarize the growth conditions, genetic backgrounds, and extraction protocols for every published SM in Aspergillus fumigatus, enabling readers to easily replicate the production of previously characterized SMs. Readers will also be equipped with the tools for developing their own strategy for expressing and extracting SMs from their given fungus or a suitable heterologous model system. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Making glycerol stocks from spore suspensions Alternate Protocol 1: Creating glycerol stocks from non-sporulating filamentous fungi Basic Protocol 2: Activating spore-suspension glycerol stocks Basic Protocol 3: Extracting secondary metabolites from Aspergillus spp grown on solid medium Alternate Protocol 2: Extracting secondary metabolites from Aspergillus spp using ethyl acetate Alternate Protocol 3: High-volume metabolite extraction using ethyl acetate Alternate Protocol 4: Extracting secondary metabolites from Aspergillus spp in liquid medium Support Protocol: Creating an overlay culture Basic Protocol 4: Extracting DNA from filamentous fungi Basic Protocol 5: Creating a DNA construct with double-joint PCR Alternate Protocol 5: Creating a DNA construct with yeast recombineering Basic Protocol 6: Transformation of Aspergillus spp Basic Protocol 7: Co-culturing fungi and bacteria for extraction of secondary metabolites.

Structure elucidation with NMR correlation data is dicey, as there is no way to tell how ambiguous the data set is and how reliably it will define a constitution. Many different software tools for computer assisted structure elucidation (CASE) have become available over the past decades, all of which could ensure a better quality of the elucidation process, but their use is still not common. Since 2011, WebCocon has integrated the possibility to generate theoretical NMR correlation data, starting from an existing structural proposal, allowing this theoretical data then to be used for CASE. Now, WebCocon can also read the recently presented NMReDATA format, allowing for uncomplicated access to CASE with experimental data. With these capabilities, WebCocon presents itself as an easily accessible Web-Tool for the quality control of proposed new natural products. Results of this application to several molecules from literature are shown and demonstrate how CASE can contribute to improve the reliability of Structure elucidation with NMR correlation data.

3CL-Pro is the SARS-CoV-2 main protease (MPro). It acts as a homodimer to cleave the large polyprotein 1ab transcript into proteins that are necessary for viral growth and replication. 3CL-Pro has been one of the most studied SARS-CoV-2 proteins and a main target of therapeutics. A number of drug candidates have been reported, including natural products. Here, we employ elaborate computational methods to explore the dimerization of the 3CL-Pro protein, and we formulate a computational context to identify potential inhibitors of this process. We report that fortunellin (acacetin 7-O-neohesperidoside), a natural flavonoid O-glycoside, and its structural analogs are potent inhibitors of 3CL-Pro dimerization, inhibiting viral plaque formation in vitro. We thus propose a novel basis for the search of pharmaceuticals as well as dietary supplements in the fight against SARS-CoV-2 and COVID-19.