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This is a comprehensive source of information on the application of ion chromatography (IC) in the analysis of pharmaceutical drugs and biologicals.This book, with contributors from academia, pharma, the biotech industry, and instrument manufacturing, presents the different perspectives, experience, and expertise of the thought leaders of IC.

Natural antioxidants are widely distributed in food and medicinal plants. These natural antioxidants, especially polyphenols and carotenoids, exhibit a wide range of biological effects, including anti-inflammatory, anti-aging, anti-atherosclerosis and anticancer. The effective extraction and proper assessment of antioxidants from food and medicinal plants are crucial to explore the potential antioxidant sources and promote the application in functional foods, pharmaceuticals and food additives. The present paper provides comprehensive information on the green extraction technologies of natural antioxidants, assessment of antioxidant activity at chemical and cellular based levels and their main resources from food and medicinal plants.

Bioactive natural products have evolved to inhibit specific cellular targets and have served as lead molecules for health and agricultural applications for the past century 1 – 3 . The post-genomics era has brought a renaissance in the discovery of natural products using synthetic-biology tools 4 – 6 . However, compared to traditional bioactivity-guided approaches, genome mining of natural products with specific and potent biological activities remains challenging 4 . Here we present the discovery and validation of a potent herbicide that targets a critical metabolic enzyme that is required for plant survival. Our approach is based on the co-clustering of a self-resistance gene in the natural-product biosynthesis gene cluster 7 – 9 , which provides insight into the potential biological activity of the encoded compound. We targeted dihydroxy-acid dehydratase in the branched-chain amino acid biosynthetic pathway in plants; the last step in this pathway is often targeted for herbicide development 10 . We show that the fungal sesquiterpenoid aspterric acid, which was discovered using the method described above, is a sub-micromolar inhibitor of dihydroxy-acid dehydratase that is effective as a herbicide in spray applications. The self-resistance gene astD was validated to be insensitive to aspterric acid and was deployed as a transgene in the establishment of plants that are resistant to aspterric acid. This herbicide-resistance gene combination complements the urgent ongoing efforts to overcome weed resistance 11 . Our discovery demonstrates the potential of using a resistance-gene-directed approach in the discovery of bioactive natural products. Fungal genome mining targeted to self-resistance genes close to biosynthetic gene clusters identifies a pathway that produces aspterric acid, which proves to be a potent inhibitor of plant growth.

Fish discards and by-products can be transformed into high value-added products such as fish protein hydrolysates (FPH) containing bioactive peptides. Protein hydrolysates were prepared from different parts (whole fish, skin and head) of several discarded species of the North-West Spain fishing fleet using Alcalase. All hydrolysates had moisture and ash contents lower than 10% and 15%, respectively. The fat content of FPH varied between 1.5% and 9.4% and had high protein content (69.8–76.6%). The amino acids profiles of FPH are quite similar and the most abundant amino acids were glutamic and aspartic acids. All FPH exhibited antioxidant activity and those obtained from Atlantic horse mackerel heads presented the highest 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity, reducing power and Cu2+ chelating activity. On the other hand, hydrolysates from gurnard heads showed the highest ABTS radical scavenging activity and Fe2+ chelating activity. In what concerns the α-amylase inhibitory activity, the IC50 values recorded for FPH ranged between 5.70 and 84.37 mg/mL for blue whiting heads and whole Atlantic horse mackerel, respectively. α-Glucosidase inhibitory activity of FPH was relatively low but all FPH had high Angiotensin Converting Enzyme (ACE) inhibitory activity. Considering the biological activities, these FPH are potential natural additives for functional foods or nutraceuticals.

Metabolomics using nontargeted tandem mass spectrometry can detect thousands of molecules in a biological sample. However, structural molecule annotation is limited to structures present in libraries or databases, restricting analysis and interpretation of experimental data. Here we describe CANOPUS (class assignment and ontology prediction using mass spectrometry), a computational tool for systematic compound class annotation. CANOPUS uses a deep neural network to predict 2,497 compound classes from fragmentation spectra, including all biologically relevant classes. CANOPUS explicitly targets compounds for which neither spectral nor structural reference data are available and predicts classes lacking tandem mass spectrometry training data. In evaluation using reference data, CANOPUS reached very high prediction performance (average accuracy of 99.7% in cross-validation) and outperformed four baseline methods. We demonstrate the broad utility of CANOPUS by investigating the effect of microbial colonization in the mouse digestive system, through analysis of the chemodiversity of different Euphorbia plants and regarding the discovery of a marine natural product, revealing biological insights at the compound class level. Unknown metabolites are classified from mass spectrometry data.

Natural products have traditionally been rich sources for drug discovery. In order to clear the road toward the discovery of unknown natural products, biologists need dereplication strategies that identify known ones. Here we report DEREPLICATOR+, an algorithm that improves on the previous approaches for identifying peptidic natural products, and extends them for identification of polyketides, terpenes, benzenoids, alkaloids, flavonoids, and other classes of natural products. We show that DEREPLICATOR+ can search all spectra in the recently launched Global Natural Products Social molecular network and identify an order of magnitude more natural products than previous dereplication efforts. We further demonstrate that DEREPLICATOR+ enables cross-validation of genome-mining and peptidogenomics/glycogenomics results. New natural products can be identified via mass spectrometry by excluding all known ones from the analysis, a process called dereplication. Here, the authors extend a previously published dereplication algorithm to different classes of secondary metabolites.

Aggregated mass spectral data by consortia such as the Global Natural Products Social (GNPS) molecular networking infrastructure enable natural product discovery. DEREPLICATOR, validated on peptidic natural products, is a computational tool to identify known metabolites in complex samples. Peptidic natural products (PNPs) are widely used compounds that include many antibiotics and a variety of other bioactive peptides. Although recent breakthroughs in PNP discovery raised the challenge of developing new algorithms for their analysis, identification of PNPs via database search of tandem mass spectra remains an open problem. To address this problem, natural product researchers use dereplication strategies that identify known PNPs and lead to the discovery of new ones, even in cases when the reference spectra are not present in existing spectral libraries. DEREPLICATOR is a new dereplication algorithm that enables high-throughput PNP identification and that is compatible with large-scale mass-spectrometry-based screening platforms for natural product discovery. After searching nearly one hundred million tandem mass spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure, DEREPLICATOR identified an order of magnitude more PNPs (and their new variants) than any previous dereplication efforts.

Study Objectives: Melatonin is an important neurohormone, which mediates circadian rhythms and the sleep cycle. As such, it is a popular and readily available supplement for the treatment and prevention of sleep-related disorders including insomnia and jet lag. This study quantified melatonin in 30 commercial supplements, comprising different brands and forms and screened supplements for the presence of serotonin. Methods: A total of 31 supplements were analyzed by ultraperformance liquid chromatography with electrochemical detection for quantification of melatonin and serotonin. Presence of serotonin was confirmed through analysis by ultraperformance liquid chromatography with mass spectrometry detection. Results: Melatonin content was found to range from −83% to +478% of the labelled content. Additionally, lot-to-lot variable within a particular product varied by as much as 465%. This variability did not appear to be correlated with manufacturer or product type. Furthermore, serotonin (5-hydroxytryptamine), a related indoleamine and controlled substance used in the treatment of several neurological disorders, was identified in eight of the supplements at levels of 1 to 75 μg. Conclusions: Melatonin content did not meet label within a 10% margin of the label claim in more than 71% of supplements and an additional 26% were found to contain serotonin. It is important that clinicians and patients have confidence in the quality of supplements used in the treatment of sleep disorders. To address this, manufacturers require increased controls to ensure melatonin supplements meet both their label claim, and also are free from contaminants, such as serotonin. Commentary: A commentary on this article appears in this issue on page 163. Citation: Erland LA, Saxena PK. Melatonin natural health products and supplements: presence of serotonin and significant variability of melatonin content. J Clin Sleep Med . 2017;13(2):275–281.

Conventional synthetic sorbents for oil spill removal are the most widely applied materials, although they are not the optimal choices from an economic and environmental point of view. The use of inexpensive, abundant, non-toxic, biodegradable, and reusable lignocellulosic materials might be an alternative to conventional sorbents, with obvious positive impact on sustainability and circular economy. The objective of this paper was to review reports on the use of natural-based adsorbing materials for the restoration of water bodies threatened by oil spills. The use of raw and modified natural sorbents as a restoration tool, their sorption capacity, along with the individual results in conditions that have been implemented, were examined in detail. Modification methods for improving the hydrophobicity of natural sorbents were also extensively highlighted. Furthermore, an attempt was made to assess the advantages and limitations of each natural sorbent since one material is unlikely to encompass all potential oil spill scenarios. Finally, an evaluation was conducted in order to outline an integrated approach based on the terms of material–environment–economy.

Metabolomics is a powerful tool in the analysis and identification of metabolites responsible for biological properties. Regarding natural product chemistry, it constitutes a potential strategy to streamline the classic and laborious process of isolating natural products, which often involves the re-isolation and identification of known compounds. In this contribution, we establish a mass spectrometry-based metabolomics strategy to discover compounds with larvicidal activity against Aedes aegypti . We analyse the Brazilian plant Annona crassiflora using different platforms to annotate the active compounds in different extracts/fractions of various plant parts. The MetaboAnalyst and GNPS platforms, which consider LC-MS and LC-MS/MS data, respectively, were chosen to identify compounds that differentiate active and inactive samples. Bio-guided isolation was subsequently performed to confirm compound activity. Results proved the capacity of metabolomics to predict metabolite differences between active and inactive samples using LC-MS and LC-MS/MS data. Moreover, we discuss the limitations, possibilities, and strategies to have a broad view of vast data.