A rapid and facile method for the dereplication of purified natural products

13C NMR dereplication-assisted isolation of bioactive polyphenolic metabolites from Clusia flava Jacq

Presently it is estimated that many of the approximately 4000 new natural products isolated every year following complicated, long, and expensive isolation processes are already known; because of this, developing new strategies for locating secondary metabolites of interest in complex extracts or fractions is important. Currently, chromatographic and spectroscopic techniques are being used to optimize the isolation and identification of natural products. In this investigation we have used 13C NMR dereplication analyses for the quick identification of a number of triterpenes (friedelin, lupeol, betulinic acid), sterols (euphol, β-sitosterol) and fatty acids (palmitic acid) present in semipurified fractions obtained from the stem bark extract of Clusia flava and to assist in the isolation of the bioactive metabolites trapezifolixanthone and paralycolin A. The complete and correct assignment of the 1H and 13C NMR spectroscopic data for paralycolin A is reported for the first time and the antioxidant and antiAGEs activity of both metabolites is described.

Establishment of a screening platform based on human coronavirus OC43 for the identification of microbial natural products with antiviral activity

IMPORTANCE

The COVID-19 pandemic has revealed the lack of effective treatments against betacoronaviruses and the urgent need for new broad-spectrum antivirals. Natural products are a valuable source of bioactive compounds with pharmaceutical potential that may lead to the discovery of new antiviral agents. Specifically, compared to conventional synthetic molecules, microbial natural extracts possess a unique and vast chemical diversity and are amenable to large-scale production. The implementation of a high-throughput screening platform using the betacoronavirus OC43 in a human cell line infection model has provided proof of concept of the approach and has allowed for the rapid and efficient evaluation of 1,280 microbial extracts. The identification of several active compounds validates the potential of the platform for the search for new compounds with antiviral capacity.

Vasorelaxant Activity of Salvia hispanica L.: Involvement of the Nitric Oxide Pathway in Its Pharmacological Mechanism

Salvia hispanica L., commonly known as chía, and its seeds have been used since ancient times to prepare different beverages. Due to its nutritional content, it is considered a dietary ingredient and has been reported with many health benefits. Chia seed components are helpful in cardiovascular disease (CVD) by reducing blood pressure, platelet aggregation, cholesterol, and oxidation. Still, its vasodilator effects on the vascular system were not reported yet. The hexanic (HESh), dichloromethanic (DESh), and methanolic (MESh) extracts obtained from chía seeds were evaluated on an aortic ring ex-vivo experimental model. The vasorelaxant efficacy and mechanism of action were determined. Also, phytochemical data was obtained through 13C NMR-based dereplication. The MESh extract showed the highest efficacy (Emax = 87%), and its effect was partially endothelium-dependent. The mechanism of action was determined experimentally, and the vasorelaxant curves were modified in the presence of L-NAME, ODQ, and potassium channel blockers. MESh caused a relaxing effect on KCl 80 mM-induced contraction and was less potent than nifedipine. The CaCl2-induced contraction was significantly decreased compared with the control curve. Phytochemical analysis of MESh suggests the presence of mannitol, previously reported as a vasodilator on aortic rings. Our findings suggest NO-cGMP pathway participation as a vasodilator mechanism of action of S. hispanica seeds; this effect can be attributed, in part, to the mannitol presence. S. hispanica could be used in future research focused on antihypertensive therapies.

Diversity and taxonomic distribution of bacterial biosynthetic gene clusters predicted to produce compounds with therapeutically relevant bioactivities

Bacteria have long been a source of natural products with diverse bioactivities that have been developed into therapeutics to treat human disease. Historically, researchers have focused on a few taxa of bacteria, mainly Streptomyces and other actinomycetes. This strategy was initially highly successful and resulted in the golden era of antibiotic discovery. The golden era ended when the most common antibiotics from Streptomyces had been discovered. Rediscovery of known compounds has plagued natural product discovery ever since. Recently, there has been increasing interest in identifying other taxa that produce bioactive natural products. Several bioinformatics studies have identified promising taxa with high biosynthetic capacity. However, these studies do not address the question of whether any of the products produced by these taxa are likely to have activities that will make them useful as human therapeutics. We address this gap by applying a recently developed machine learning tool that predicts natural product activity from biosynthetic gene cluster (BGC) sequences to determine which taxa are likely to produce compounds that are not only novel but also bioactive. This machine learning tool is trained on a dataset of BGC-natural product activity pairs and relies on counts of different protein domains and resistance genes in the BGC to make its predictions. We find that rare and understudied actinomycetes are the most promising sources for novel active compounds. There are also several taxa outside of actinomycetes that are likely to produce novel active compounds. We also find that most strains of Streptomyces likely produce both characterized and uncharacterized bioactive natural products. The results of this study provide guidelines to increase the efficiency of future bioprospecting efforts.

ONE-SENTENCE SUMMARY

This paper combines several bioinformatics workflows to identify which genera of bacteria are most likely to produce novel natural products with useful bioactivities such as antibacterial, antitumor, or antifungal activity.

A Machine Learning Bioinformatics Method to Predict Biological Activity from Biosynthetic Gene Clusters

Research in natural products, the genetically encoded small molecules produced by organisms in an idiosyncratic fashion, deals with molecular structure, biosynthesis, and biological activity. Bioinformatics analyses of microbial genomes can successfully reveal the genetic instructions, biosynthetic gene clusters, that produce many natural products. Genes to molecule predictions made on biosynthetic gene clusters have revealed many important new structures. There is no comparable method for genes to biological activity predictions. To address this missing pathway, we developed a machine learning bioinformatics method for predicting a natural product's antibiotic activity directly from the sequence of its biosynthetic gene cluster. We trained commonly used machine learning classifiers to predict antibacterial or antifungal activity based on features of known natural product biosynthetic gene clusters. We have identified classifiers that can attain accuracies as high as 80% and that have enabled the identification of biosynthetic enzymes and their corresponding molecular features that are associated with antibiotic activity.

Hydrazine-Containing Heterocycle Cytochalasan Derivatives From Hydrazinolysis of Extracts of a Desert Soil-Derived Fungus Chaetomium madrasense 375

"Diversity-enhanced extracts" is an effective method of producing chemical libraries for the purpose of drug discovery. Three rare new cytochalasan derivative chaetoglobosins B1-B3 (1-3) were obtained from chemically engineered crude broth extracts of Chaetomium madrasense 375 prepared by reacting with hydrazine monohydrate and four known metabolite chaetoglobosins (4-7) were also identified from the fungus. The structures were identified by NMR and MS analysis and electronic circular dichroism simulation. In addition, the antiproliferative activities of these compounds were also evaluated, and the drug-resistant activities of cytochalasans were evaluated for the first time. Compound 6 possessed potent activity against four human cancer cells (A549, HCC827, SW620, and MDA-MB-231), and two drug-resistant HCC827 cells (Gefitinib-resistant, Osimertinib-resistant) compared with the positive controls.

Novel applications of mass spectrometry-based metabolomics in herbal medicines and its active ingredients: Current evidence

Current evidence shows that herbal medicines could be beneficial for the treatment of various diseases. However, the complexities present in chemical compositions of herbal medicines are currently an obstacle for the progression of herbal medicines, which involve unclear bioactive compounds, mechanisms of action, undetermined targets for therapy, non-specific features for drug metabolism, etc. To overcome those issues, metabolomics can be a great to improve and understand herbal medicines from the small-molecule metabolism level. Metabolomics could solve scientific difficulties with herbal medicines from a metabolic perspective, and promote drug discovery and development. In recent years, mass spectrometry-based metabolomics was widely applied for the analysis of herbal constituents in vivo and in vitro. In this review, we highlight the value of mass spectrometry-based metabolomics and metabolism to address the complexity of herbal medicines in systems pharmacology, and to enhance their biomedical value in biomedicine, to shed light on the aid that mass spectrometry-based metabolomics can offer to the investigation of its active ingredients, especially, to link phytochemical analysis with the assessment of pharmacological effect and therapeutic potential. © 2019 Wiley Periodicals, Inc. Mass Spec Rev.

Various Dereplication Strategies Using LC-MS for Rapid Natural Product Lead Identification and Drug Discovery

Natural products are the most consistently successful source of drug leads. The rapid identification of known compounds from natural product extracts, or ‘dereplication’, is an important step in an efficiently run drug discovery program. Modern spectroscopic methods have largely revolutionized compound identification and tremendously accelerated the pace at which isolated compounds can be identified. Dereplication strategies use analytical techniques and database searching to determine the identity of an active compound at the earliest possible stage in the discovery process. This prevents wasted effort on samples with no potential for development and allows resources to be focused on the most promising lead. In the past few years, advances in technology have allowed the development of tandem analytical techniques, such as HPLC-PDA, LC-MS, LC-MS-MS, LC-NMR, and LC-NMR-MS. This review describes the principles and performance of a number of hyphenated techniques involving LC-MS that can be used for dereplication of natural products for rapid lead identification.

Database for Rapid Dereplication of Known Natural Products Using Data from MS and Fast NMR Experiments

The discovery of novel and/or new bioactive natural products from biota sources is often confounded by the reisolation of known natural products. Dereplication strategies that involve the analysis of NMR and MS spectroscopic data to infer structural features present in purified natural products in combination with database searches of these substructures provide an efficient method to rapidly identify known natural products. Unfortunately this strategy has been hampered by the lack of publically available and comprehensive natural product databases and open source cheminformatics tools. A new platform, DEREP-NP, has been developed to help solve this problem. DEREP-NP uses the open source cheminformatics program DataWarrior to generate a database containing counts of 65 structural fragments present in 229 358 natural product structures derived from plants, animals, and microorganisms, published before 2013 and freely available in the nonproprietary Universal Natural Products Database (UNPD). By counting the number of times one or more of these structural features occurs in an unknown compound, as deduced from the analysis of its NMR (¹H, HSQC, and/or HMBC) and/or MS data, matching structures carrying the same numeric combination of searched structural features can be retrieved from the database. Confirmation that the matching structure is the same compound can then be verified through literature comparison of spectroscopic data. This methodology can be applied to both purified natural products and fractions containing a small number of individual compounds that are often generated as screening libraries. The utility of DEREP-NP has been verified through the analysis of spectra derived from compounds (and fractions containing two or three compounds) isolated from plant, marine invertebrate, and fungal sources. DEREP-NP is freely available at https://github.com/clzani/DEREP-NP and will help to streamline the natural product discovery process.

Targeted Dereplication of Microbial Natural Products by High-Resolution MS and Predicted LC Retention Time

A new strategy for the identification of known compounds in Streptomyces extracts that can be applied in the discovery of natural products is presented. The strategy incorporates screening a database of 5555 natural products including 5098 structures from Streptomyces sp., using a high-throughput LCMS data processing algorithm that utilizes HRMS data and predicted LC retention times (t_R) as filters for rapid identification of known compounds in the natural product extract. The database, named StrepDB, contains for each compound the structure, molecular formula, molecular mass, and predicted LC retention time. All identified compounds are annotated and color coded for easier visualization. It is an indirect approach to quickly assess masses (which are not annotated) that may potentially lead to the discovery of new or novel structures. In addition, a spectral database named MbcDB was generated using the ACD/Spectrus DB Platform. MbcDB contains 665 natural products, each with structure, experimental HRESIMS, MS/MS, UV, and NMR spectra. StrepDB was used to screen a mutant Streptomyces albus extract, which led to the identification and isolation of two new compounds, legonmaleimides A and B, the structures of which were elucidated with the aid of MbcDB and spectroscopic techniques. The structures were confirmed by computer-assisted structure elucidation (CASE) methods using ACD/Structure Elucidator Suite. The developed methodology suggests a pipeline approach to the dereplication of extracts and discovery of novel natural products.

Modern mass spectrometry for synthetic biology and structure-based discovery of natural products

Covering: up to 2016In this highlight, we describe the current landscape for dereplication and discovery of natural products based on the measurement of the intact mass by LC-MS. Often it is assumed that because better mass accuracy (provided by higher resolution mass spectrometers) is necessary for absolute chemical formula determination (≤1 part-per-million), that it is also necessary for dereplication of natural products. However, the average ability to dereplicate tapers off at ∼10 ppm, with modest improvement gained from better mass accuracy when querying focused databases of natural products. We also highlight some recent examples of how these platforms are applied to synthetic biology, and recent methods for dereplication and correlation of substructures using tandem MS data. We also offer this highlight to serve as a brief primer for those entering the field of mass spectrometry-based natural products discovery.

Dereplication: racing to speed up the natural products discovery process

Covering: 1993-2014 (July)To alleviate the dereplication holdup, which is a major bottleneck in natural products discovery, scientists have been conducting their research efforts to add tools to their "bag of tricks" aiming to achieve faster, more accurate and efficient ways to accelerate the pace of the drug discovery process. Consequently dereplication has become a hot topic presenting a huge publication boom since 2012, blending multidisciplinary fields in new ways that provide important conceptual and/or methodological advances, opening up pioneering research prospects in this field.

Current perspectives in drug discovery against tuberculosis from natural products

Currently, one third of the world's population is latently infected with Mycobacterium tuberculosis (MTB), while 8.9-9.9 million new and relapse cases of tuberculosis (TB) are reported yearly. The renewed research interests in natural products in the hope of discovering new and novel antitubercular leads have been driven partly by the increased incidence of multidrug-resistant strains of MTB and the adverse effects associated with the first- and second-line antitubercular drugs. Natural products have been, and will continue to be a rich source of new drugs against many diseases. The depth and breadth of therapeutic agents that have their origins in the secondary metabolites produced by living organisms cannot be compared with any other source of therapeutic agents. Discovery of new chemical molecules against active and latent TB from natural products requires an interdisciplinary approach, which is a major challenge facing scientists in this field. In order to overcome this challenge, cutting edge techniques in mycobacteriology and innovative natural product chemistry tools need to be developed and used in tandem. The present review provides a cross-linkage to the most recent literature in both fields and their potential to impact the early phase of drug discovery against TB if seamlessly combined.

Dereplication of natural products using minimal NMR data inputs

A strategy for the dereplication of a complete and a partial structure using ¹H NMR, ¹H–¹³C HSQC and ¹H–¹H COSY spectral data, a molecular formula composition range and structural fragments against a massive database of about 22 million compounds is considered.

Biosynthetic potential-based strain prioritization for natural product discovery: a showcase for diterpenoid-producing actinomycetes

Natural products remain the best sources of drugs and drug leads and serve as outstanding small-molecule probes to dissect fundamental biological processes. A great challenge for the natural product community is to discover novel natural products efficiently and cost effectively. Here we report the development of a practical method to survey biosynthetic potential in microorganisms, thereby identifying the most promising strains and prioritizing them for natural product discovery. Central to our approach is the innovative preparation, by a two-tiered PCR method, of a pool of pathway-specific probes, thereby allowing the survey of all variants of the biosynthetic machineries for the targeted class of natural products. The utility of the method was demonstrated by surveying 100 strains, randomly selected from our actinomycete collection, for their biosynthetic potential of four classes of natural products, aromatic polyketides, reduced polyketides, nonribosomal peptides, and diterpenoids, identifying 16 talented strains. One of the talented strains, Streptomyces griseus CB00830, was finally chosen to showcase the discovery of the targeted classes of natural products, resulting in the isolation of three diterpenoids, six nonribosomal peptides and related metabolites, and three polyketides. Variations of this method should be applicable to the discovery of other classes of natural products.

New iridoid glycoside and triterpenoid glycoside from Premna fulva

Liquid chromatography-photodiode array detector-mass spectrometry-based chemical investigation of the leaves and stems of Premna fulva yielded one new iridoid glycoside (1), one new triterpenoid glycoside (2) along with six known compounds isolated for the first time from the genus. Their structures were established on the basis of extensive spectroscopic data analyses and chemical methods.

Targeting the human genome-microbiome axis for drug discovery: inspirations from global systems biology and traditional Chinese medicine

Most chronic diseases impairing current human public health involve not only the human genome but also gene-environment interactions, and in the latter case the gut microbiome is an important factor. This makes the classical single drug-receptor target drug discovery paradigm much less applicable. There is widespread and increasing international interest in understanding the properties of traditional Chinese medicines (TCMs) for their potential utilization as a source of new drugs for Western markets as emerging evidence indicates that most TCM drugs are actually targeting both the host and its symbiotic microbes. In this review, we explore the challenges of and opportunities for harmonizing Eastern-Western drug discovery paradigms by focusing on emergent functions at the whole body level of humans as superorganisms. This could lead to new drug candidate compounds for chronic diseases targeting receptors outside the currently accepted "druggable genome" and shed light on current high interest issues in Western medicine such as drug-drug and drug-diet-gut microbial interactions that will be crucial in the development and delivery of future therapeutic regimes optimized for the individual patient.

Dereplication, residual complexity, and rational naming: the case of the Actaea triterpenes

The genus Actaea (including Cimicifuga) has been the source of ∼200 cycloartane triterpenes. While they are major bioactive constituents of complementary and alternative medicines, their structural similarity is a major dereplication problem. Moreover, their trivial names seldom indicate the actual structure. This project develops two new tools for Actaea triterpenes that enable rapid dereplication of more than 170 known triterpenes and facilitates elucidation of new compounds. A predictive computational model based on classification binary trees (CBTs) allows in silico determination of the aglycone type. This tool utilizes the Me (1)H NMR chemical shifts and has potential to be applicable to other natural products. Actaea triterpene dereplication is supported by a new systematic naming scheme. A combination of CBTs, (1)H NMR deconvolution, characteristic (1)H NMR signals, and quantitative (1)H NMR (qHNMR) led to the unambiguous identification of minor constituents in residually complex triterpene samples. Utilizing a 1.7 mm cryo-microprobe at 700 MHz, qHNMR enabled characterization of residual complexity at the 10-20 μg level in a 1-5 mg sample. The identification of five co-occurring minor constituents, belonging to four different triterpene skeleton types, in a repeatedly purified natural product emphasizes the critical need for the evaluation of residual complexity of reference materials, especially when used for biological assessment.

CYANOS: a data management system for natural product drug discovery efforts using cultured microorganisms

A software package, termed "CYANOS", has been developed to facilitate the data management and mining for natural product drug discovery efforts. This system allows for the management of data associated with field collections, culture conditions, harvests, extractions, chemical separations, and biological evaluation. This software utilizes a MySQL database for data storage, which allows for reporting and data mining via third party tools. In addition, a Web-based interface was constructed to allow for multiuser access from a variety of desktop platforms. The code for this system is freely available and has been released under the Illinois Open Source license.

Bioprospecting microbial natural product libraries from the marine environment for drug discovery

Marine microorganisms are fascinating resources due to their production of novel natural products with antimicrobial activities. Increases in both the number of new chemical entities found and the substantiation of indigenous marine actinobacteria present a fundamental difficulty in the future discovery of novel antimicrobials, namely dereplication of those compounds already discovered. This review will share our experience on the taxonomic-based construction of a highly diversified and low redundant marine microbial natural product library for high-throughput antibiotic screening. We anticipate that libraries such as these can drive the drug discovery process now and in the future.

A multipronged approach to the study of peruvian ethnomedicinal plants: a legacy of the ICBG-Peru Project

A multidisciplinary and international team of scientists was assembled in the early 1990s to conduct an ethnobotanical study of plants used by the Aguaruna people of the Peruvian Amazon forest. The initial ethnobotanical project, carried out under the auspices of an International Cooperative Biodiversity Grant (ICBG), led to the collection of approximately 4000 plant species. Some members of the original team of scientists have continued this collaboration by focusing on potential sources of new anticancer, anti-infective, and wound-healing agents. This effort has uncovered several secondary metabolites representing a wide variety of chemical diversity. In this short review we describe some bioactive compounds of interest as part of our continuing collaboration.

Automated molecular formula determination by tandem mass spectrometry (MS/MS)

Automated software was developed to analyze the molecular formula of organic molecules and peptides based on high-resolution MS/MS spectroscopic data. The software was validated with 96 compounds including a few small peptides in the mass range of 138-1569 Da containing the elements carbon, hydrogen, nitrogen and oxygen. A Micromass Waters Q-TOF Ultima Global mass spectrometer was used to measure the molecular masses of precursor and fragment ions. Our software assigned correct molecular formulas for 91 compounds, incorrect molecular formulas for 3 compounds, and no molecular formula for 2 compounds. The obtained 95% success rate indicates high reliability of the software. The mass accuracy of the precursor ion and the fragment ions, which is critical for the success of the analysis, was high, i.e. the accuracy and the precision of 850 data were 0.0012 Da and 0.0016 Da, respectively. For the precursor and fragment ions below 500 Da, 60% and 90% of the data showed accuracy within < or = 0.001 Da and < or = 0.002 Da, respectively. The precursor and fragment ions above 500 Da showed slightly lower accuracy, i.e. 40% and 70% of them showed accuracy within < or = 0.001 Da and < or = 0.002 Da, respectively. The molecular formulas of the precursor and the fragments were further used to analyze possible mass spectrometric fragmentation pathways, which would be a powerful tool in structural analysis and identification of small molecules. The method is valuable in the rapid screening and identification of small molecules such as the dereplication of natural products, characterization of drug metabolites, and identification of small peptide fragments in proteomics. The analysis was also extended to compounds that contain a chlorine or bromine atom.

Evolving trends in the dereplication of natural product extracts: new methodology for rapid, small-scale investigation of natural product extracts

The use of an HPLC bioactivity profiling/microtiter plate technique in conjunction with capillary probe NMR instrumentation and access to appropriate databases effectively short-circuits conventional dereplication procedures, necessarily based on multimilligram extracts, to a single, more rapid submilligram operation. This approach to dereplication is illustrated using fungal or bacterial extracts that contain known compounds. In each case the dereplication steps were carried out on microgram quantities of extract and demonstrate the discriminating power of (1)H NMR spectroscopy as a definitive dereplication tool.

Compound identification: a Journal of Agricultural and Food Chemistry perspective

This perspective is designed to summarize the standards that authors of manuscripts submitted to the Journal of Agricultural and Food Chemistry are expected to follow in establishing the structures of either new or unknown compounds identified in the course of a study. It is especially important that the molecular formulas of new compounds be determined by either high-resolution mass spectrometry or combustion analysis. All relevant physical, spectroscopic, and spectrometric data should also be reported, so that other research workers have criteria for comparison with compounds that may be isolated in the future. In the case of flavor and aroma constituents, it is not sufficient to depend upon mass spectrometric identifications based solely on comparison with commercial databases. Mass spectra and retention indices on GC stationary phases of different polarities must be determined and the results compared to data for reference compounds and with commercial standards, when available. If geometric or positional isomers may be present, or for chiral compounds, the retention indices of all isomers or enantiomers must be determined. Odor properties or odor thresholds determined by GC-olfactometry may also serve as appropriate tools for compound identification. Adherence to these standards will ensure that processing of manuscripts proceeds expeditiously and that the high standards of the Journal are maintained.

Liquid chromatography–tandem mass spectrometric method for determination of the anti-inflammatory compound vicenin-2 in the leaves ofL. ericoides Mart

This paper reports a rapid and sensitive method for determination of the anti-inflammatory compound vicenin-2 in L. ericoides Mart. using liquid chromatography-tandem mass spectrometry. Separation of the compound of interest was performed on a VP-ODS(18) (150 x 2 mm, Shimadzu, Japan) column and a pre-column packed with GPV-ODS C(18) (5 x 2 mm, Sigma-Aldrich, USA) with acetonitrile-water (15:85) mobile phase containing 2% acetic acid using isocratic flow at 0.5 mL/min for 2 min. Multiple-reaction monitoring of vicenin-2 was performed using electrospray positive ionization. The linear calibration curves were generated using a concentration range of 5-2500 ng/mL with correlation coefficients >0.99. The values of limit of detection and limit of quantitation were found to be 1 and 5 ng/mL, respectively. The method developed based on LC-ESIMS/MS is advantageous because it permits the rapid and selective detection of vicenin-2. Furthermore, the method can be easily applied to the routine analysis of vicenin-2 in plant extracts using a minimal amount of sample.

New perspectives on natural products in TB drug research

The challenge of discovering new, urgently needed anti-TB drugs from natural sources requires a truly interdisciplinary research. Cutting-edge mycobacteriology and innovative natural products chemistry tools have to be developed and employed in tandem, in order to meet these demands. The present review provides cross-linkage to the most recent literature on anti-TB active natural products and summarizes the recent developments in both fields and their potential to impact the early steps of the TB drug discovery process.

Research opportunities for bioactive natural constituents in agriculture and food prepared for the 50th anniversary of the Journal of Agricultural and Food Chemistry

The Journal of Agricultural and Food Chemistry recently introduced a new subject matter category titled "Bioactive Constituents" to cover investigations of the composition of natural compounds and their biological activity in crops and foods. It is recognized by the Editors that a number of other journals specialize in various aspects of the chemistry of natural products, but the intent of this classification is to emphasize and stimulate submission of manuscripts in such areas of agricultural and food chemistry that have so far been neglected or under-represented. Selected topics dealing with bioactive constituents are given as representative examples of the types of investigations that would be appropriate to the scope of the Journal.