Exploring correlations between MS and NMR for compound identification using essential oils: A pilot study

Novel Developments and Progress in Two-Dimensional Correlation Spectroscopy (2D-COS)

This first of the two-part series of the comprehensive survey review on the progress of the two-dimensional correlation spectroscopy (2D-COS) field during the period 2021-2022, covers books, reviews, tutorials, novel concepts and theories, and patent applications that appeared in the last two years, as well as some inappropriate use or citations of 2D-COS. The overall trend clearly shows that 2D-COS is continually growing and evolving with notable new developments. The technique is well recognized as a powerful analytical tool that provides deep insights into systems in many science fields.

Diverse Applications of Two-Dimensional Correlation Spectroscopy (2D-COS)

This second of the two-part series of a comprehensive survey review provides the diverse applications of two-dimensional correlation spectroscopy (2D-COS) covering different probes, perturbations, and systems in the last two years. Infrared spectroscopy has maintained its top popularity in 2D-COS over the past two years. Fluorescence spectroscopy is the second most frequently used analytical method, which has been heavily applied to the analysis of heavy metal binding, environmental, and solution systems. Various other analytical methods including laser-induced breakdown spectroscopy, dynamic mechanical analysis, differential scanning calorimetry, capillary electrophoresis, seismologic, and so on, have also been reported. In the last two years, concentration, composition, and pH are the main effects of perturbation used in the 2D-COS fields, as well as temperature. Environmental science is especially heavily studied using 2D-COS. This comprehensive survey review shows that 2D-COS undergoes continuous evolution and growth, marked by novel developments and successful applications across diverse scientific fields.

Metabolomics: An Emerging "Omics" Platform for Systems Biology and Its Implications for Huntington Disease Research

Huntington's disease (HD) is a progressive, fatal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. The precise mechanisms of HD progression are poorly understood; however, it is known that there is an expansion of the trinucleotide cytosine-adenine-guanine (CAG) repeat in the Huntingtin gene. Important new strategies are of paramount importance to identify early biomarkers with predictive value for intervening in disease progression at a stage when cellular dysfunction has not progressed irreversibly. Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under certain conditions and is becoming an essential tool for the systemic characterization of metabolites to provide a snapshot of the functional and pathophysiological states of an organism and support disease diagnosis and biomarker discovery. This review briefly highlights the historical progress of metabolomic methodologies, followed by a more detailed review of the use of metabolomics in HD research to enable a greater understanding of the pathogenesis, its early prediction, and finally the main technical platforms in the field of metabolomics.

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

Understanding the Seasonal Effect of Metabolite Production in Terminalia catappa L. Leaves through a Concatenated MS- and NMR-Based Metabolomics Approach

Terminalia catappa L. (Combretaceae) is a medicinal plant that is part of the Brazilian biodiversity; this plant is popularly used for the treatment of a wide range of diseases. To better understand the chemical composition of T. catappa in different seasons, we conducted a thorough study using LC-MS and NMR data analysis techniques. The study helped obtain a chemical profile of the plant ethanolic extracts in different seasons of the year (spring, summer, autumn, and winter). The dereplication of LC-HRMS data allowed the annotation of 90 compounds in the extracts of T. catappa (hydrolyzable tannins, ellagic acid derivatives, and glycosylated flavonoids). Triterpenes and C-glycosyl flavones were the compounds that significantly contributed to differences observed between T. catappa plant samples harvested in autumn/winter and spring, respectively. The variations observed in the compound composition of the plant leaves may be related to processes induced by environmental stress and leaf development. Data fusion applied in the metabolomic profiling study allowed us to identify metabolites with greater confidence, and provided a better understanding regarding the production of specialized metabolites in T. catappa leaves under different environmental conditions, which may be useful to establish appropriate quality criteria for the standardization of this medicinal plant.

Data Fusion-based Discovery (DAFdiscovery) pipeline to aid compound annotation and bioactive compound discovery across diverse spectral data

INTRODUCTION

Data Fusion-based Discovery (DAFdiscovery) is a pipeline designed to help users combine mass spectrometry (MS), nuclear magnetic resonance (NMR), and bioactivity data in a notebook-based application to accelerate annotation and discovery of bioactive compounds. It applies Statistical Total Correlation Spectroscopy (STOCSY) and Statistical HeteroSpectroscopy (SHY) calculation in their data using an easy-to-follow Jupyter Notebook.

METHOD

Different case studies are presented for benchmarking, and the resultant outputs are shown to aid natural products identification and discovery. The goal is to encourage users to acquire MS and NMR data from their samples (in replicated samples and fractions when available) and to explore their variance to highlight MS features, NMR peaks, and bioactivity that might be correlated to accelerated bioactive compound discovery or for annotation-identification studies.

RESULTS

Different applications were demonstrated using data from different research groups, and it was shown that DAFdiscovery reproduced their findings using a more straightforward method.

CONCLUSION

DAFdiscovery has proven to be a simple-to-use method for different situations where data from different sources are required to be analyzed together.

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

The release of the FDA’s guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.