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

A story of falsification and authentication: Authenticity control of phytopharmaceuticals and herbal remedies

Natural remedies and phytopharmaceuticals are gaining popularity among both consumers and patients who seek a healthier and more environmentally friendly lifestyle. Healthcare professionals are also increasingly incorporating phytopharmaceuticals into therapeutic practices, recognizing their potential benefits for overall health and well-being. This trend is further driven by a growing distrust of synthetic compounds and dissatisfaction with conventional medical treatments, prompting many patients to explore natural alternatives. As a result, the demand for phytopharmaceuticals, essential oils, and nutraceuticals continues to rise steadily. However, falsified natural remedies can pose serious risks to human health, making quality assurance a critical priority. Sophisticated and reliable analytical strategies are essential to ensure product authenticity and to protect consumers and patients from potentially harmful adulteration. This review provides an overview of documented cases of adulteration and the corresponding countermeasures developed to detect such incidents. Commonly used analytical techniques for authenticity verification of phytopharmaceuticals, essential oils, and other natural remedies are presented. The review discusses the application of spectroscopic methods, mass spectrometry, chromatographic separation techniques, and DNA-based profiling, with reference to current scientific literature. In addition, selected chemometric tools are introduced to illustrate how meaningful information can be extracted from complex analytical data. The potential of multidimensional data analysis, combining complementary insights from various analytical platforms, is also explored. Finally, key considerations for generating and analysing reliable data are highlighted.

HPTLC Combined with sHetCA and Multivariate Statistics for the Detection of Bioactive Compounds in Complex Mixtures

High-Performance Thin Layer Chromatography (HPTLC) is widely utilized in natural products research due to its simplicity, low cost, and short total analysis time, including data treatment. While bioautography can be used for rapid detection of bioactive compounds in extracts, the number of available bioautographic methods is limited mainly due to the high cost and difficulty in developing protocols that lead to accurate and reproducible results. For this reason, an alternative method for the detection of bioactive compounds in plant extracts prior to their isolation using HPTLC, combined with multivariate chemometrics, was previously explored by our lab. To evaluate this method and compare it to other chemometrics-based methods, an artificial mixture (ArtExtr) of 59 standard compounds was used as a case study. The ArtExtr was fractionated by FCPC and the inhibitory activity of all fractions against DPPH was evaluated, while their chemical profiles were recorded using HPTLC. Multivariate statistics and the heterocovariance approach (HetCA) were employed and compared, with the success rate in detecting the ArtExtr bioactive substances being 85.7% via sparse heterocovariance (sHetCA). HPTLC combined with sHetCA can serve as a valuable tool for the detection of bioactive compounds in complex mixtures when bioautography is not feasible.

Can NMR-HetCA be a Reliable Prediction Tool for the Direct Identification of Bioactive Substances in Complex Mixtures?

Conventional isolation methods in natural products chemistry are time-consuming and costly and often result in the isolation of moderately active compounds or the detection of already known natural products (NPs). A fast and cost-effective way to identify bioactive metabolites in plant extracts prior to isolation has been developed based on the nuclear magnetic resonance (NMR)-heterocovariance approach (NMR-HetCA). In order to evaluate in depth the application of this chemometrics-based drug discovery methodology, simple mixtures of 10 standard NPs simulating a fast centrifugal partition chromatography (FCPC) fractionation (artificial fractions, ArtFrcts), as well as a more complex mixture of 59 natural standard substances simulating a crude plant extract (artificial extract, ArtExtr), were prepared. FCPC was employed for the fractionation of the ArtExtr, while the inhibitory activity of all fractions against DPPH was evaluated, and their chemical profile was recorded using NMR spectroscopy. Spectral information was processed in the MATLAB environment, and statistical approaches, including HetCA and statistical total correlation spectroscopy (STOCSY), were applied to identify bioactive compounds. Total heterocovariance plots (pseudospectra) facilitated the detection of highly correlated metabolites and led to the direct identification of 52.6% of the active compounds. The success in identifying the ArtExtr bioactive substances increased to 63.2% when spectral alignment was implemented. HetCA incorporates chromatographic (fractionation), spectroscopic (NMR profiling), and bioactivity results along with advanced chemometrics and could be established as a method of choice for the rapid and effective identification of bioactive NPs in plant extracts prior to isolation.

Unlocking Biological Activity and Metabolomics Insights: Primary Screening of Cyanobacterial Biomass from a Tropical Reservoir

Cyanobacterial harmful algal blooms can pose risks to ecosystems and human health worldwide due to their capacity to produce natural toxins. The potential dangers associated with numerous metabolites produced by cyanobacteria remain unknown. Only select classes of cyanopeptides have been extensively studied with the aim of yielding substantial evidence regarding their toxicity, resulting in their inclusion in risk management and water quality regulations. Information about exposure concentrations, co-occurrence, and toxic impacts of several cyanopeptides remains largely unexplored. We used liquid chromatography-mass spectrometry (LC-MS)-based metabolomic methods associated with chemometric tools (NP Analyst and Data Fusion-based Discovery), as well as an acute toxicity essay, in an innovative approach to evaluate the association of spectral signatures and biological activity from natural cyanobacterial biomass collected in a eutrophic reservoir in southeastern Brazil. Four classes of cyanopeptides were revealed through metabolomics: microcystins, microginins, aeruginosins, and cyanopeptolins. The bioinformatics tools showed high bioactivity correlation scores for compounds of the cyanopeptolin class (0.54), in addition to microcystins (0.54-0.58). These results emphasize the pressing need for a comprehensive evaluation of the (eco)toxicological risks associated with different cyanopeptides, considering their potential for exposure. Our study also demonstrated that the combined use of LC-MS/MS-based metabolomics and chemometric techniques for ecotoxicological research can offer a time-efficient strategy for mapping compounds with potential toxicological risk. Environ Toxicol Chem 2024;43:2222-2231. © 2024 SETAC.

High-speed countercurrent chromatography with offline detection by electrospray mass spectrometry and nuclear magnetic resonance detection as a tool to resolve complex mixtures: A practical approach using Coffea arabica leaf extract

INTRODUCTION

Many secondary metabolites isolated from plants have been described in the literature owing to their important biological properties and possible pharmacological applications. However, the identification of compounds present in complex plant extracts has remained a great scientific challenge, is often laborious, and requires a long research time with high financial cost.

OBJECTIVES

The aim of this study was to develop a method that allows the identification of secondary metabolites in plant extracts with a high degree of confidence in a short period of time.

MATERIAL AND METHODS

In this study, an ethanolic extract of Coffea arabica leaves was used to validate the proposed method. Countercurrent chromatography was chosen as the initial step for extraction fractionation using gradient elution. Resulting fractions presented a variation of compounds concentrations, allowing for statistical total correlation spectroscopy (STOCSY) calculations between liquid chromatography coupled with high-resolution tandem mass spectrometry (LC-HRMS/MS) and NMR across fractions.

RESULTS

The proposed method allowed the identification of 57 compounds. Of the annotated compounds, 20 were previously described in the literature for the species and 37 were reported for the first time. Among the inedited compounds, we identified flavonoids, alkaloids, phenolic acids, coumarins, and terpenes.

CONCLUSION

The proposed method presents itself as a valid alternative for the study of complex extracts in an effective, fast, and reliable way that can be reproduced in the study of other extracts.

NMR as a tool for compound identification in mixtures

INTRODUCTION

Natural products and metabolomics are intrinsically linked through efforts to analyze complex mixtures for compound annotation. Although most studies that aim for compound identification in mixtures use MS as the main analysis technique, NMR has complementary advances that are worth exploring for enhanced structural confidence.

OBJECTIVE

This review aimed to showcase a portfolio of the main tools available for compound identification using NMR.

MATERIALS AND METHODS

COLMAR, SMART-NMR, MADByTE, and NMRfilter are presented using examples collected from real samples from the perspective of a natural product chemist. Data are also made available through Zenodo so that readers can test each case presented here.

CONCLUSION

The acquisition of ¹ H NMR, HSQC, TOCSY, HSQC-TOCSY, and HMBC data for all samples and fractions from a natural products study is strongly suggested. The same is valid for MS analysis to create a bridged analysis between both techniques in a complementary manner. The use of NOAH supersequences has also been suggested and demonstrated to save NMR time.

Towards Decoding Hepatotoxicity of Approved Drugs through Navigation of Multiverse and Consensus Chemical Spaces

Drug-induced liver injury (DILI) is the principal reason for failure in developing drug candidates. It is the most common reason to withdraw from the market after a drug has been approved for clinical use. In this context, data from animal models, liver function tests, and chemical properties could complement each other to understand DILI events better and prevent them. Since the chemical space concept improves decision-making drug design related to the prediction of structure-property relationships, side effects, and polypharmacology drug activity (uniquely mentioning the most recent advances), it is an attractive approach to combining different phenomena influencing DILI events (e.g., individual "chemical spaces") and exploring all events simultaneously in an integrated analysis of the DILI-relevant chemical space. However, currently, no systematic methods allow the fusion of a collection of different chemical spaces to collect different types of data on a unique chemical space representation, namely "consensus chemical space." This study is the first report that implements data fusion to consider different criteria simultaneously to facilitate the analysis of DILI-related events. In particular, the study highlights the importance of analyzing together in vitro and chemical data (e.g., topology, bond order, atom types, presence of rings, ring sizes, and aromaticity of compounds encoded on RDKit fingerprints). These properties could be aimed at improving the understanding of DILI events.