Discovery and Characterization of Phenolic Compounds in Bearberry (Arctostaphylos uva-ursi) Leaves Using Liquid Chromatography-Ion Mobility-High-Resolution Mass Spectrometry

Olive tree leaves as a great source of phenolic compounds: Comprehensive profiling of NaDES extracts

Waste from the olive industry is a noticeable source of antioxidant compounds that can be extracted and reused to produce raw materials related to the chemical, cosmetic, food and pharmaceutical sectors. This work studies the phenolic composition of olive leaf samples using liquid chromatography with ultraviolet detection coupled to mass spectrometry (LC-UV-MS). Olive leaf waste samples have been crushed, homogenized, and subjected to a solid-liquid extraction treatment with mechanical shaking at 80 °C for 2 h using Natural Deep Eutectic Solvents (NaDES). The phenolic compound identification in the resulting extracts has been carried out by high-resolution mass spectrometry (HRMS) using data-dependent acquisition mode using an Orbitrap HRMS instrument. >60 different phenolic compounds have been annotated tentatively, of which about 20 have been confirmed from the corresponding standards. Some of the most noticeable compounds are oleuropein and its aglycone and glucoside form, luteolin-7-O-glucoside, 3-hydroxytyrosol, and verbascoside.

Characterization of the Polyphenolic Profile in Tomato (Lycopersicon esculentum P. Mill) Peel and Seeds by LC-HRMS/MS

Peel and seeds are the main byproducts from tomato (Lycopersicon esculentum P. Mill) processing with high concentrations of polyphenols that have been underexploited. Herein, polyphenolic profiles in tomato peel and seeds were elucidated by untargeted liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) with an LTQ Orbitrap analyzer. Samples from two Spanish regions─"Murcia" and "Almería"─were analyzed to obtain complementary results. 57 compounds were found, mainly phenolic acids and flavonoids, of which eight were identified for the first time in tomato. Polyphenols were more abundant in byproducts from "Murcia" samples than in those from"Almería" samples, where the abundance of compounds like coutaric, caffeic, neochlorogenic, dicaffeoylquinic and ferulic acids, vanillic acid hexoside, catechin, naringenin, prunin, apigenin-O-hexoside, rutin, and rutin-O-pentoside was even much higher in byproducts than that in whole fruits. These results reveal the wide range of polyphenols found in tomato byproducts, with potential applications in pharmaceutical research, food preservation, and cosmetic development, among others.

Suspect and nontarget screening of per- and polyfluoroalkyl substances based on ion mobility mass spectrometry and machine learning techniques

High-resolution mass spectrometry (HRMS)-based suspect and nontarget screening techniques are powerful tools for the comprehensive identification of per- and polyfluoroalkyl substances (PFASs), but the interference of complex matrices (especially for wastewater) pose an analytical challenge. This study explored the potential of combining ion mobility spectrometry (IMS) with HRMS and machine learning techniques to achieve the rapid and accurate suspect and nontarget screening of PFAS in wastewater. There were fewer interfering peaks and a clearer spectrum in the data acquired by IMS-HRMS than conventional HRMS. The introduction of collision cross section (CCS) in PFAS homologous series search could filter out 63% of false positive results. Retention time and CCS prediction models were helpful in improving the confidence for PFAS qualitative identification and the random forest algorithm combined with RDKit descriptor performed best for CCS prediction. With the inclusion of extra dimensional information, this study also proposed a comprehensive and concise confidence assignment criterion to better convey the certainty of the qualitative identification of PFAS. Finally, a total of 56 potential PFASs were identified in the wastewater sample using the newly developed method and 45 of them were identified outside reference standards, emphasizing the importance of suspect and nontarget screening for PFAS.

Application of Ion Mobility Spectrometry and the Derived Collision Cross Section in the Analysis of Environmental Organic Micropollutants

Ion mobility spectrometry (IMS) is a rapid gas-phase separation technique, which can distinguish ions on the basis of their size, shape, and charge. The IMS-derived collision cross section (CCS) can serve as additional identification evidence for the screening of environmental organic micropollutants (OMPs). In this work, we summarize the published experimental CCS values of environmental OMPs, introduce the current CCS prediction tools, summarize the use of IMS and CCS in the analysis of environmental OMPs, and finally discussed the benefits of IMS and CCS in environmental analysis. An up-to-date CCS compendium for environmental contaminants was produced by combining CCS databases and data sets of particular types of environmental OMPs, including pesticides, drugs, mycotoxins, steroids, plastic additives, per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs), as well as their well-known transformation products. A total of 9407 experimental CCS values from 4170 OMPs were retrieved from 23 publications, which contain both drift tube CCS in nitrogen (DTCCSN2) and traveling wave CCS in nitrogen (TWCCSN2). A selection of publicly accessible and in-house CCS prediction tools were also investigated; the chemical space covered by the training set and the quality of CCS measurements seem to be vital factors affecting the CCS prediction accuracy. Then, the applications of IMS and the derived CCS in the screening of various OMPs were summarized, and the benefits of IMS and CCS, including increased peak capacity, the elimination of interfering ions, the separation of isomers, and the reduction of false positives and false negatives, were discussed in detail. With the improvement of the resolving power of IMS and enhancements of experimental CCS databases, the practicability of IMS in the analysis of environmental OMPs will continue to improve.

Global Profiling of the Antioxidant Constituents in Chebulae Fructus Based on an Integrative Strategy of UHPLC/IM-QTOF-MS, MS/MS Molecular Networking, and Spectrum-Effect Correlation

An integrative strategy of UHPLC/IM-QTOF-MS analysis, MS/MS molecular networking (MN), in-house library search, and a collision cross-section (CCS) simulation and comparison was developed for the rapid characterization of the chemical constituents in Chebulae Fructus (CF). A total of 122 Constituents were identified, and most were phenolcarboxylic and tannic compounds. Subsequently, 1,3,6-tri-O-galloyl-β-d-glucose, terflavin A, 1,2,6-tri-O-galloyl-β-d-glucose, punicalagin B, chebulinic acid, chebulagic acid, 1,2,3,4,6-penta-O-galloyl-β-d-glucose, and chebulic acid, among the 23 common constituents of CF, were screened out by UPLC-PDA fingerprinting and multivariate statistical analyses (HCA, PCA, and OPLS-DA). Then, Pearson's correlation analysis and a grey relational analysis were performed for the spectrum-effect correlation between the UPLC fingerprints and the antioxidant capacity of CF, which was finally validated by an UPLC-DPPH• analysis for the main antioxidant constituents. Our study provides a global identification of CF constituents and contributes to the quality control and development of functional foods and preparations dedicated to CF.

β-triketone herbicide exposure cause tyrosine and fat accumulation in Caenorhabditis elegans

β-triketone herbicides have been efficiently employed as an alternate to atrazine. Triketones are 4-hydroxyphenylpyruvate dioxygenase (HPPD) enzyme inhibitors and exposure is reported to cause significant increase in plasma tyrosine levels. In this study, we have employed a non-target organism Caenorhabditis elegans to determine the impact of β-triketone exposures at recommended field doses (RfD). Our results indicate sulcotrione and mesotrione, negatively influence the survival, behavior, and reproduction of the organism at RfD. Additionally, we have traced the parallels regarding the impact of triketones on the tyrosine metabolism pathway, in C. elegans to those in mammalian models, wherein the expression of the tyrosine metabolism pathway genes are altered, directly influencing tyrosine catabolism leading to significant tyrosine accumulation in exposed organism. Further, we investigated the impact of sulcotrione and mesotrione exposure on fat deposition (triglyceride levels, Oil-Red-O staining and lipidomics) and the fatty acid metabolism pathway. In the exposed worms, the expression of enlongases and fatty acid desaturases were up-regulated along with an increase in the levels of triglycerides. Thus, the data indicates a positive association of β-triketone exposure to mis-regulation of the fatty acid metabolism pathway genes leading to fat accumulation in worms. Therefore, β-triketone might be a potential obesogen.

Collision Cross Section Prediction Based on Machine Learning

Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.

Traveling Wave Ion Mobility-Derived Collision Cross Section Database for Plant Specialized Metabolites: An Application to Ventilago harmandiana Pierre

The combination of ion mobility mass spectrometry (IM-MS) and chromatography is a valuable tool for identifying compounds in natural products. In this study, using an ultra-performance liquid chromatography system coupled to a high-resolution quadrupole/traveling wave ion mobility spectrometry/time-of-flight MS (UPLC-TWIMS-QTOF), we have established and validated a comprehensive TWCCSN2 and MS database for 112 plant specialized metabolites. The database included 15 compounds that were isolated and purified in-house and are not commercially available. We obtained accurate m/z, retention times, fragment ions, and TWIMS-derived CCS (TWCCSN2) values for 207 adducts (ESI+ and ESI-). The database included novel 158 TWCCSN2 values from 79 specialized metabolites. In the presence of plant matrix, the CCS measurement was reproducible and robust. Finally, we demonstrated the application of the database to extend the metabolite coverage of Ventilago harmandiana Pierre. In addition to pyranonaphthoquinones, a group of known specialized metabolites in V. harmandiana, we identified flavonoids, xanthone, naphthofuran, and protocatechuic acid for the first time through targeted analysis. Interestingly, further investigation using IM-MS of unknown features suggested the presence of organonitrogen compounds and lipid and lipid-like molecules, which is also reported for the first time. Data are available on the MassIVE (https://massive.ucsd.edu, data set identifier MSV000090213).

Identification of Nonvolatile Migrates from Food Contact Materials Using Ion Mobility-High-Resolution Mass Spectrometry and in Silico Prediction Tools

The identification of migrates from food contact materials (FCMs) is challenging due to the complex matrices and limited availability of commercial standards. The use of machine-learning-based prediction tools can help in the identification of such compounds. This study presents a workflow to identify nonvolatile migrates from FCMs based on liquid chromatography-ion mobility-high-resolution mass spectrometry together with in silico retention time (RT) and collision cross section (CCS) prediction tools. The applicability of this workflow was evaluated by screening the chemicals that migrated from polyamide (PA) spatulas. The number of candidate compounds was reduced by approximately 75% and 29% on applying RT and CCS prediction filters, respectively. A total of 95 compounds were identified in the PA spatulas of which 54 compounds were confirmed using reference standards. The development of a database containing predicted RT and CCS values of compounds related to FCMs can aid in the identification of chemicals in FCMs.

Construction of PEGylated boronate-affinity-oriented imprinting magnetic nanoparticles for ultrasensitive detection of ellagic acid from beverages

Molecularly imprinted polymers (MIPs) can exhibit antibody-level affinity for target molecules. However, the nonspecific adsorption of non-imprinted regions for non-target molecules limits the application range of MIPs. Herein, we fabricated PEGylated boronate-affinity-oriented ellagic acid-imprinting magnetic nanoparticles (PBEMN), which first integrated boronate-affinity-oriented surface imprinting and sequential PEGylation for small molecule-imprinted MIPs. The resultant PBEMN possess higher adsorption capacity and faster adsorption rate for template ellagic acid (EA) molecules than the non-PEGylated control. To prove the excellent performance, the PBEMN were linked with hydrophilic boronic acid-modified/fluorescein isothiocyanate-loaded graphene oxide (BFGO), because BFGO could selectively label cis-diol-containing substances by boronate-affinity and output ultrasensitive fluorescent signals. Based on a dual boronate-affinity synergy, the PBEMN first selectively captured EA molecules by boronate-affinity-oriented molecular imprinted recognition, and then the EA molecules were further labeled with BFGO through boronate-affinity. The PBEMN linked BFGO (PBPF) strategy provided ultrahigh sensitivity for EA molecules with a limit of detection of 39.1 fg mL^-1, resulting from the low nonspecific adsorption of PBEMN and the ultrasensitive fluorescence signal of BFGO. Lastly, the PBPF strategy was successfully employed in the determination of EA concentration in a spiked beverage sample with recovery and relative standard deviation in the range of 96.5 to 104.2% and 3.8 to 5.1%, respectively. This work demonstrates that the integration of boronate-affinity-oriented surface imprinting and sequential PEGylation may be a universal tool for improving the performance of MIPs.

Prediction of Collision Cross-Section Values for Extractables and Leachables from Plastic Products

The use of ion mobility separation (IMS) in conjunction with high-resolution mass spectrometry has proved to be a reliable and useful technique for the characterization of small molecules from plastic products. Collision cross-section (CCS) values derived from IMS can be used as a structural descriptor to aid compound identification. One limitation of the application of IMS to the identification of chemicals from plastics is the lack of published empirical CCS values. As such, machine learning techniques can provide an alternative approach by generating predicted CCS values. Herein, experimental CCS values for over a thousand chemicals associated with plastics were collected from the literature and used to develop an accurate CCS prediction model for extractables and leachables from plastic products. The effect of different molecular descriptors and machine learning algorithms on the model performance were assessed. A support vector machine (SVM) model, based on Chemistry Development Kit (CDK) descriptors, provided the most accurate prediction with 93.3% of CCS values for [M + H]⁺ adducts and 95.0% of CCS values for [M + Na]⁺ adducts in testing sets predicted with <5% error. Median relative errors for the CCS values of the [M + H]⁺ and [M + Na]⁺ adducts were 1.42 and 1.76%, respectively. Subsequently, CCS values for the compounds in the Chemicals associated with Plastic Packaging Database and the Food Contact Chemicals Database were predicted using the SVM model developed herein. These values were integrated in our structural elucidation workflow and applied to the identification of plastic-related chemicals in river water. False positives were reduced, and the identification confidence level was improved by the incorporation of predicted CCS values in the suspect screening workflow.

Proteome changes in human bladder T24 cells induced by hydroquinone derived from Arctostaphylos uva-ursi herbal preparation

ETHNOPHARMACOLOGICAL RELEVANCE

Arctostaphylos uva-ursi (L.) Spreng. (bearberry) is a well-known traditional herbal plant used as a urinary tract disinfectant. Its antiseptic and diuretic properties can be attributed to hydroquinone, obtained by hydrolysis of arbutin.

AIM OF THE STUDY

This study aimed to determine the toxic profile of free hydroquinone on urinary bladder cells (T24) as a target of therapeutic action.

MATERIALS AND METHODS

Quantitative and qualitative analysis of the extract and the digestive stability and bioavailability of arbutin and hydroquinone were performed by HPLC assay and simulated in vitro digestion, respectively. Cytotoxic effect, reactive oxygen species induction and proteome changes in T24 cells after hydroquinone treatment were determined using Neutral red assay, 2',7'-dichlorofluorescein-diacetate (DCFH-DA) assay and mass spectrometry, respectively.

RESULTS

Through in vitro digestion, arbutin was stable, but hydroquinone increased after pepsin treatment (109.6%) and then decreased after the small intestine phase (65.38%). The recommended doses of Uva-ursi had a cytotoxic effect on T24 cells only when all hydroquinone conjugates were converted to free hydroquinone (320 and 900 μg/mL) and the toxic effect was enhanced by recovery. One cup of the therapeutic dose had a prooxidative effect after 4 h of incubation. Shorter time of cell exposure (2 h) to hydroquinone did not have any impact on reactive oxygen species induction. Proteomic analysis found 17 significantly up-regulated proteins compared to control. Hydroquinone activated proteins related to oxidative stress response, stress-adaptive signalling, heat shock response and initiation of translation.

CONCLUSIONS

Despite the therapeutic properties of bearberry, up-regulated T24 cell proteins are evidence that plant compounds, although from a natural source, may exhibit negative properties.

Construction of a Microfluidic Platform With Core-Shell CdSSe@ZnS Quantum Dot-Encoded Superparamagnetic Iron Oxide Microspheres for Screening and Locating Matrix Metalloproteinase-2 Inhibitors From Fruits of Rosa roxburghii

The microfluidic platform is a versatile tool for screening and locating bioactive molecules from functional foods. Here, a layer-by-layer assembly approach was used to fabricate core-shell CdSSe@ZnS quantum dot encoded superparamagnetic iron oxide microspheres, which served as a carrier for matrix metalloproteinase-2. The matrix metalloproteinase-2 camouflaged magnetic microspheres was further incorporated into a homemade microfluidic platform and incubated with extracts of fruits of Rosa roxburghii. The flow rate of the microfluidic platform was tuned. The major influencing parameters on ligand binding, such as dissociate solvents, incubation pH, ion strength, temperature, and incubation time were also optimized by using ellagic acid as a model compound. The specific binding ligands were sent for structure elucidation by mass spectrometry. The absolute recovery of ellagic acid ranged from 101.14 to 102.40% in the extract of R. roxburghii under the optimal extraction conditions. The linearity was pretty well in the range of 0.009–1.00 mg·ml⁻¹ (R² = 0.9995). The limit of detection was 0.003 mg·ml⁻¹. The relative SDs of within-day and between-day precision were <1.91%. A total of thirteen ligands were screened out from fruits of R. roxburghii, which were validated for their inhibitory effect by enzyme assay. Of note, eleven new matrix metalloproteinase-2 inhibitors were identified, which may account for the antitumor effect of fruits of R. roxburghii.

A Collision Cross Section Database for Extractables and Leachables from Food Contact Materials

The chemicals in food contact materials (FCMs) can migrate into food and endanger human health. In this study, we developed a database of traveling wave collision cross section in nitrogen (^TWCCS_N2) values for extractables and leachables from FCMs. The database contains a total of 1038 ^TWCCS_N2 values from 675 standards including those commonly used additives and nonintentionally added substances in FCMs. The ^TWCCS_N2 values in the database were compared to previously published values, and 85.7, 87.7, and 64.9% [M + H]⁺, [M + Na]⁺, and [M - H]^- adducts showed deviations <2%, with the presence of protomers, post-ion mobility spectrometry dissociation of noncovalent clusters and inconsistent calibration are possible sources of CCS deviations. Our experimental ^TWCCS_N2 values were also compared to CCS values from three prediction tools. Of the three, CCSondemand gave the most accurate predictions. The ^TWCCS_N2 database developed will aid the identification and differentiation of chemicals from FCMs in targeted and untargeted analysis.

Variation in Population and Solvents as Factors Determining the Chemical Composition and Antioxidant Potential of Arctostaphylos uva-ursi (L.) Spreng. Leaf Extracts

The bearberry Arctostaphylos uva-ursi (L.) Spreng. has a long history of ethnopharmacological use. This species has been used in folk medicine for centuries as a rich source of raw material abundant in secondary metabolites and is important for medicinal and pharmacological purposes. The plant is a source of herbal material—Uvae ursi folium, which is highly valued and sought by pharmaceutical and cosmetic industries. The studied bearberry leaves can be classified as a suitable herbal material for use in pharmacy; therefore, the investigated populations can be a potentially valuable source of plant material for cultivation and can be used in in vitro cultures and in biotechnological processes. The objective of this study was to characterize the variability of the phytochemical composition and antioxidant activity of water and ethanol bearberry extracts from raw material collected from different natural populations. In each of the twelve A. uva-ursi sites, three leaf samples were collected and analyzed. The water extracts from bearberry leaves were characterized by similar concentration of arbutin (77.64–105.56 mg g⁻¹) and a significantly higher concentration of hydroquinone (6.96–13.08 mg g⁻¹) and corilagin (0.83–2.12 mg g⁻¹) in comparison with the ethanol extracts −77.21–103.38 mg g⁻¹, 10.55–16.72 mg g⁻¹, 0.20–1.54 mg g⁻¹, respectively. The concentration of other metabolites in the water extracts was significantly lower in comparison with the ethanol extracts. In the case of the water extracts, a significant effect of not only total phenolic compounds, but also hydroquinone on the antioxidant parameters, was observed, which indicates the solvent-related activity of these metabolites. Therefore, it is suggested that special attention should be paid to the concentration of not only arbutin, but also hydroquinone in Uvae ursi folium. The latter metabolite serving a very important function as an active bearberry ingredient should be controlled not only in alcoholic extracts but also in water extracts, since bearberry leaves are applied as infusions and decoctions. The results presented in this paper can contribute to appropriate selection of plant material for pharmaceutical, cosmetic, and food industries, with special emphasis on the antioxidant activity of different types of extracts.

Evaluation of Ion Mobility Spectrometry for Improving Constitutional Assignment in Natural Product Mixtures

The comprehensive chemical characterization of biological samples remains a central challenge in the field of natural products. Conventional workflows using liquid chromatography (LC)-coupled high-resolution tandem mass spectrometry (MS/MS or MS2) allow the detection of relevant small molecules while providing diagnostic fragment ions for their structural assignment. Still, many natural product extracts are of a molecular complexity that challenges the resolving power of modern LC-MS2 pipelines. In this study, we examined the effect of integrating ion mobility spectrometry (IMS) to our LC-MS2 platform for the characterization of natural product mixtures. IMS provides an additional axis of separation in the gas phase as well as experimental collision cross-sectional (CCS) values. We analyzed a mixture of 20 commercial standards at 2 concentration ranges, either solubilized in solvent or spiked into an actinobacterial extract. Data were acquired in positive ion mode using both data-dependent acquisition (DDA) and data-independent acquisition (DIA) MS2 fragmentation approaches and assessed for both chemical coverage and spectral quality. IMS-DIA identified the largest number of standards in the spiked extract at the lower concentration of standards (17), followed by IMS-DDA (10), DDA (8), and DIA (6). In addition, we examined how these data sets performed in the Global Natural Products Social Molecular Networking (GNPS) platform. Overall, integrating IMS increased both metabolite detection and the quality of MS2 spectra, particularly for samples analyzed in DIA mode.

Structural diversity and substrate preferences of three tannase enzymes encoded by the anaerobic bacterium Clostridium butyricum

Tannins are secondary metabolites that are enriched in the bark, roots, and knots in trees and are known to hinder microbial attack. The biological degradation of water-soluble gallotannins, such as tannic acid, is initiated by tannase enzymes (EC 3.1.1.20), which are esterases able to liberate gallic acid from aromatic-sugar complexes. However, only few tannases have previously been studied in detail. Here, for the first time, we biochemically and structurally characterize three tannases from a single organism, the anaerobic bacterium Clostridium butyricum, which inhabits both soil and gut environments. The enzymes were named CbTan1-3, and we show that each one exhibits a unique substrate preference on a range of galloyl ester model substrates; CbTan1 and 3 demonstrated preference toward galloyl esters linked to glucose, while CbTan2 was more promiscuous. All enzymes were also active on oak bark extractives. Furthermore, we solved the crystal structure of CbTan2 and produced homology models for CbTan1 and 3. In each structure, the catalytic triad and gallate-binding regions in the core domain were found in very similar positions in the active site compared with other bacterial tannases, suggesting a similar mechanism of action among these enzymes, though large inserts in each enzyme showcase overall structural diversity. In conclusion, the varied structural features and substrate specificities of the C. butyricum tannases indicate that they have different biological roles and could further be used in development of new valorization strategies for renewable plant biomass.

Prediction of Collision Cross Section Values: Application to Non-Intentionally Added Substance Identification in Food Contact Materials

The synthetic chemicals in food contact materials can migrate into food and endanger human health. In this study, the traveling wave collision cross section in nitrogen values of more than 400 chemicals in food contact materials were experimentally derived by traveling wave ion mobility spectrometry. A support vector machine-based collision cross section (CCS) prediction model was developed based on CCS values of food contact chemicals and a series of molecular descriptors. More than 92% of protonated and 81% of sodiated adducts showed a relative deviation below 5%. Median relative errors for protonated and sodiated molecules were 1.50 and 1.82%, respectively. The model was then applied to the structural annotation of oligomers migrating from polyamide adhesives. The identification confidence of 11 oligomers was improved by the direct comparison of the experimental data with the predicted CCS values. Finally, the challenges and opportunities of current machine-learning models on CCS prediction were also discussed.

Recent Analytical Approaches for the Study of Bioavailability and Metabolism of Bioactive Phenolic Compounds

The study of the bioavailability of bioactive compounds is a fundamental step for the development of applications based on them, such as nutraceuticals, functional foods or cosmeceuticals. It is well-known that these compounds can undergo metabolic reactions before reaching therapeutic targets, which may also affect their bioactivity and possible applications. All recent studies that have focused on bioavailability and metabolism of phenolic and terpenoid compounds have been developed because of the advances in analytical chemistry and metabolomics approaches. The purpose of this review is to show the role of analytical chemistry and metabolomics in this field of knowledge. In this context, the different steps of the analytical chemistry workflow (design study, sample treatment, analytical techniques and data processing) applied in bioavailability and metabolism in vivo studies are detailed, as well as the most relevant results obtained from them.