Development and validation of online SPE purification coupled to HILIC-fluorescence-MS analysis for the characterization of N-glycans.

N-glycans of therapeutic glycoproteins is a critical quality attribute to be addressed. We developed a sensitive method for N-glycan characterization using procainamide (ProcA) labelling and online solid phase extraction (online SPE). N-glycans were enzymatically released, then labeled with ProcA and cleaned up via the online SPE using HILIC chemistry (online HILIC SPE). Two preparation protocols were optimized: a short one (1 h 30) and a long one (18 h). Furthermore, the developed approach was compared to RapiFluor-MS (RFMS) kit (from Waters) and to InstantPC kit (from Agilent) which both include a classical HILIC µElution plate SPE purification. Samples were analyzed using HILIC separation coupled to fluorescence and MS detection (HILIC-FLD-MS) with or without the online HILIC SPE. During the validation, repeatability, intermediate precision, stability, response function and injection volume were tested. Human IgG mix (Multigam®) and NIST mAb standard were used as references as their glycoprofiles are well described. A comparison of three batches of a rituximab biosimilar (Truxima®) and one batch of its originator (MabThera®) was also performed. Online HILIC SPE sample cleanup shows a higher sensitivity and repeatability compared to the classical HILIC µElution SPE. Our online HILIC SPE approach also offers the highest MS signal compared to both commercial kits. However, InstantPC shows the highest FLD signal. The analyses of rituximab samples were in line with the literature showing the efficiency of the method for N-glycan monitoring of biotherapeutics. In conclusion, the results demonstrated the usefulness and ease of application of the developed protocol with the online HILIC SPE purification.

Unravelling the brain metabolome: A review of liquid chromatography - mass spectrometry strategies for extracellular brain metabolomics.

The analysis of the brain extracellular metabolome is of interest for numerous subdomains within neuroscience. Not only does it provide information about normal physiological functions, it is even more of interest for biomarker discovery and target discovery in disease. The extracellular analysis of the brain is particularly interesting as it provides information about the release of mediators in the brain extracellular fluid to look at cellular signaling and metabolic pathways through the release, diffusion and re-uptake of neurochemicals. In vivo samples are obtained through microdialysis, cerebral open-flow microperfusion or solid-phase microextraction. The analytes of potential interest are typically low in concentration and can have a wide range of physicochemical properties. Liquid chromatography coupled to mass spectrometry has proven its usefulness in brain metabolomics. It allows sensitive and specific analysis of low sample volumes, obtained through different approaches. Several strategies for the analysis of the extracellular fluid have been proposed. The most widely used approaches apply sample derivatization, specific stationary phases and/or hydrophilic interaction liquid chromatography. Miniaturization of these methods allows an even higher sensitivity. The development of chiral metabolomics is indispensable, as it allows to compare the enantiomeric ratio of compounds and provides even more challenges. Some limitations continue to exist for the previously developed methods and the development of new, more sensitive methods remains needed. This review provides an overview of the methods developed for sampling and liquid chromatography-mass spectrometry analysis of the extracellular metabolome.

Cannabis profiling of seized samples: An intra-location variability study using gas chromatography-mass spectrometry profiles and multivariate data analysis.

Yearly, cannabis belongs to the most seized drugs worldwide. During judicial investigations, illicit cannabis profiling can be performed to compare seized herbal material. However, comparison is challenging because of the natural heterogeneity of the psychoactive crop. Gas chromatography-mass spectrometry (GC-MS) profiles, consisting of eight cannabinoids, were used to study the intra-location (within) and inter-location (between) variabilities. Decision thresholds were derived from the 95% and 99% confidence limits, applying Pearson correlation coefficients for the intra-location samples. The false negatives and false positives (FPs) determined the discriminative power of different pretreatments applied to obtain the lowest FP error rate possible. Initially, a 97 samples data set was used and with log transformation as pretreatment, a decrease in FPs from 38% and 45% FPs to 17% and 22%, for both confidence limits, respectively, was seen relative to internal standard normalization that was used as reference. An additional intra-plantation variability study with 130 samples verified whether the initial model contained sufficient within-location information, but this was not the case. Hence, a combined data matrix was constructed with all seized samples. Log transformation provided the best FP results for both limits, that is, an improvement from 58% and 64% to 21% and 26%, respectively, was seen. The representativeness of these 'linked' thresholds was demonstrated using both cross-validation and an external set, for which similar FP results as for the calibration set were obtained. By applying data pretreatment, a significant improvement was observed to distinguish seized samples. However, the FP rate is still not at an acceptable level to defend in court.

Evaluation of Multivariate Filters on Vibrational Spectroscopic Fingerprints for the PLS-DA and SIMCA Classification of Argan Oils from Four Moroccan Regions.

This study aimed to develop an analytical method to determine the geographical origin of Moroccan Argan oil through near-infrared (NIR) or mid-infrared (MIR) spectroscopic fingerprints. However, the classification may be problematic due to the spectral similarity of the components in the samples. Therefore, unsupervised and supervised classification methods-including principal component analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA)-were evaluated to distinguish between Argan oils from four regions. The spectra of 93 samples were acquired and preprocessed using both standard preprocessing methods and multivariate filters, such as External Parameter Orthogonalization, Generalized Least Squares Weighting and Orthogonal Signal Correction, to improve the models. Their accuracy, precision, sensitivity, and selectivity were used to evaluate the performance of the models. SIMCA and PLS-DA models generated after standard preprocessing failed to correctly classify all samples. However, successful models were produced after using multivariate filters. The NIR and MIR classification models show an equivalent accuracy. The PLS-DA models outperformed the SIMCA with 100% accuracy, specificity, sensitivity and precision. In conclusion, the studied multivariate filters are applicable on the spectroscopic fingerprints to geographically identify the Argan oils in routine monitoring, significantly reducing analysis costs and time.

Modelling the enantiorecognition of structurally diverse pharmaceuticals on O-substituted polysaccharide-based stationary phases.

This study aims to develop models to predict the retention, separation and elution sequence of the enantiomers of structurally diverse pharmaceuticals. More specifically, Quantitative Structure Retention Relationships (QSRR) models are built that describe the relationship between molecular descriptors and retention. Eighteen structurally diverse chiral mixtures, each consisting of a pair of enantiomers, were analyzed on two polysaccharide chiral stationary phases, Chiralcel OD-RH (cellulose tris(3,5-dimethylphenylcarbamate)) and Lux amylose-2 (amylose tris(5-chloro-2-methylphenylcarbamate)), applying either a basic or an acidic mobile phase, and their retention factor and elution sequence were determined. Both achiral and, in-house defined, chiral descriptors were used as descriptive variables to build the models. Linear regression techniques, i.e. stepwise multiple linear regression (sMLR) and partial least squares (PLS) regression, were applied to model the retention or separation as a function of the descriptors. In a first step, models were built with only achiral descriptors to model the global retention of both enantiomers of a chiral molecule. Subsequently, models were built with only chiral descriptors to predict the enantioseparation and elution sequence, and finally, models were considered with both descriptor types to predict the retention, the separation and the elution sequence of the enantiomers. The global retention was predicted well by the sMLR models with only achiral descriptors. The models with only chiral descriptors were not found suitable to predict the enantioseparation and elution sequence. Finally, the models containing both chiral and achiral descriptors allowed predicting the retention well, but their ability to predict the elution sequence and separation of the enantiomers differed widely for the chromatographic systems considered.

Improving the LC-MS/MS analysis of neuromedin U-8 and neuromedin S by minimizing their adsorption behavior and optimizing UHPLC and MS parameters.

Neuromedin U (NmU) and neuromedin S (NmS) are two closely related neuropeptides belonging to the neuromedin family. NmU usually occurs either as a truncated eight amino acid long peptide (NmU-8) or as an 25 amino acid long peptide, although other molecular forms exist depending on the species considered. NmS, on the other hand, is a 36 amino acid long peptide, sharing the same amidated C-terminal heptapeptide with NmU. Nowadays, liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is the preferred analytical technique for peptide quantification, because of its excellent sensitivity and selectivity. However, reaching the required quantification limits for these compounds in biological samples remains an extremely challenging task, especially because of their nonspecific binding (NSB). This study highlights the difficulties that are faced when quantifying larger neuropeptides (23-36 amino acids) compared to smaller ones (< 15 amino acids). The first part of this work aims to solve the adsorption problem for NmU-8 and NmS, by investigating the different steps involved in the sample preparation, i.e. the different solvents applied and the pipetting protocol. The addition of 0.05% plasma as an adsorption competitor was found to be primordial to avoid peptide loss due to NSB. The second part of this work focusses on further improving the sensitivity of the LC-MS/MS method for NmU-8 and NmS, by evaluating some UHPLC-parameters, including the stationary phase, the column temperature and the trapping conditions. For both peptides of interest, the best results were achieved when combining a C18 trap column with a C18 iKey separation device containing a positively charged surface. Column temperatures of 35 and 45 °C for NmU-8 and NmS respectively, resulted in the highest peak areas and S/N ratios, while applying higher column temperatures substantially decreased sensitivity. Moreover, a gradient starting at 20% organic modifier instead of 5% significantly improved the peak shape of both peptides. Finally, some compound-specific MS parameters, i.e. the capillary and the cone voltages, were evaluated. The peak areas increased with a factor 2 and 7 for NmU-8 and NmS respectively and peptide detection in the low picomolar range is now feasible.

Quality Control and Authentication of Argan Oils: Application of Advanced Analytical Techniques.

In addition to the nutritional and therapeutic benefits, Argan oil is praised for its unique bio-ecological and botanic interest. It has been used for centuries to treat cardiovascular issues, diabetes, and skin infections, as well as for its anti-inflammatory and antiproliferative properties. Argan oil is widely commercialized as a result of these characteristics. However, falsifiers deliberately blend Argan oil with cheaper vegetable oils to make economic profits. This reduces the quality and might result in health issues for consumers. Analytical techniques that are rapid, precise, and accurate are employed to monitor its quality, safety, and authenticity. This review provides a comprehensive overview of studies on the quality assessment of Moroccan Argan oil using both untargeted and targeted approaches. To extract relevant information on quality and adulteration, the analytical data are coupled with chemometric techniques.

Comparison of supercritical fluid chromatographic methods to predict the skin permeability of pharmaceutical and cosmetic compounds.

Supercritical fluid chromatography (SFC) was explored as an alternative for liquid chromatography to predict the skin permeability of pharmaceutical and cosmetic compounds. Nine dissimilar stationary phases were applied to screen a test set of 58 compounds. The experimental retention factors (log k), in addition to two sets of theoretical molecular descriptors, were applied to model the skin permeability coefficient. Different modelling approaches, i.e. multiple linear regression (MLR) and partial least squares (PLS) regression, were used. In general, the MLR models performed better than the PLS models for a given descriptor set. The results obtained on a cyanopropyl (CN) column provided the best correlation with the skin permeability data. The retention factors obtained on this column were included in a simple MLR model, together with the octanol-water partition coefficient and the number of atoms (r² = 0.81, RMSEC = 0.537 or 20.5% and RMSECV = 0.580 or 22.1%). The overall best MLR model included the chromatographic descriptor from a phenyl column and 18 descriptors (r² = 0.98, RMSEC = 0.167 or 6.2% and RMSECV = 0.238 or 8.9%). This model showed a good fit, on top of very good predictive features. However, stepwise MLR models with a reduced complexity could also be determined, with the best performance parameters obtained with the CN-column based retention and eight descriptors (r² = 0.95, RMSEC = 0.282 or 10.7% and RMSECV = 0.353 or 13.4%). SFC thus provides a suitable alternative to the liquid chromatographic techniques previously applied to model the skin permeability.

Development of an adverse outcome pathway network for breast cancer: a comprehensive representation of the pathogenesis, complexity and diversity of the disease.

Adverse outcome pathways (AOPs), introduced in modern toxicology, intend to provide an evidence-based representation of toxicological effects and facilitate safety assessment of chemicals not solely based on laboratory animal in vivo experiments. However, some toxicological processes are too complicated to represent in one AOP. Therefore, AOP networks are developed that help understanding and predicting toxicological processes where complex exposure scenarios interact and lead to the emergence of the adverse outcome. In this study, we present an AOP network for breast cancer, developed after an in-depth survey of relevant scientific literature. Several molecular initiating events (MIE) were identified and various key events that link the MIEs with breast cancer were described. The AOP was developed according to Organization of Economic Co-Operation and Development (OECD) guidance, weight of evidence was assessed through the Bradford Hill criteria and confidence was tested by the OECD key questions. The AOP network provides a straightforward understanding of the disease onset and progression at different biological levels. It can be used to pinpoint knowledge gaps, identify novel therapeutic targets and act as a stepping stone for the development of novel in vitro test methods for hazard identification and risk assessment of newly developed chemicals and drugs.

Predicting skin permeability of pharmaceutical and cosmetic compounds using retention on octadecyl, cholesterol-bonded and immobilized artificial membrane columns.

In this study, the retention on three types of columns, an immobilized artificial membrane (IAM), a cholesterol-bonded and an octadecyl (C18) column, was applied for the prediction of skin permeability. The first two columns are biomimicking ones, which have certain components of the skin bound to the stationary phase, and were applied in HPLC, while the sub-2 µm C18 column was studied in UHPLC because of its fast features. Fifty-eight compounds were analyzed applying different mobile-phase compositions, with varying percentages of organic modifier on every column, to extrapolate the retention factor to a theoretically purely aqueous mobile phase (log kw). The retention factors, along with two sets of theoretical molecular descriptors, were used to model the skin permeability coefficient (log Kp) using multiple linear regression (MLR) and partial least squares (PLS) regression modelling. Although the retention factors (log k) on the IAM column showed a better correlation with the skin permeability, the overall best model was obtained by applying a stepwise MLR approach on the UHPLC parameters combined with some theoretical descriptors. This model showed a good fit, and on top has potential to accurately predict skin permeability values. Furthermore, the UHPLC method has the advantage of being fast and can thus be classified as a high-throughput approach.

Authentication of extra virgin Argan oil by selected-ion flow-tube mass-spectrometry fingerprinting and chemometrics.

Recognized for its nutritional and therapeutic use, extra-virgin Argan Oil (EVAO) is frequently adulterated. Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) spectra were applied to quantify adulterants (i.e., Argan oil of lower quality (LQAO), olive oil (OO), and sunflower oil (SO)) in EVAO. Four data sets, i.e., using H3O+, NO+, O2+ reagent ions, and the combined data were considered. Soft independent modelling of class analogy (SIMCA), and partial least squares discriminant analysis (PLS-DA) were assessed to distinguish adulterated- from pure EVAO. The effectiveness of SIFT-MS associated with PLS and support vector machine (SVM) regression to quantify trace adulterants in EVAO was evaluated. Variable Importance in Projection (VIP), and interval-PLS (iPLS) were also investigated to extract useful features. Different models were built to predict the EVAO authenticity and the degree of adulteration. High accuracy was achieved. SIFT-MS spectra handled with the appropriate chemometric tools were found suitable for the quality evaluation of EVAO.

Fabrication of a molecularly imprinted monolithic column via the epitope approach for the selective capillary microextraction of neuropeptides in human plasma.

A novel molecularly imprinted monolithic (MIM) column was designed and fabricated using the epitope approach, and was used for the selective capillary microextraction (CME) of the neuropeptides neurotensin (NT) and neuromedin N (NmN). The MIMs were synthesized in a capillary by thermally initiated polymerization of the functional monomer (methacrylic acid (MAA)), in the presence of a dummy template (Pro-Tyr-Ile-Leu (PYIL)), a crosslinker and porogens. The resulting monoliths were characterized by scanning electron microscopy, pore size distribution measurement, and Fourier transform infrared spectroscopy. Different synthesis conditions of the MIM column were investigated. The parameters affecting the MIM-CME performance, including loading, washing and elution protocols, were optimized as well. The MIMs were used to enrich NT and NmN from human plasma prior to HPLC-UV analysis. The imprinted monolith showed an excellent maximum adsorption capacity of 245-711 mg mL-1 and selectivity (imprinting factor of 5.7-13.4) towards its target peptides. Low detection limits of 0.62 and 1.20 nM, and satisfactory recoveries (82.5-98.8%) were obtained for NT and NmN, respectively. The proposed MIM-CME/HPLC-UV method was found suitable to be used as an effective tool for the highly efficient and specific analysis of NT and NmN in human plasma samples.

In vitro antimycobacterial activity of medicinal plants Lantana camara, Cryptolepis sanguinolenta, and Zanthoxylum leprieurii.

Background: Imperative need exists to search for new anti-TB drugs that are safer, and more effective against drug-resistant strains. Medicinal plants have been the source of active ingredients for drug development. However, the slow growth and biosafety level requirements of M. tuberculosis culture are considerable challenges. M. smegmatis can be used as a surrogate for M. tuberculosis. In the current study, preliminary phytochemical screening and antimycobacterial activity evaluation of crude methanolic extracts of medicinal plants against M. smegmatis, and two M. tuberculosis strains, were conducted. Materials and Methods: Crude methanolic extracts, obtained from the leaves of L. camara, roots of C. sanguinolenta, and stem barks of Z. leprieurii, were tested for antimycobacterial activity against M. smegmatis (mc2155), pan-sensitive (H37Rv), and rifampicin-resistant (TMC-331) M. tuberculosis, using visual Resazurin Microtiter Assay (REMA) on 96 well plates. Preliminary qualitative phytochemical screening tests were performed using standard chemical methods. Results: The three methanolic extracts inhibited mycobacterial growth in vitro. They were more active against rifampicin-resistant strain with MICs of 176, 97, and 45 µg/mL for L. camara, C. sanguinolenta, and Z. leprieurii extracts, respectively. The lowest activity was observed against M. smegmatis with MICs of 574, 325, and 520 µg/mL, respectively. Against H37Rv, activity was intermediate to those of TMC-331 and mc2155. However, L. camara extract showed the same activity against H37Rv and M. smegmatis. Preliminary phytochemical analysis revealed alkaloids, flavonoids, phenolic compounds, saponins, tannins, and terpenoids. Conclusions: Leaves of L. camara, roots of C. sanguinolenta, and stem barks of Z. leprieurii exhibit antimycobacterial activity against M. smegmatis, pan-sensitive, and rifampicin-resistant M. tuberculosis. This offers the possibilities for novel therapeutic opportunities against TB including multidrug-resistant TB. Further investigations on safety and mechanisms of action are required. These studies could be done using M. smegmatis as a surrogate for the highly pathogenic M. tuberculosis.

Coupling of chiral and achiral stationary phases in supercritical fluid chromatography: Evaluating and improving retention prediction.

Isomers and stereoisomers are always challenging to separate. Column coupling may provide improved chromatographic selectivity, necessary for the separation of the compounds with similar chemical and structural properties. The relatively low viscosity of supercritical fluids, used as mobile phases allows for the coupling of several columns in series in supercritical fluid chromatography (SFC), without exceeding the pressure limits of the system. The aim of this study is to propose reliable prediction of the retention behaviour of analytes on a coupled column system, based on a limited number of initial analyses. The chiral compounds atenolol, ephedrine, propranolol, mianserin, labetalol and nadolol, besides the diastereomers quinine and quinidine, and the structural isomers of aminophenol and aminocresol were used as model analytes. The retention behaviour of the analytes was determined on the individual chiral columns Lux Cellulose-1, Lux Cellulose-2, Lux Cellulose-3, Lux Cellulose-4, Lux Amylose-2 and the achiral columns Luna NH2, Luna Silica, Synergi RP and FluoroSep RP. The mobile phase was composed of CO2 mixed with 20% (v/v) MeOH, which contained 0.1% (v/v) trifluoroacetic acid and 0.1% (v/v) isopropylamine. The retention factors of the analytes on coupled stationary phases were predicted, and subsequently compared to the experimentally obtained ones. Relative deviations of predicted and experimental retention factors were in range from 0.00% to 51.91%. Flow rate and back pressure of the screening conditions were adjusted to improve prediction precision on four column combinations, with varying success rates. The average relative deviations of retention factors were reduced to 2.84% - 6.59% by adjusting flow rate, and to 2.30% - 8.57% by adjusting back pressure. The most successful approach, flow rate adjustment, was then applied to select a column combination providing improved resolution of the structurally similar components of silymarin extract.

Development of a capillary electrophoresis method for the separation of flavonolignans in silymarin complex.

CE method for the baseline separation of structurally similar flavonolignans silybin A, silybin B, isosilybin A, isosilybin B, silychristin, silydianin, and their precursor taxifolin in silymarin complex has been developed and validated. The optimized background electrolyte was 100 mmol/L boric acid (pH 9.0) containing 5 mmol/L heptakis(2,3,6-tri-O-methyl)-ß-CD and 10% (v/v) of methanol. The separation was carried out in an 80.5/72 cm (50 µm id) fused silica capillary at +25 kV with UV detection at 200 nm. Genistein (10 µg/mL) was used as internal standard. The resolution between the diastereomers of silybin and isosilybin was 1.73 and 2.59, respectively. The method was validated for each analyte in a concentration range of 2.5-50 µg/mL. The calibration curves were rectilinear with correlation coefficients ≥0.9972. The method was applied to determine flavonolignans in two dietary supplements containing Silybum marianum extract. The accuracy was evaluated by comparing the results of the CE analyses of the dietary supplements with those of the reference United States Pharmacopeial HPLC method. The unpaired t-test did not show a statistically significant difference between the results of both the proposed CE and the reference method (p > 0.05, n = 3).

Natural plant toxins in honey: An ignored threat to human health.

Consumers often believe that "natural food" is harmless, however naturally occurring toxins in food represent a health risk to humans. Honey as a natural, nutritious sweetener, is one of the most commonly consumed foods throughout the world. However, food safety concerns for honey arise when honeybees collect nectar from poisonous plants such as Rhododendron sp., Coriaria arborea, and Tripterygium wilfordii Hook F. Such honey contains natural plant toxins. Humans may develop intoxication symptoms after consuming toxic honey; in some cases, it can be fatal. As a result, toxic honey poses an often-ignored threat to public health. Typical plant toxins such as grayanotoxins, triptolides, tutin and pyrrolizidine alkaloids, have been identified in toxic honey. Although different toxic honeys elicit similar symptoms, such as vomiting, nausea, and dizziness, the mechanism of toxicity may be different. Thus, it is necessary to determine the exact toxicity mechanism of different toxins to further develop effective antidotes and cures. Another important challenge is preventing toxic honey from entering the food chain. Liquid chromatography-mass spectrometry has a wide range of applications in the detection of different toxins due to its accuracy and simplicity. More methods, however, are urgently needed to detect multiple plant-derived toxins in honey and its derivatives. Developing uniform international standards for toxin detection during quarantine using advanced techniques is critical for preventing human consumption of toxic honey.

Improved modelling for low-correlated multiple responses by common-subset-of-independent-variables partial-least-squares.

In this study, a new approach for PLS modelling for low-correlated multiple responses, called Common-Subset-of-Independent-Variables Partial-Least-Squares, denoted as CSIV-PLS1, is proposed and evaluated. In CSIV-PLS1, for each response vector, individual PLS1 models with individual model complexities are developed, based on one common set of independent variables, obtained after variable selection by the Final Complexity Adapted Models method, using the absolute values of the PLS regression coefficients, denoted as FCAM-REG. CSIV-PLS1 combines a common variable set for all response vectors, which is a characteristic of PLS2, with the individual model complexity for each response, which is a characteristic of PLS1. These characteristics make CSIV-PLS1 more flexible than PLS2. The selective and predictive abilities of the proposed CSIV-PLS1 method are investigated using one simulated and four real data sets with low-correlated multiple responses from different sources. The simulated data set is used to test the general applicability of the CSIV-PLS1 method. The predictive abilities, measured by the RMSEP values, resulting from CSIV-PLS1 models, are statistically compared with those of the corresponding PLS1 and PLS2 models, using one-tailed paired t-tests. The selective ability of the CSIV-PLS1 method is good, because mostly variables with an informative meaning to the responses are selected. The RMSEP values resulting from the CSIV-PLS1 method are (i) significantly lower at the 95% confidence level than those of the corresponding PLS2 method, and (ii) borderline significantly lower at the 90-95% confidence level than those of the corresponding PLS1 methods. In case of low-correlated multiple responses, the predictive ability of the CSIV-PLS1 method is significantly better than that of the PLS2 method, and borderline significantly better than those of the corresponding PLS1 methods. Therefore, CSIV-PLS1 modelling may be an alternative for PLS1 or PLS2.

Evaluating micellar liquid chromatographic methods on octadecyl particle-based and monolithic columns to predict the skin permeation of drug and cosmetic molecules.

A micellar liquid chromatographic method was developed to assist in the modeling of the skin permeability of pharmaceutical and cosmetic compounds. The composition of the mobile phase was determined by means of a two-factor central composite design, after which it was tested on both a particle-based and monolithic column. The latter provided the opportunity to increase the flow rate from 1 to 8 mL/min without reaching too high backpressures. The micellar conditions allowed analyzing a large test set of compounds with diverse characteristics with just one mobile-phase composition. The obtained experimental chromatographic descriptors besides two sets of theoretical molecular descriptors were used to model the skin permeability coefficient log Kp, applying multiple linear regression and partial least squares regression approaches. The micellar method on the monolithic column provided useful models with similar or even slightly better performance parameters than the method on the particle-based column. Furthermore, a much faster analysis can be achieved when applying a flow rate of 8 mL/min, making the micellar monolithic method ideal to estimate skin permeability.

Gas Chromatographic Fingerprint Analysis for the Comparison of Seized Cannabis Samples.

Cannabis sativa L. is widely used as recreational illegal drugs. Illicit Cannabis profiling, comparing seized samples, is challenging due to natural Cannabis heterogeneity. The aim of this study was to use GC-FID and GC-MS herbal fingerprints for intra (within)- and inter (between)-location variability evaluation. This study focused on finding an acceptable threshold to link seized samples. Through Pearson correlation-coefficient calculations between intra-location samples, 'linked' thresholds were derived using 95% and 99% confidence limits. False negative (FN) and false positive (FP) error rate calculations, aiming at obtaining the lowest possible FP value, were performed for different data pre-treatments. Fingerprint-alignment parameters were optimized using Automated Correlation-Optimized Warping (ACOW) or Design of Experiments (DoE), which presented similar results. Hence, ACOW data, as reference, showed 54% and 65% FP values (95 and 99% confidence, respectively). An additional fourth root normalization pre-treatment provided the best results for both the GC-FID and GC-MS datasets. For GC-FID, which showed the best improved FP error rate, 54 and 65% FP for the reference data decreased to 24 and 32%, respectively, after fourth root transformation. Cross-validation showed FP values similar as the entire calibration set, indicating the representativeness of the thresholds. A noteworthy improvement in discrimination between seized Cannabis samples could be concluded.

Cytotoxic, Antioxidant, and Antidiabetic Activities versus UPLC-ESI-QTOF-MS Chemical-Profile Analysis of Ipomoea aquatica Fractions.

Ipomoea aquatica is a common green leafy vegetable that has numerous uses in traditional medicine. This study focused on the determination of the cytotoxic, antiradical, and antidiabetic properties of various fractions of the I. aquatica methanolic extract, as well as on the tentative identification of some bioactive compounds in the same fractions. The cytotoxicity was determined by the brine shrimp lethal test. The antioxidant activities of the I. aquatica fractions were investigated through 3 assays. The antidiabetic activity (in vitro) was measured by α-glucosidase and α-amylase inhibition assays. Phytochemical qualitative analyses demonstrated the presence of alkaloids, terpenoids, phenols, and flavonoids in the ethyl acetate-methanol and methanol fractions. The total phenolic and total flavonoid contents were found to be highest in the ethyl acetate-MeOH fractions. The evaluation of the cytotoxicity showed that the hexane-dichloromethane fraction is the most toxic, while the others are moderately toxic. The antioxidant activity assays showed that the ethyl acetate-MeOH fractions are the most potent, while the α-glucosidase and α-amylase assays revealed that the hexane-dichloromethane fraction might contain a potent antidiabetic agent. Some bioactive substances in the MeOH fractions, such as salicylic acid glucoside, 1-O-sinapoyl-ß-D-glucose derivative, and dihydroferulic acid derivative, were tentatively identified. To the best of our knowledge, this is the first report to detect and identify these compounds in this species. Based on the results of this study, it may be concluded that I. aquatica is a potent antioxidant agent and could be a good candidate as a natural antioxidant in food and therapeutics.