Synchronous spectrofluorimetry and chemometric modeling: A synergistic approach for analyzing simeprevir and daclatasvir, with application to pharmacokinetics evaluation.

Simeprevir and daclatasvir represent a cornerstone in the management of Hepatitis C Virus infection, a global health concern that affects millions of people worldwide. In this study, we propose a synergistic approach combining synchronous spectrofluorimetry and chemometric modeling i.e. Partial Least Squares (PLS-1) for the analysis of simeprevir and daclatasvir in different matrices. Moreover, the study employs firefly algorithms to further optimize the chemometric models via selecting the most informative features thus improving the accuracy and robustness of the calibration models. The firefly algorithm was able to reduce the number of selected wavelengths to 47-44% for simeprevir and daclatasvir, respectively offering a fast and sensitive technique for the determination of simeprevir and daclatasvir. Validation results underscore the models' effectiveness, as evidenced by recovery rates close to 100% with relative root mean square error of prediction (RRMSEP) of 2.253 and 2.1381 for simeprevir and daclatasvir, respectively. Moreover, the proposed models have been applied to determine the pharmacokinetics of simeprevir and daclatasvir, providing valuable insights into their distribution and elimination patterns. Overall, the study demonstrates the effectiveness of synchronous spectrofluorimetry coupled with multivariate calibration optimized by firefly algorithms in accurately determining and quantifying simeprevir and daclatasvir in HCV antiviral treatment, offering potential applications in pharmaceutical formulation analysis and pharmacokinetic studies for these drugs.

Therapeutic implications of dapagliflozin on the metabolomics profile of diabetic rats: A GC-MS investigation coupled with multivariate analysis.

BACKGROUND: Diabetes mellitus is a complex metabolic disorder with systemic implications, necessitating the search for reliable biomarkers and therapeutic strategies. This study investigates the metabolomics profile alterations in diabetic rats, with a focus on the therapeutic effects of Dapagliflozin, a drug known to inhibit renal glucose reabsorption, using Gas Chromatography-Mass Spectrometry analysis. METHODS: A GC-MS based metabolomics approach combined with multivariate and univariate statistical analyses was utilized to study serum samples from a diabetic model of Wistar rats, treated with dapagliflozin. Metabolomics pathways analysis was also performed to identify the altered metabolic pathways associated with the disease and the intervention. RESULTS: Dapagliflozin treatment in diabetic rats resulted in normalized levels of metabolites associated with insulin resistance, notably branched-chain and aromatic amino acids. Improvements in glycine metabolism were observed, suggesting a modulatory role of the drug. Additionally, reduced palmitic acid levels indicated an alleviation of lipotoxic effects. The metabolic changes indicate a restorative effect of dapagliflozin on diabetes-induced metabolic perturbations. CONCLUSIONS: The comprehensive metabolomics analysis demonstrated the potential of GC-MS in revealing significant metabolic pathway alterations due to dapagliflozin treatment in diabetic model rats. The therapy induced normalization of key metabolic disturbances, providing insights that could advance personalized diabetes mellitus management and therapeutic monitoring, highlighting the utility of metabolomics in understanding drug mechanisms and effects.

Derivative spectroscopy and wavelet transform as green spectrophotometric methods for abacavir and lamivudine measurement.

Herein, two different sustainable and green signal processing spectrophotometric approaches, namely, derivative spectroscopy and wavelet transform, have been utilized for effective measurement of the antiretroviral therapy abacavir and lamivudine in their pharmaceutical formulations. These methods were used to enhance the spectral data and differentiate between the absorption bands of abacavir and lamivudine in order to accurately measure their concentrations. For determining abacavir and lamivudine, the first derivative spectrophotometric method has been applied to the zero-order and ratio spectra of both drugs. The same approach has been tested using the continuous wavelet transform method where a second order 2.4 of rbio and bior wavelet families were found to be optimum for measuring both drugs. Validation of the proposed methods affirmed their reliability in terms of linearity over the concentration range 1.5-30 µg/mL and 1.5-36 µg/mL for abacavir and lamivudine, respectively, precision (RSD < 2 %), and accuracy with mean recoveries ranging between 98 % and 102 %. Additionally, these spectrophotometric methodologies were applied to real pharmaceutical preparations and yielded results congruent with a prior chromatographic method. Most prominently, the proposed methods stood out for their greenness and sustainability with 97 points as evaluated by the analytical eco-scale method and a score value of 0.79 as analyzed by AGREE method, thereby making them suitable for resource-limited settings and highlighting the potential for broader application of green analytical methods in pharmaceutical analysis.

Efficient and eco-friendly detection of gabapentin using nitrogen-doped carbon quantum dots: an analytical and green chemistry approach.

This study presents the development of an eco-friendly and highly selective mitrogen-doped carbon quantum dot based sensor (N-CQDs) for the detection of gabapentin - a commonly misused drug. A detailed characterization of N-CQDs spectral features and their interaction with gabapentin is provided. The optimal conditions for sensing, including pH value, buffer volume, N-CQDs concentration, and incubation time, were established. The results showed excellent fluorescence quenching at 475 nm (λex = 380 nm) due to the dynamic quenching mechanism, and the sensor demonstrated excellent linearity in the 0.5-8.0 µg mL-1 concentration range with correlation coefficients of more than 0.999, a limit of detection (LOD) of 0.160 and limit of quantification (LOQ) of 0.480 µg mL-1. The accuracy of the proposed sensor was acceptable with a mean accuracy of 99.91 for gabapentin detection. In addition, precision values were within the acceptable range, with RSD% below 2% indicating good repeatability and reproducibility of the sensor. Selectivity was validated using common excipients and pooled plasma samples. The proposed sensor accurately estimated gabapentin concentration in commercial pharmaceutical formulations and spiked plasma samples, exhibiting excellent comparability with previously published methods. The 'greenness' of the sensing system was evaluated using the Analytical GREEnness calculator, revealing low environmental impact and strong alignment with green chemistry principles with a greenness score of 0.76. Thus, the developed N-CQDs-based sensor offers a promising, eco-friendly, and effective tool for gabapentin detection in various situations, ranging from clinical therapeutics to forensic science.

Augmented least squares, a powerful chemometric approach for the spectroscopic analysis of the antiretroviral therapy abacavir, lamivudine and dolutegravir in their ternary mixture.

Augmented least squares models such as concentration residual augmented classical least squares (CRACLS) and spectral residual augmented classical least squares (SRACLS) are powerful chemometric approaches that can be applied for spectroscopic analysis of many pharmaceutical compounds. Herein, both CRACLS and SRACL have been employed for UV spectral analysis of three antiretroviral therapy namely abacavir (ACV), lamivudine (LMV) and dolutegravir (DTG) in their ternary mixture. A partial factorial design has been utilized for calibration set construction then both CRACLS and SRACLS models have been optimized regarding the number of iterations and principal components, respectively, using a leave-one-out cross-validation procedure. It was found that a higher number of iterations and principal components were required for modelling the minor component DTG indicating more augmentation procedures to improve the models' accuracy. Validation of the proposed models was performed using external validation set of 13 mixtures and different validation parameters have been evaluated regarding models' predictive abilities. Both models showed excellent performance for analyzing ACV and LMV with relative root mean square error of prediction (RRMSEP) below 2 %. However, higher RRMSEP values around 5 % were observed for the minor component DTG suggesting that these models should be utilized with caution when analyzing minor components in mixtures. Furthermore, the suggested models have been applied for analyzing ACV, LMV and DTG in their pharmaceutical formulation and excellent agreement was observed between the suggested models and the reported chromatographic method posing these models as powerful chemometric approaches for quality control analysis of many pharmaceutical compounds.

Environmentally sustainable DRS-FTIR probe assisted by chemometric tools for quality control analysis of cinnarizine and piracetam having diverged concentration ranges: Validation, greenness, and whiteness studies.

A novel diffuse reflectance fourier transform infrared spectroscopic method accompanied by chemometrics was optimized to fulfill the white analytical chemistry and green analytical chemistry principles for the quantification of cinnarizine and piracetam for the first time without any prior separation in their challenging pharmaceutical preparation, which has a pretty substantial difference in the concentration of cinnarizine/piracetam (1:16). Furthermore, the suggested method was used for cinnarizine/piracetam dissolution testing as an effective alternative to traditional methods. For the cinnarizine/piracetam dissolution tests, we used a dissolution vessel with 900 mL of phosphate buffer pH 2.5 at 37 °C ± 0.5 °C, then the sampling was carried out by frequent withdrawal of 20 µl samples from the dissolution vessel at a one-minute interval, over one hour, then representative fourier transform infrared spectra were recorded. To create a partial-least-squares regression model, a fractional factorial design with 5 different levels and 2 factors was used. This led to the creation of 25 mixtures, 15 as a calibration set and 10 as a validation set, with varying concentration ranges: 1-75 and 16-1000 µg/mL for cinnarizine/piracetam, respectively. Upon optimization of the partial-least-squares regression model, in terms of latent variables and spectral region, root mean square error of cross-validation of 0.477 and 0.270, for cinnarizine/piracetam respectively, were obtained. The optimized partial-least-squares regression model was further validated, providing good results in terms of recovery% (around 98 to 102 %), root mean square error of prediction (0.436 and 3.329), relative root mean square error of prediction (1.210 and 1.245), bias-corrected mean square error of prediction (0.059 and 0.081), and limit of detection (0.125 and 2.786) for cinnarizine/piracetam respectively. Ultimately, the developed method was assessed for whiteness, greenness, and sustainability using five assessment tools. the developed method achieved a greener national environmental method index and complementary green analytical procedure index quadrants with higher eco-scale assessment scores (91), analytical greenness metric scores (0.87), and red-greenblue 12 algorithm scores (89.7) than the reported methods, showing high practical and environmental acceptance for quality control of cinnarizine/piracetam.

Metabolites Profiling and Bioassays Reveal Bassia indica Ethanol Extract Protective Effect against Stomach Ulcers Development via HMGB1/TLR-4/NF-κB Pathway.

Clinical manifestation of gastric ulcers is frequent, in addition to their costly drug regimens, warranting the development of novel drugs at lower costs. Although Bassia indica is well characterized for its anti-inflammatory and antioxidant potential, capacity of its ethanol extract (BIEE) to prevent stomach ulcers' progression has not been reported. A nuclear protein termed high-mobility group box 1 (HMGB1) plays a key role in the formation of stomach ulcers by triggering a number of inflammatory responses. The main purpose of the current investigation was to evaluate the in vivo anti-inflammatory and anti-ulcerogenic capabilities of BIEE against ethanol-induced gastric ulcers in rats via the HMGB1/TLR-4/NF-B signaling pathway. HMGB1 and Nuclear factor kappa (NF-B) expression, IL-1ß and Nrf2 contents showed an increase along with ulcer development, concurrent with an increase in immunohistochemical TLR-4 level. In contrast, pre-treatment with BIEE significantly reduced HMGB1 and Nuclear factor kappa (NF-B) expression levels, IL-1ß and Nrf2 contents and ulcer index value. Such protective action was further confirmed based on histological and immunohistochemical TLR-4 assays. Untargeted analysis via UPLC-ESI-Qtof-MS has allowed for the comprehensive characterization of 40 metabolites in BIEE mostly belonged to two main chemical classes, viz., flavonoids and lipids. These key metabolites, particularly flavonoids, suggesting a mediation for the anti-inflammatory and anti-ulcerogenic properties of BIEE, pose it as a promising natural drug regimen for treatment of stomach ulcers.

Decoding Metabolic Reprogramming in Plants under Pathogen Attacks, a Comprehensive Review of Emerging Metabolomics Technologies to Maximize Their Applications.

In their environment, plants interact with a multitude of living organisms and have to cope with a large variety of aggressions of biotic or abiotic origin. What has been known for several decades is that the extraordinary variety of chemical compounds the plants are capable of synthesizing may be estimated in the range of hundreds of thousands, but only a fraction has been fully characterized to be implicated in defense responses. Despite the vast importance of these metabolites for plants and also for human health, our knowledge about their biosynthetic pathways and functions is still fragmentary. Recent progress has been made particularly for the phenylpropanoids and oxylipids metabolism, which is more emphasized in this review. With an increasing interest in monitoring plant metabolic reprogramming, the development of advanced analysis methods should now follow. This review capitalizes on the advanced technologies used in metabolome mapping in planta, including different metabolomics approaches, imaging, flux analysis, and interpretation using bioinformatics tools. Advantages and limitations with regards to the application of each technique towards monitoring which metabolite class or type are highlighted, with special emphasis on the necessary future developments to better mirror such intricate metabolic interactions in planta.

Integrated comparative metabolite profiling via NMR and GC-MS analyses for tongkat ali (Eurycoma longifolia) fingerprinting and quality control analysis.

Tongkat ali commonly known as Malaysian Ginseng (Eurycoma longifolia) is a herbal root worldwide available in nutraceuticals, either as a crude powder or capsules blended with other herbal products. Herein, a multiplexed metabolomics approach based on nuclear magnetic resonance (NMR) and solid-phase microextraction combined with gas chromatography-mass spectrometry (SPME-GC-MS) was applied for authentic tongkat ali extract vs some commercial products quality control analysis. NMR metabolite fingerprinting identified 15 major metabolites mostly ascribed to sugars, organic and fatty acids in addition to quassinoids and cinnamates. Following that, multivariate analysis as the non-supervised principal component analysis (PCA) and supervised orthogonal partial least squares-discriminant analysis (OPLS-DA) were applied revealing that differences were related to fatty acids and 13,21-dihydroeurycomanone being more enriched in authentic root. SPME-GC-MS aroma profiling led to the identification of 59 volatiles belonging mainly to alcohols, aldehydes/furans and sesquiterpene hydrocarbons. Results revealed that aroma of commercial products showed relatively different profiles being rich in vanillin, maltol, and methyl octanoate. Whereas E-cinnamaldehyde, endo-borneol, terpinen-4-ol, and benzaldehyde were more associated to the authentic product. The present study shed the light for the potential of metabolomics in authentication and standardization of tongkat ali and identification of its true flavor composition.

QSRR modeling of the chromatographic retention behavior of some quinolone and sulfonamide antibacterial agents using firefly algorithm coupled to support vector machine.

Quinolone and sulfonamide are two classes of antibacterial agents with an opulent history of medicinal chemistry features that contribute to their bacterial spectrum, efficacy, pharmacokinetics, and adverse effect profiles. The urgent need for their use, combined with the escalating rate of their resistance, necessitates the development of suitable analytical methods that accelerate and facilitate their analysis. In this study, the advanced firefly algorithm (FFA) coupled with support vector regression (SVR) was used to select the most significant descriptors and to construct two quantitative structure-retention relationship (QSRR) models using a series of 11 selected quinolone and 13 sulfonamide drugs, respectively, to predict their retention behavior in HPLC. Precisely, the effect of the pH value and acetonitrile composition in the mobile phase on the retention behavior of quinolones and sulfonamides, respectively, were studied. The obtained QSRR models performed well in both internal and external validations, demonstrating their robustness and predictive ability. Y-randomization validation demonstrated that the obtained models did not result by statistical chance. Moreover, the obtained results shed the light on the molecular features that influence the retention behavior of these two classes under the current chromatographic conditions.

Innovative electrochemical electrode modified with Al2O3 nanoparticle decorated MWCNTs for ultra-trace determination of tamsulosin and solifenacin in human plasma and urine samples and their pharmaceutical dosage form.

A simple, cheap, sensitive, and time-saving square wave voltammetric (SWV) procedure using a carbon paste electrode modified with aluminum oxide nanoparticle decorated multi-walled carbon nanoparticles (Al2O3-NPs/MWCNTs/CPE) is presented for the ultra-sensitive determination of tamsulosin (TAM) and solifenacin (SOL), one of the most prescribed pharmaceutical combinations in urology. Characterization of the developed electrode was performed using scanning electron microscopy (SEM), X-ray diffraction (XRD) patterns, energy dispersive X-ray analysis (EDX), transmission electron microscopy (TEM) and FT-IR spectrophotometry. The voltammetric behavior of TAM/SOL was evaluated using Al2O3-NPs in different content and electrode compositions. The use of Al2O3 functionalized MWCNTs as a CPE modifier increased the process of electron transfer as well as improved the electrode active surface area therefore, ultra-sensitive results were acquired with a linear range of 10-100 and 12-125 ng ml-1 for TAM and SOL respectively, and a limit of the detection value of 2.69 and 3.25 ng ml-1 for TAM and SOL, respectively. Interestingly, the proposed method succeeded in quantifying TAM and SOL with acceptable percentage recoveries in dosage forms having diverged concentration ranges and in the biological fluids with very low peak plasma concentration (C max). Furthermore, the proposed method was validated, according to the ICH criteria, and shown to be accurate and reproducible.

A New Chemometrically Assisted UV Spectrophotometric Method for Simultaneous Determination of Tamsulosin and Dutasteride in Their Pharmaceutical Mixture.

BACKGROUND: Tamsulosin (TAM) and dutasteride (DUT) are ranked among the most frequently prescribed therapies in urology. Interestingly, studies have also been carried out on TAM/DUT in terms of their ability to protect against recent COVID-19. However, very few studies were reported for their simultaneous quantification in their combined dosage form and were mainly based on chromatographic analysis. Subsequently, it is very important to offer a simple, selective, sensitive, and rapid method for the quantification of TAM and DUT in their challenging dosage form. OBJECTIVE: In this study, a new chemometrically assisted ultraviolet (UV) spectrophotometric method has been presented for the quantification of TAM and DUT without any prior separation. METHOD: For the calibration set, a partial factorial experimental design was used, resulting in 25 mixtures with central levels of 20 and 25 µg/mL for TAM and DUT, respectively. In addition, to assess the predictive ability of the developed approaches, another central composite design of 13 samples was used as a validation set. Post-processing by chemometric analysis of the recorded zero-order UV spectra of these sets has been applied. These chemometric approaches include partial least-squares (PLS) and genetic algorithm (GA), as an effective variable selection technique, coupled with PLS. RESULTS: The models' validation criteria displayed excellent recoveries and lower errors of prediction. CONCLUSIONS: The proposed models were effectively used to determine TAM/DUT in their combined dosage form, and statistical comparison with the reported method revealed satisfactory results. HIGHLIGHTS: Overall, this work presents powerful simple, selective, sensitive, and precise methods for simultaneous quantification of TAM/DUT in their dosage form with satisfactory results. The predictive ability and accuracy of the developed methods offer the opportunity to be employed as a quality control technique for the routine analysis of TAM/DUT when chromatographic instruments are not available.

Comparative 16S Metabarcoding of Nile Tilapia Gut Microbiota from the Northern Lakes of Egypt.

Nile tilapia, Oreochromis niloticus, is the principal fish bred in Egypt. A pilot study was designed to analyze the bacterial composition of the Nile tilapia fish guts from two saltwater lakes in Northern Egypt. Fish samples were obtained from two Delta lakes: Manzala (ML) and Borollus (BL). DNA was extracted, and the bacterial communities in the stomach content were classified (down to the species level) using the 16S rRNA-based analysis. From the two metagenomics libraries in this study, 1,426,740 reads of the amplicon sequence corresponding to 508 total taxonomic operational units were recorded. The most prevalent bacterial phyla were Proteobacteria, Firmicutes, Actinobacteria, and Synergistetes in all samples. Some of the strains identified belong to classes of pathogenic zoonotic bacteria. A notable difference was observed between gut bacteria of Nile tilapia fish obtained from BL and ML. There is a remarkable indication that Nile tilapia fish living in BL is heavily burdened with pathogenic microbes most remarkably those involved with methylation of mercury and its accumulation in fish organs. These pathogenic microbes could have clinical implications and correlated with many diseases. This result was also consistent with the metagenomic data's functional prediction that indicated that Nile tilapia species harboring these two Egyptian northern lakes may be exposed to numerous anthropogenic pollutants. The findings show that the host environment has a significant impact on the composition of its microbiota. The first step towards exploring the better management of this profit-making fish is recognizing the structure of the microbiome.

Spectrophotometric Determination of Aspirin and Omeprazole in the Presence of Salicylic Acid as a Degradation Product: A Comparative Evaluation of Different Univariate/Multivariate Post Processing Algorithms.

BACKGROUND: A recent combination of aspirin (ASP) and omeprazole (OMP) has been presented in a fixed dosage form for the treatment of many cardiovascular diseases, particularly in patients with gastric diseases. However, ASP is very sensitive to degradation into salicylic acid (SAL) as its main degradation product. Hence, it is very important to develop methods for the determination of ASP and OMP in the presence of SAL. OBJECTIVE: In this study, UV spectrophotometry assisted by different univariate/multivariate post processing algorithms is presented for quantitative determination of ASP, OMP, and SAL without any prior separation. METHODS: The univariate/multivariate algorithms include double divisor ratio difference and double divisor mean centering as the univariate approaches while the multivariate methods include principal component regression (PCR) and partial least squares (PLS) models. Validation of the univariate methods was done according to International Conference on Harmonization guidelines, while the multivariate models were validated using an external validation set. RESULTS: The univariate algorithms displayed excellent regression and validation capabilities in terms of linearity, accuracy, precision, and selectivity. Regarding PCR and PLS, the number of latent variables were carefully optimized, and the model's validation criteria displayed excellent recoveries and lower errors of prediction. CONCLUSION: Our findings indicate that the developed methods were comparable to the reported chromatographic methods, but are simpler and have much shorter analysis times. HIGHLIGHTS: Overall, this report presents the first spectrophotometric methods applied for determination of possible combinations of ASP, OMP, and SAL, and poses these methods as valuable analytical tools for in-process testing and quality control analysis.

Analysis of the ternary antiretroviral therapy dolutegravir, lamivudine and abacavir using UV spectrophotometry and chemometric tools.

Herein, a simple spectrophotometric method coupled with chemometric techniques i.e. partial least square (PLS) and genetic algorithm (GA) were utilized for the simultaneous determination of the vital ternary antiretroviral therapy dolutegravir (DTG), lamivudine (LMV), and abacavir (ACV) in their combined dosage form. Calibration (25 samples) and validation (13 samples) sets were prepared for these drugs at different concentrations via implementing partial factorial experimental designs. The zero order UV spectra of calibration and validation sets were measured and then subjected for further chemometric analysis. Partial least squares with/without variable selection procedures i.e. genetic algorithm (GA) were utilized to untangle the UV spectral overlapping of these mixtures. Cross-validation and external validation methods were applied to compare the performance of these chemometric techniques in terms of accuracy and predictive abilities. It was found that six latent variables were optimum for modelling DTG, four latent variables for modelling LMV and three latent variables for modelling ACV. Although, good recoveries with prompt predictive ability were attained by these PLS, GA-PLS showed better analytical performance owing to its capability to remove redundant variables i.e. the number of absorbance variables have been reduced to about 21-29%. The proposed chemometric methods can be reliably applied for simultaneous determination of DTG, LMV, and ACV in their laboratory prepared mixtures and pharmaceutical preparation posing these chemometric methods as worthy and substantial analytical tools in in-process testing and quality control analysis of many antiretroviral pharmaceutical preparations.

Sweat metabolome and proteome: Recent trends in analytical advances and potential biological functions.

Metabolome and proteome profiling of biofluids, e.g., urine, plasma, has generated vast and ever-increasing amounts of knowledge over the last few decades. Paradoxically, omics analyses of sweat, one of the most readily available human biofluids, have lagged behind. This review capitalizes on the current knowledge and state of the art analytical advances of sweat metabolomics and proteomics. Moreover, current applications of sweat omics such as the discovery of disease biomarkers and monitoring athletic performance are also presented in this review. Another area of emerging knowledge that has been highlighted herein lies in the role of skin host-microbiome interactions in shaping the sweat metabolite-protein profiles. Discussion of future research directions describes the need to have a better grasp of sweat chemicals and to better understand how they function as aided by advances in omics tools. Overall, the role of sweat as an information-rich biofluid that could complement the exploration of the skin metabolome/proteome is emphasized.

Antiproliferative activity of Syzygium coriaceum, an endemic plant of Mauritius, with its UPLC-MS metabolite fingerprint: A mechanistic study.

Flowering plants from the Syzygium genus have long been used in different ethnomedicinal systems worldwide and have been under scrutiny for their biological activities. Syzygium coriaceum, an endemic plant of Mauritius has been poorly studied for its potential application against cancer. Herein, Syzygium coriaceum leaf extract has been investigated for its anticancer effect against hepatocellular carcinoma (HepG2) cells. The anticancer activity was assessed using cell proliferation assays, flow cytometry, JC-1 mitochondrial membrane potential assay, and the COMET assay. Un-targeted metabolite profiling via ultra-performance liquid chromatography coupled to high-resolution qTOF-MS (UPLC-MS) and aided by molecular networking was employed to identify the crude extract metabolites. S. coriaceum treatment induced a dose-dependent increase in lactate dehydrogenase leakage into the culture media, peaking up to 47% (p ≤ 0.0001), compared to untreated control. Moreover, at 40 µg/mL, S. coriaceum led to 88.1% (p ≤ 0.0001) drop in mitochondrial membrane potential and 5.7% (p ≤ 0.001) increased in the number of the cell population in G0/G1 phase as well as increased (p < 0.05) the proportion of cells undergoing apoptotic/necrotic cell death. More so, at 10 µg/mL, S. coriaceum induced DNA damage which was 19 folds (p < 0.001) higher than that of untreated control cells. Metabolite profiling indicated the presence of 65 metabolites, out of which 59 were identified. Tannins, flavonoids, nitrogenous compounds, and organic acids were the most predominant classes of compounds detected. Our findings showed that the presence of tannins and flavonoids in S. coriaceum leaf extract could account for the multiple mechanisms of actions underlying the antiproliferative effect against HepG2 cells.

An evaluation of different bio-inspired feature selection techniques on multivariate calibration models in spectroscopy.

Herein, two new swarm intelligence based algorithms namely; grey wolf optimization (GWO) and antlion optimization (ALO) algorithms were presented, for the first time, as variable selection tools in spectroscopic data analysis. In order to assess the performance of these algorithms, they were applied along with the recently introduced firefly algorithm (FFA) and the well-established genetic algorithm (GA) and particle swarm optimization (PSO) algorithm on four different spectroscopic datasets of varying sizes and nature (UV and IR). Partial least squares (PLS) regression models were built using the selected variables by these algorithms along with the full spectral data as the reference models. The obtained results prove that the ALO and GWO optimization algorithms select variables in most cases less than GA and PSO while keeping the PLS performance almost the same. Accordingly, these algorithms can be successfully used for variable selection in spectroscopic data analysis.

Metabolomic profiling to reveal the therapeutic potency of Posidonia oceanica nanoparticles in diabetic rats.

Posidonia oceanica is a sea grass belonging to the family Posidoniaceae, which stands out as a substantial reservoir of bioactive compounds. In this study, the secondary metabolites of the P. oceanica rhizome were annotated using UPLC-HRESI-MS/MS, revealing 86 compounds including simple phenolic acids, flavonoids, and their sulphated conjugates. Moreover, the P. oceanica butanol extract exhibited substantial antioxidant and antidiabetic effects in vitro. Thus, a reliable, robust drug delivery system was developed through the encapsulation of P. oceanica extract in gelatin nanoparticles to protect active constituents, control their release and enhance their therapeutic activity. To confirm these achievements, untargeted GC-MS metabolomics analysis together with biochemical evaluation was employed to investigate the in vivo anti-diabetic potential of the P. oceanica nano-extract. The results of this study demonstrated that the P. oceanica gelatin nanoparticle formulation reduced the serum fasting blood glucose level significantly (p < 0.05) in addition to improving the insulin level, together with the elevation of glucose transporter 4 levels. Besides, multivariate/univariate analyses of the GC-MS metabolomic dataset revealed several dysregulated metabolites in diabetic rats, which were restored to normalized levels after treatment with the P. oceanica gelatin nanoparticle formulation. These metabolites mainly originate from the metabolism of amino acids, fatty acids and carbohydrates, indicating that this type of delivery was more effective than the plain extract in regulating these altered metabolic processes. Overall, this study provides novel insight for the potential of P. oceanica butanol extract encapsulated in gelatin nanoparticles as a promising and effective antidiabetic therapy.

Novel trends and applications of multidimensional chromatography in the analysis of food, cosmetics and medicine bearing essential oils.

The analysis of aroma composition in scented plants and natural products to probe their quality and safety is an important topic, particularly when such ingredients are intended for long term use as in food or cosmetic products. The development of fast, comprehensive, and effective analytical tools for essential oil analysis in such complex mixtures is of interest and moreover when present in blend of several oils, as typical in case of nutraceuticals or cosmetics. Comprehensive studies of volatiles could be achieved via coupling the separation power of multidimensional chromatography with selective detectors as mass spectrometers. This strategy enables high-throughput and global analysis of hundreds of metabolites in a single step. Different multidimensional setups such as GC × GC and LC-GC in addition to various chemometric approaches applied for essential oils analysis form a fundamental part of this review. Asides, applications of multidimensional chromatography for essential oils chemotyping, enantio-separation, quality control and adulteration detection in the different matrices are also presented.