Orthogonal Partial Least Squares Research Articles

Assessment of volatile compounds as potential markers of water deficit stress of two wild ecotypes of Salvia reuterana Boiss

Overall 90 volatile compoundswere recognized in well-watered and mild drought-stressed samples of two wild Iranian ecotypes of S. reuterana Boiss., namely S. reuterana A and S. reuterana B, and of S. officinalis L. (control) during the main phenological stages (vegetative, pre-flowering and flowering). In water-stressed S. officinalis the total level of volatiles increased (50,03 %) at the vegetative stage, while it decreased (-11,11 %) at the flowering stage. In drought-stressed S. reuterana A, volatiles amount decreased both at vegetative (-74,62 %) and pre-flowering (-31,34 %) stages, whereas control and water-stressed S. reuterana B showed similar volatile levels in all the stages. The Orthogonal Partial Least-Squares Discriminant Analysis (OPLS-DA) revealed that, excluding S. officinalis at the flowering stage, control and drought-stressed samples can be differentiated based on their volatile contents and despite the phenological stage. Moreover, OPLS-DA indicated that linalool and p-methylanisole can be considered putative biomarkers of the water deficit conditions in S. officinalis and S. reuterana A. Finally, as, when cultivated, S. reuterana A and S. officinalis seemed to show similar volatiles patterns, the cultivation of the Iranian ecotype A could provide a valuable source of natural antioxidants for the preparation of novel functional foods, pharmaceutical and cosmetics ingredients.

A novel NIRS modelling method with OPLS-SPA and MIX-PLS for timber evaluation

The identification of timber properties is important for safe application. Near Infrared Spectroscopy (NIRS) technology is widely-used because of its simplicity, efficiency, and positive environmental attributes. However, in its application, weak signals are extracted from complex, overlapping and changing information. This study focused on the stability of NIR modeling. The Orthogonal Partial Least Squares(OPLS) and Successive Projections Algorithm (SPA) eliminates noise and extracts effective spectra, and an ensemble learning method MIX-PLS, is applied to establish the model. The elastic modulus of timber is taken as an example, and 201 wood samples of three species, Xylosma-congesta (Lour.) Merr., Acer pictum subsp. mono, and Betula pendula, samples were divided into three groups to investigate modelling performance. The results show that OPLS can preprocess the near-infrared spectroscopy information according to the target object in the face of the system error and reduce errors to minimum. SPA finally selects 13 spectral bands, simplifies the NIR spectral data and improves model accuracy.The Pearson's correlation coefficient of Calibration (Rc) and the Pearson's correlation coefficient of Prediction (Rp) of Mix Partial Least Squares (MIX-PLS) were 0.95 and 0.90, and Root Mean Square Error of Calibration (RMSEC) and Root Mean Square Error of Prediction (RMSEP) are 2.075 and 6.001, respectively, which shows the model has good generalization abilities.

HPTLC-Based Chemical Profiling: An Approach to Monitor Plant Metabolic Expansion Caused by Fungal Endophytes.

Fungal endophytes isolated from two latex bearing species were chosen as models to show their potential to expand their host plant chemical diversity. Thirty-three strains were isolated from Alstonia scholaris (Apocynaceae) and Euphorbia myrsinites (Euphorbiaceae). High performance thin layer chromatography (HPTLC) was used to metabolically profile samples. The selected strains were well clustered in three major groups by hierarchical clustering analysis (HCA) of the HPTLC data, and the chemical profiles were strongly correlated with the strains’ colony size. This correlation was confirmed by orthogonal partial least squares (OPLS) modeling using colony size as “Y” variable. Based on the multivariate data analysis of the HPTLC data, the fastest growing strains of each cluster were selected and used for subsequent experiments: co-culturing to investigate interactions between endophytes-phytopathogens, and biotransformation of plant metabolites by endophytes. The strains exhibited a high capacity to fight against fungal pathogens. Moreover, there was an increase in the antifungal activity after being fed with host-plant metabolites. These results suggest that endophytes play a role in plant defense mechanisms either directly or by biotransformation/induction of metabolites. Regarding HPTLC-based metabolomics, it has proved to be a robust approach to monitor the interactions among fungal endophytes, the host plant and potential phytopathogens.

Holistic analysis and prediction of life cycle cost for vertical greenery systems in Singapore

Recent decades have witnessed the emerging and development of vertical greenery systems (VGS) or green walls as an alternative to enhance the quality of urban environments, especially for high-density cities. Although previous studies have calculated the life cycle cost (LCC) of VGS, the conventional method is time-consuming and requires a large amount of detailed data, which is unable and unpractical to obtain LCC of new VGS projects by collecting data of only several key variables of VGS. Hence, after computing and analyzing the LCC of 40 cases from different projects with various types, sizes, and other attributes, our study established a prediction model for the LCC of VGS using the data from forty cases of three types of VGS. Multiple linear regression (MLR), MLR with stepwise regression, and orthogonal partial least square (OPLS) models were built and compared. And the OPLS model was selected as the prediction model as it has relatively good prediction performance and can avoid the collinearity issue encountered by the other two methods. Furthermore, the combined OPLS model for three types of VGS was separated into three individual models with improved total prediction accuracy (85.4%–95.5%). In addition, key variables affecting the LCC of different types of VGS were also identified including salary, plant density, plant price, etc. Applications of LCC prediction models for VGS for various stakeholders were discussed to conclude the study.

Identification of inhibitors of pinellic acid generation in whole wheat bread

Bitterness is a common aversive flavor attribute of foods associated with low consumer acceptance. Untargeted LC–MS flavoromic profiling was utilized to identify endogenous compounds that influence the generation of the bitter compound 9,12,13-trihydroxy-trans-10-octadecenoic acid (pinellic acid) during bread making. A diverse sample set of wheat germplasm was chemically profiled. The corresponding pinellic acid concentrations after dough formation were modeled by orthogonal partial least squares (OPLS) with good fit (R2Y = 0.8) and predictive ability (Q2 = 0.6). The most predictive feature (negatively correlated), postulated to interfere with the biosynthetic pathway, was identified as schaftoside, an apigenin di-C-glycoside. Recombination experiments involving the addition of schaftoside to flour prior to breadmaking resulted in a 26% decrease in pinellic acid formation and significantly lower perceived bitterness intensity in whole wheat bread. This work provides novel understanding of bitter generation pathways in wheat products and new strategies to improve flavor profiles and consumer acceptability.

Identification of coffee compounds that suppress bitterness of brew

Untargeted LC–MS flavoromic profiling was utilized to identify compounds that suppress bitterness perception of coffee brew. The chemical profiles of fourteen brew samples and corresponding perceived bitterness intensities determined by descriptive sensory analysis were modeled by orthogonal partial least squares (OPLS) with good fit (R2Y > 0.9) and predictive ability (Q2 > 0.9). Ten chemical markers that were highly predictive and negatively correlated to bitter intensity were subsequently purified by multi-dimensional preparative LC–MS to conduct sensory recombination testing and/or confirm compound identifications by NMR. Three of the ten compounds evaluated, namely 4-caffeoylquinic acid, 5-caffeoylquinic acid, and 2-O-β-d-glucopyranosyl-atractyligenin were identified as bitter modulators in coffee, and significantly decreased the perceived bitterness intensity of the brew.

Metabolomics-based profiling with chemometric approach to identify bioactive compounds in Salacca zalacca fruits extracts and in silico molecular docking

Salak (Salacca zalacca) is well-known as snake fruit and it is immensely studied for its antioxidative and antidiabetic active metabolites throughout the southeast Asian countries. However, there are many remaining unidentified metabolites due to very low abundance and natural variation, which need to be further explored. Nowadays mass spectrometry (MS/MS) facilitates the tentative identification of unknown compounds in the crude herbal extracts. This study described the metabolite profiling of hydroalcoholic extracts of S. zalaccaanalysed by LC-QTOF-MS/MS. The 60% ethanolic extract exhibited the highest α-glucosidase inhibition and ferric reducing antioxidant power activities with IC50 of 15.94 µg/mL and 78.13 μg AAE/g, respectively. Multivariate data analysis (MVDA) by an orthogonal partial least-squares (OPLS) algorithm was conducted to correlate the α-glucosidase inhibition activity with the LC- QTOF-MS data. A total of 4 compounds were reported for first time in this fruit and identified based on the molecular mass and fragment ions. LC-QTOF-MS analysis indicated the presence of carexane I, 5-phenoxytetrazol-1-yl)-2,3,5,6-hexahydrofurofuran-3-ethylurea, 3-acetylphenoxy)-N-[(2)-1-amino-4-methyl-1-oxopentan-2-yl]-4,5-dihydroxycyclohexene-1-carboxamide and Ethyl 4-[5-methyl-2-oxo-1′,2′,5′,6′,7′,7′a-hexahydro-1H-spiro[indole-3,3′-pyrrolizine]-2′-ylamido] benzoate. Molecular docking of those compounds with the α–glucosidase enzyme was performed to confirm their antidiabetic potential. These bioactive compounds could be suggested as α-glucosidase inhibitors and functional food additives.

Providing a view for toxicity mechanism of tetracycline by analysis of the connections between metabolites and biologic endpoints of wheat

Metabolomics is an implement for testing the toxicity of antibiotics, and provides a comprehensive view of the overall response to stress; however, the connections between metabolites and biologic endpoints keep unclear in response to antibiotics. In this study, wheat seeds were exposed to tetracycline for 5 days. The results proved that tetracycline restrained growth, reduced chlorophyl and carotinoid contents and cell permeability, and increased reactive oxygen species (ROS) levels and malondialdehyde (MDA) content. Orthogonal partial least squares (OPLS) was used to analyze the connections between metabolites and biologic endpoints, which discovered that 11 metabolic pathways were significantly affected by tetracycline, and amino acid metabolism could largely apply to root growth and ROS accumulation, while carbohydrate metabolism could have a ruling effect on tetracycline-induced cell permeability. 13 metabolites all played active roles in mediating tetracycline’s effects on root length, root fresh weight and cell permeability but had no significant effects on ROS levels. The majority of metabolites with passive effects on root length, root fresh weight and cell permeability had active effects on ROS levels. These results offer a view about stress reaction of wheat to tetracycline.

Gas Chromatography-Mass Spectrometry Coupled with Multivariate Statistical Analysis to Identify the Alpha Glucosidase Inhibitors from Flesh of Salacca zalacca Fruits and Their Molecular Docking Studies

Fruit of salak (Salaaca zalacca) is traditionally used and commercialized as an antidiabetic agent. However, scientific evidence to prove this folk claim is quite lacking. Therefore, this research was aimed to evaluate the α-glucosidase inhibition activity of S. zalacca fruit and identify the bioactive compounds. The fruits were extracted by different ratios of ethanol and water (0, 20, 40, 60, 80, 100%, v/v) to get E0 (100% water), E20 (20% ethanol), E40 (40% ethanol), E60 (60% ethanol), E80 (80% ethanol), and E100 (100% ethanol) extracts. The extracts obtained were subjected to the α-glucosidase inhibitory assay. Gas chromatography-mass spectrometry- (GC-MS-) based metabolomics approach was used in profiling the bioactive metabolites present in the extracts. Orthogonal partial least square (OPLS) was used to correlate GC-MS data and α-glucosidase assay results to identify the possible chemical markers. All active compounds identified were subjected to molecular docking. The extracts from the S. zalacca fruit showed potent inhibition activity against α-glucosidase. The IC50 values from the α-glucosidase inhibitory assay ranged between 16 and 275 µg/ml. Overall, E60 displayed significantly higher α-glucosidase inhibition activity, while E0 showed the lowest α-glucosidase inhibition activity. Major compounds detected in S. zalacca fruits were sugars, fatty acids, and sterols, including myo-inositol, palmitic acid, stearic acid, and β-sitosterol. Moreover, the results obtained from molecular docking indicated that palmitic acid and β-sitosterol were close to the active side of the enzyme. Some of the residues that interacted include HID295, ASN259, LEU313, LYS125, PHE159, VAL216, PHE178, TYR72, TYR158, HIE315, ARG315, and PHE303. The bioassay result strongly suggests that E60 extract from S. zalacca fruits has potential α-glucosidase inhibitory activity. The hydrophobic compounds, including palmitic acid and β-sitosterol, were found to induce the α-glucosidase inhibition activity.

Feature selection using distributions of orthogonal PLS regression vectors in spectral data

Feature selection, which is important for successful analysis of chemometric data, aims to produce parsimonious and predictive models. Partial least squares (PLS) regression is one of the main methods in chemometrics for analyzing multivariate data with input X and response Y by modeling the covariance structure in the X and Y spaces. Recently, orthogonal projections to latent structures (OPLS) has been widely used in processing multivariate data because OPLS improves the interpretability of PLS models by removing systematic variation in the X space not correlated to Y. The purpose of this paper is to present a feature selection method of multivariate data through orthogonal PLS regression (OPLSR), which combines orthogonal signal correction with PLS. The presented method generates empirical distributions of features effects upon Y in OPLSR vectors via permutation tests and examines the significance of the effects of the input features on Y. We show the performance of the proposed method using a simulation study in which a three-layer network structure exists in compared with the false discovery rate method. To demonstrate this method, we apply it to both real-life NIR spectra data and mass spectrometry data.

The comprehensive quality evaluation of scutellariae radix based on HPLC fingerprint and antibacterial activity

Scutellaria Radix, a traditional Chinese medicine, studies on its main active ingredient are limited. In this study, the purpose was to investigate the quality difference of Scutellariae Radix from different origins based on chemical components and biological activities. The chromatographic fingerprints of Scutellariae Radix from 33 origins were established using HPLC, and the antibacterial activities were studied with the microdilution method. Moreover, orthogonal partial least-square regression, pearson correlational analysis and grey relational analysis methods were performed to explore the relationship between the compositions and bioactivities. In addition, and origin identification model was established to comprehensively evaluate the quality of Scutellariae Radix. The results showed that Scutellariae Radix had in-vitro antibacterial effect on Staphylococcus aureus, and the best were in Gansu, Shandong Province. Multivariate statistical analysis common showed that three components were positively correlated with antibacterial activity, which were respectively wogonin, baicalein and oroxylin. In conclusion, the quality of Scutellariae Radix varies greatly from different origins, and the better was in Gansu and Shandong Province. This work successfully provides a general model that combined the chromatographic fingerprint and bioactivity assay to study the spectrum–effect relationships, which could be used to discover the primary active ingredients in traditional Chinese medicines.

Estimation of Early Postmortem Interval of Asphyxial Death Rats at Different Ambient Temperatures by GC-MS-Based Metabolomics

Objective To establish the orthogonal partial least square （OPLS） model for the estimation of early postmortem interval （PMI） of asphyxial death rats in four ambient temperatures based on gas chromatography-mass spectrometry （GC-MS） metabolomics. Methods The 96 rats were divided into four temperature groups （5 ℃, 15 ℃, 25 ℃ and 35 ℃）. Each temperature group was further divided into 3 h, 6 h, 12 h and 24 h after death, and 6 other rats were taken as the control group. The cardiac blood was collected at the set time points for the four temperature groups and 0 h after death for the control group for the metabolomics analysis by GC-MS. By OPLS analysis, the variable importance in projection （VIP）>1 and the result of Kruskal-Wallis test P<0.001 were used to screen out the differential metabolite related to PMIs in the cardiac blood of rats of different temperature groups. Then OPLS regression models of different temperature groups were established with these metabolites. At the same time, a prediction group for investigating the prediction ability of these models was set up. Results Through the analysis of OPLS, 18, 15, 24 and 30 differential metabolites （including organic acids, amino acids, sugars and lipids） were screened out from the rats in groups of 5 ℃, 15 ℃, 25 ℃ and 35 ℃, respectively. The prediction results of the four temperature group models showed that the prediction deviation of 5 ℃ model was larger than that of other groups. The prediction results of other temperature groups were satisfactory. Conclusion There are some differences in the changes of metabolites in cardiac blood of rats at different ambient temperatures. The influence of ambient temperature should be investigated in the study of PMI estimation by metabolomics, which may improve the accuracy of PMI estimation.

An in situ Raman spectroscopic method for quantification of polyethylene glycol (PEG) in waterlogged archaeological wood

AbstractThe weakened microstructure of archaeological wood (AW) objects from waterlogged environments necessitates consolidation to avoid anisotropic shrinkage upon drying. Polymer impregnation through submergence or spraying treatments is commonly applied, and for larger and thicker objects, the impregnation period can stretch over decades. Thus, for efficient treatment, continuous monitoring of the impregnation status is required. Today, such monitoring is often destructive and expensive, requiring segments for extraction and chromatographic quantification. This study proposes an in situ Raman spectroscopic method for quantification of polyethylene glycol (PEG) in waterlogged AW. A calibration model was built on standards of PEG, cellulose powder, and milled wood lignin using orthogonal partial least squares (OPLS). The OPLS model had a strong linear relationship, and the PEG content in wood of varying degrees of degradation could be determined. However, the accuracy of the model was low with a root mean square error of prediction of 11 wt%. The low accuracy was traced to the heterogeneity in the calibration and validation set samples with regard to the small probing volume of the confocal instrumental setup.

Exploring multivariate linear regression methods for the prediction of total phenolic content in standard American lager beers using synchronous fluorescence spectroscopy fused data

This work aims to explore chemometric methods for determining the total phenolic content in Brazilian standard American lager beers using synchronous fluorescence spectroscopy fused data. Two hundred and sixteen (216) beer samples were diluted at a ratio of 1:5 (v/v), sonicated for 15 ​min in a water bath, and buffered using an acetic acid/acetate buffer of pH 4.5. The synchronous fluorescence spectra were obtained from 200 to 600 ​nm with a resolution of 1 ​nm. However, different offsets were tested for synchronous fluorescence (Δλ ​= ​20, 50, and 80). All measurements were carried out in triplicate, and the Folin-Ciocalteu method was used as the reference method for the total phenolic content. All chemometric methods were explored using R software and its graphic interface, RStudio. First, the spectral data with different offsets were normalized and fused. After this, the fused data were smoothed with the Savitzky-Golay algorithm with a window of length 11 and a quadratic polynomial, followed by data mean centering. Robust principal component analysis (ROBPCA) was used to identify anomalous data. The Kennard-Stone algorithm was used to split the dataset into 75% for the training set and 25% for the test set. The prediction models used were partial least squares (PLS), orthogonal partial least squares (O-PLS), and elastic net regressions with cross-validation (CV). Ensemble PLS and ensemble sparse PLS with bootstrap resampling were also used. Other PLS regression methods based on the selection of variables were tested, such as significance multivariate regression (sMC – PLS), uninformative variable elimination (UVE – PLS), and interval PLS (iPLS) global and optimized models with cross-validation. All the regression models were built with the training set. The validation was carried out with the application of the regression models to the test set. Within this context, two multivariate regression were highlighted, the CV elastic net and iPLS optimized models, with the root mean square error of prediction (RMSEP) equaling 1.0 and 1.1 ​mg GAE L−1, respectively.

The anti-neuroinflammatory effects of Clinacanthus nutans leaf extract on metabolism elucidated through 1H NMR in correlation with cytokines microarray.

Clinacanthus nutans (CN) (Acanthaceae) is well-known for its anti-inflammatory properties among Asian communities; however, there are currently no data specifically focused on the anti-inflammatory effects of CN on the brain tissue. Neuroinflammation is a common consequence of toxin intrusion to any part of the central nervous system (CNS). As an innate immune response, the CNS may react through both protective and/or toxic actions due to the activation of neuron cells producing pro- and/or anti-inflammatory cytokines in the brain. The unresolved activation of the inflammatory cytokines’ response is associated with the pathogenesis of neurological disorders. The present study aimed to decipher the metabolic mechanism on the effects of 14 days oral treatment with CN aqueous extract in induced-lipopolysaccharides (LPS) rats through 1H NMR spectroscopic biomarker profiling of the brain tissue and the related cytokines. Based on the principal component analysis (PCA) of the nuclear magnetic resonance (NMR) spectral data, twenty-one metabolites in the brain tissue were profiled as biomarkers for the LPS (10 μL)-induced neuroinflammation following intracerebroventricular injection. Among the twenty-one biomarkers in the neuroinflammed rats, CN treatment of 1000 and 500 mg/kg BW successfully altered lactate, pyruvate, phosphorylcholine, glutamine, and α-ketoglutarate when compared to the negative control. Likewise, statistical isolinear multiple component analysis (SIMCA) showed that treatments by CN and the positive control drug, dextromethorphan (DXM, 5 mg/kg BW), have anti-neuroinflammatory potential. A moderate correlation, in the orthogonal partial least squares (OPLS) regression model, was found between the spectral metabolite profile and the cytokine levels. The current study revealed the existence of high levels of pro-inflammatory cytokines, namely IL-1α, IL-1β, and TNF-α in LPS-induced rats. Both CN dose treatments lowered IL-1β significantly better than DXM Interestingly, DXM and CN treatments both exhibited the upregulation of the anti-inflammatory cytokines IL-2 and 4. However, DXM has an advantage over CN in that the former also increased the expression of IL-10 of anti-inflammatory cytokines. In this study, a metabolomics approach was successfully applied to discover the mechanistic role of CN in controlling the neuroinflammatory conditions through the modulation of complex metabolite interactions in the rat brain.

Alpha-Glucosidase Inhibitory Effect of Psychotria malayana Jack Leaf: A Rapid Analysis Using Infrared Fingerprinting.

The plant Psychotria malayana Jack belongs to the Rubiaceae family and is known in Malaysia as “meroyan sakat/salung”. A rapid analytical technique to facilitate the evaluation of the P. malayana leaves’ quality has not been well-established yet. This work aimed therefore to develop a validated analytical technique in order to predict the alpha-glucosidase inhibitory action (AGI) of P. malayana leaves, applying a Fourier Transform Infrared Spectroscopy (FTIR) fingerprint and utilizing an orthogonal partial least square (OPLS). The dried leaf extracts were prepared by sonication of different ratios of methanol-water solvent (0, 25, 50, 75, and 100% v/v) prior to the assessment of alpha-glucosidase inhibition (AGI) and the following infrared spectroscopy. The correlation between the biological activity and the spectral data was evaluated using multivariate data analysis (MVDA). The 100% methanol extract possessed the highest inhibitory activity against the alpha-glucosidase (IC50 2.83 ± 0.32 μg/mL). Different bioactive functional groups, including hydroxyl (O-H), alkenyl (C=C), methylene (C-H), carbonyl (C=O), and secondary amine (N-H) groups, were detected by the multivariate analysis. These functional groups actively induced the alpha-glucosidase inhibition effect. This finding demonstrated the spectrum profile of the FTIR for the natural herb P. malayana Jack, further confirming its medicinal value. The developed validated model can be used to predict the AGI of P. malayana, which will be useful as a tool in the plant’s quality control.

Understanding performance of 3D-printed sorbent in study of metabolic stability.

Metabolic stability tests are one of the fundamental steps at the preclinical stages of new drug development. Microsomes, used as a typical enzymatic model of liver biotransformation, can be a challenging matrix for analytical scientists due to a high concentration of cellular proteins and membrane lipids. In the work, we propose a new procedure integrating biotransformation reaction with SPME-like protocol for sample clean-up. It is beneficial to increase the overall quality of results in contrary to the typical protein precipitation approach. A set of ten arylpiperazine analogs, six of which are considered promising drug candidates (and four are accepted drugs) were used as a probe to assess the goodness of the newly proposed approach. In order to promote an efficient extraction protocol, a new, miniaturized shape of a sorbent, suitable to perform the extraction in 100 µL of the sample has been designed. Termination of the biotransformation process by protein denaturation with hot water was additionally evaluated. A quantitative structure-property relationship (QSPR) study using Orthogonal Partial Least Squares (OPLS) technique to reveal insights to the sorption mechanism was also performed. The obtained results showed the new 3D-printed sorbent can be an attractive basis for the new sample preparation approach for metabolic stability studies and an alternative for commercially available protocols based on solid-phase microextraction (SPME) or solid-phase extraction (SPE) principles.

Method development for in situ study of marine vanadium peroxidase based on SERS and chemometrics

AbstractVanadium peroxidases from marine algae are responsible for the production of ozone‐depleting compounds, volatile halogenated organic compounds (VHOC). Due to the impact the release of these compounds has on the global atmospheric and biogeochemical processes, there is an interest within marine sciences in developing analytical methods for studying the various aspects of the VHOC production, particularly in situ. This study aimed to provide new methods towards the development of in situ methods within marine sciences.We demonstrate the use of design of experiments together with orthogonal partial least squares (OPLS) and transposed orthogonal partial least squares (T‐OPLS) to address the qualitative spectral analysis of an enzyme‐buffer system. The measurements were performed with surface‐enhanced Raman spectroscopy (SERS) on vanadium bromoperoxisase from the red algae Corallina officinalis. The chemometric tools used aimed to provide greater insights into how factors such as time, amount of gold nanoparticles and enzyme concentration influence the spectral responses and whether there was any synergy between those factors. The results acquired in this report aim to support future method development of chemometrics within in situ applications in marine sciences.

Drug affinity to human serum albumin prediction by retention of cetyltrimethylammonium bromide pseudostationary phase in micellar electrokinetic chromatography and chemically advanced template search descriptors

The development of high-throughput methods for the estimation of physicochemical and biological properties of drug candidates is highly desired in the pharmaceutical landscape. Affinity to plasma protein is one of the most important biological properties, which should be taken under concern during the design and assessment of future potential medicines. The main goal of this study was to develop a quantitative retention-activity relationship model, with rationalized in vivo and in silico approach to predict the affinity to human serum albumin (HSA), which is one of the most important plasma proteins. To achieve this goal, a set of 27 chemically diverse drugs with known affinity to HSA were analyzed by micellar electrokinetic chromatography (MEKC). The proposed model for HSA affinity assessment was based on retention in hexadecyltrimethylmonium bromide (CTAB) pseudostationary phase and chemically advanced template search (CATS) pharmacophore descriptors. The comparison of various regression methods, namely multiple linear regression (MLR), partial least squares regression (PLS), orthogonal partial least squares (OPLS), and support vector machine (SVM) were performed to develop a model with highest predictability. The obtained models are suitable for the prediction of drug affinity to human serum albumin using retention factor determined by MEKC and CATS descriptors, and only slightly differ in terms of coefficients of determination, Q2 value calculated using leave-one-out cross-validation technique and root-mean-squared error of cross-validation (RMSECV) as well as root-mean-square error in prediction (RMSEP) obtained by external validation.

Metabolomics analysis of the antidepressant prescription Danzhi Xiaoyao Powder in a rat model of Chronic Unpredictable Mild Stress (CUMS)

Ethnopharmacological relevanceDanzhi Xiaoyao Powder (DZXY) is a classical prescription, that has been extensively used in traditional Chinese medicine (TMC) to treat depression for many years. However, the mechanism of DZXY is still unclear. Aim of the studyThe aim was to investigate the mechanism of the antidepressant effect of DZXY on a rat model of chronic unpredictable mild stress (CUMS). Materials and methodsForty male SD (Sprague-Dawley) rats with similar open field test (OFT) results were randomLy divided into a control group (n = 10) and an experimental group (n = 30). A depression model was established in the experimental group using the CUMS method. After the CUMS model was established successfully, the rats were randomLy divided into a depression model group and a DZXY group. The DZXY group was fed DZXY, while the depression model group and control group were given an equal amount of 0.5% sodium carboxymethyl cellulose suspension. Intragastric administration was performed once daily for 14 consecutive days. Animal weight, the sugar preference test, the open field test and the forced swimming test were used to evaluate the modeling effect and the antidepressant effect of DZXY. After the experiment, the plasma of rats was collected and the changes in plasma metabolites were analyzed by UPLC/Q-TOF-MS. The UPLC/Q-TOF-MS spectra data were evaluated by pattern recognition analysis to determine the changes in endogenous metabolites in the rat plasma samples. ResultsThe results of the behavioral investigation showed that the rat model of depression was successfully replicated and that DZXY had an antidepressant effect. Using the UPLC-MS/MS metabolomics platform, partial least squares (PLS) and orthogonal partial least squares (OPLS), metabolic profile models (R2 and Q2 ≥ 0.5) of rat plasma were successfully constructed. The model could distinguish among the control group, the depression model group and the DZXY group. Finally, 38 differential metabolites were identified in the plasma. According to KEGG (http://www.kegg.jp) pathway analysis, amino acid metabolism, lipid metabolism, purine metabolism, the prolactin signaling pathway and bile secretion were enriched and represented the main metabolic pathways influenced in the plasma. ConclusionsThis study successfully established a CUMS depression model. A total of 38 differential metabolites associated with depression were identified in the plasma of rats, 24 of which were modulated by DZXY. These results suggest that DZXY can improve excitability and play an antidepressant role by regulating phenylalanine metabolism, arachidonic acid metabolism, porphyrin metabolism, D-arginine and D-ornithine metabolism, steroid hormone biosynthesis, unsaturated fatty acid biosynthesis and steroid biosynthesis.