Explore the vast range of titles available.

The number of protein kinase inhibitors (PKIs) approved worldwide continues to grow steadily, with 39 drugs approved in the period between 2001 and January 2018. PKIs on the market have been the subject of many reviews, and structure-property relationships specific to this class of drugs have been inferred. However, the large number of PKIs under development is often overlooked. In this paper, we present PKIDB (Protein Kinase Inhibitor Database), a monthly-updated database gathering approved PKIs as well as PKIs currently in clinical trials. The database compiles currently 180 inhibitors ranging from phase 0 to 4 clinical trials along with annotations extracted from seven public resources. The distribution and property ranges of standard physicochemical properties are presented. They can be used as filters to better prioritize compound selection for future screening campaigns. Interestingly, more than one-third of the kinase inhibitors violate at least one Lipinski’s rule. A Principal Component Analysis (PCA) reveals that Type-II inhibitors are mapped to a distinct chemical space as compared to orally administrated drugs as well as to other types of kinase inhibitors. Using a Principal Moment of Inertia (PMI) analysis, we show that PKIs under development tend to explore new shape territories as compared to approved PKIs. In order to facilitate the analysis of the protein space, the kinome tree has been annotated with all protein kinases being targeted by PKIs. Finally, we analyzed the pipeline of the pharmaceutical companies having PKIs on the market or still under development. We hope that this work will assist researchers in the kinase field in identifying and designing the next generation of kinase inhibitors for still untargeted kinases. The PKIDB database is freely accessible from a website at http://www.icoa.fr/pkidb and can be easily browsed through a user-friendly spreadsheet-like interface.

In this study, the chemical structure of asphalt aging was analyzed and identified based on [sup.1]H-NMR quantitative technology and chemometrics analysis. The characteristic full component information of 30 samples before and after aging from 5 different oil sources was measured by [sup.1]H-NMR, and the results were converted into a data matrix. This study used PCA, HAC, OPLS-DA, and Fisher discriminant analysis to evaluate the change rules of the chemical composition of asphalt from different oil sources after aging. The results showed that the [sup.1]H-NMR spectra of 30 asphalt samples were very similar, and hydrogen could be divided into 4 categories according to the chemical shift: H[sub.A], H[sub.α], H[sub.β], and H[sub.γ]. The shapes of [sup.1]H-NMR of asphalt samples from different oil sources showed slight differences, while the shapes of the [sup.1]H-NMR spectra of asphalt samples with different aging degrees from the same oil source was basically the same. The results of PCA and HAC analysis showed that the samples of the same asphalt and asphalt with similar oil sources before and after aging were still in the same category, and the spatial distance was very close, while the spatial distance of asphalts from different oil sources was very different. The Fisher discriminant function established by PCA and HAC can be used to distinguish asphalt samples from different oil sources with an accuracy of up to 100%.

Stingless bee honey produced by Heterotrigona itama from different botanical origins was characterised and discriminated. Three types of stingless bee honey collected from acacia, gelam, and starfruit nectars were analyzed and compared with Apis mellifera honey. The results showed that stingless bee honey samples from the three different botanical origins were significantly different in terms of their moisture content, pH, free acidity, total soluble solids, colour characteristics, sugar content, amino acid content and antioxidant properties. Stingless bee honey was significantly different from Apis mellifera honey in terms of physicochemical and antioxidant properties. The amino acid content was further used in the chemometrics analysis to evaluate the role of amino acid in discriminating honey according to botanical origin. Partial least squares-discriminant analysis (PLS-DA) revealed that the stingless bee honey was completely distinguishable from Apis mellifera honey. Notably, a clear distinction between the stingless bee honey types was also observed. The specific amino acids involved in the distinction of honey were cysteine for acacia and gelam, phenylalanine and 3-hydroxyproline for starfruit, and proline for Apis mellifera honey. The results showed that all honey samples were successfully classified based on amino acid content.

There are possible environmental risks related to gene flow from genetically engineered organisms. It is important to find accurate, fast, and inexpensive methods to detect and monitor the presence of genetically modified (GM) organisms in crops and derived crop products. In the present study, GM maize kernels containing both cry1Ab/cry2Aj-G10evo proteins and their non-GM parents were examined by using hyperspectral imaging in the near-infrared (NIR) range (874.41–1733.91 nm) combined with chemometric data analysis. The hypercubes data were analyzed by applying principal component analysis (PCA) for exploratory purposes, and support vector machine (SVM) and partial least squares discriminant analysis (PLS–DA) to build the discriminant models to class the GM maize kernels from their contrast. The results indicate that clear differences between GM and non-GM maize kernels can be easily visualized with a nondestructive determination method developed in this study, and excellent classification could be achieved, with calculation and prediction accuracy of almost 100%. This study also demonstrates that SVM and PLS–DA models can obtain good performance with 54 wavelengths, selected by the competitive adaptive reweighted sampling method (CARS), making the classification processing for online application more rapid. Finally, GM maize kernels were visually identified on the prediction maps by predicting the features of each pixel on individual hyperspectral images. It was concluded that hyperspectral imaging together with chemometric data analysis is a promising technique to identify GM maize kernels, since it overcomes some disadvantages of the traditional analytical methods, such as complex and monotonous sampling.

Since the first approval of a protein kinase inhibitor (PKI) by the Food and Drug Administration (FDA) in 2001, 55 new PKIs have reached the market, and many inhibitors are currently being evaluated in clinical trials. This is a clear indication that protein kinases still represent major drug targets for the pharmaceutical industry. In a previous work, we have introduced PKIDB, a publicly available database, gathering PKIs that have already been approved (Phase 4), as well as those currently in clinical trials (Phases 0 to 3). This database is updated frequently, and an analysis of the new data is presented here. In addition, we compared the set of PKIs present in PKIDB with the PKIs in early preclinical studies found in ChEMBL, the largest publicly available chemical database. For each dataset, the distribution of physicochemical descriptors related to drug-likeness is presented. From these results, updated guidelines to prioritize compounds for targeting protein kinases are proposed. The results of a principal component analysis (PCA) show that the PKIDB dataset is fully encompassed within all PKIs found in the public database. This observation is reinforced by a principal moments of inertia (PMI) analysis of all molecules. Interestingly, we notice that PKIs in clinical trials tend to explore new 3D chemical space. While a great majority of PKIs is located on the area of “flatland”, we find few compounds exploring the 3D structural space. Finally, a scaffold diversity analysis of the two datasets, based on frequency counts was performed. The results give insight into the chemical space of PKIs, and can guide researchers to reach out new unexplored areas. PKIDB is freely accessible from the following website: http://www.icoa.fr/pkidb.

Selenium is a beneficial element in agriculture, particularly for its potential to improve plant growth and stress tolerance at suitable concentrations. In this study, Phaseolus vulgaris was foliar-sprayed with selenium selenate (Se) or selenium nanoparticles (SeNP) at different concentrations during the vegetative stage; afterward, the seed yield was analyzed for metabolomics using 1 H, J -resolved and HSQC NMR data, and NMR databases. A total of 47 metabolites were identified with sugars being the major chemical class. In the control sample, the most abundant sugar was stachyose (14.6 ± 0.8 mM). Among the identified alkaloids, the concentration of trigonelline was the highest (0.6 ± 0.08 mM). Chemometric and cluster analyses distinctly differentiated the control from the Se and SeNP-treated samples. Treatments with SeNP resulted in elevated concentrations of sugars, carboxylic acids, and sulfur-containing amino acids compared to control and Se treated samples. Conversely, betaine levels were higher in Se samples. The presence of Se and SeNP significantly decreased the levels of several aliphatic amino acids, e.g. alanine. The addition of 50 µM SeNP upregulated the levels of trigonelline and syringate by 2-fold and 1.75-fold, respectively, relative to the control. Pathway analysis indicated the most significantly altered pathways due to SeNP addition were arginine biosynthesis and nitrogen metabolism. The pathways influenced by Se addition were glyoxylate and dicarboxylate metabolism as well as glycine-serine and threonine metabolism. This study proved that SeNP are more efficient than Se in enhancing the metabolic profile of Phaseolus vulgaris which will have implications for agricultural practices, focusing on the sustainability and nutritional enhancement of crops.

Pomegranate peel contains ellagitannins as antioxidant principles but the aqueous extraction temperature for optimum ellagitannins recovery and antioxidant profile remains elusive. This study employed advanced chemometrics models, including principal component analysis (PCA) and orthogonal projections to latent structures (OPLS) to determine the influence of different extraction temperatures (25–95 °C) during a 1 h period on the antioxidant profile of the peel’s aqueous extract, as well as the profile of the constituent ellagitannins recovered using Amberlite ® XAD16N resin and characterized by LC–MS. Extraction at 78 °C had the most potent radical scavenging potency, oxygen radical absorbance capacity (ORAC), and anti-lipid peroxidative activity, which was consistent with its highest ellagitannins constituents relative to other extraction temperatures. Pearson’s bivariate correlation depicted strong positive correlations ( r  = 0.7–0.9) between the ellagitannins and the anti-lipid peroxidative profile of the extracts. PCA and OPLS models adequately characterized and predicted the variation and patterns in the antioxidant and phytochemical datasets (up to 93.6%). OPLS biplot showed a distinct clustering pattern of the antioxidant and ellagitannins variables around 78 °C extraction temperature, suggesting the observable influence of the ellagitannins on the potent antioxidant profile of the 78 °C extraction. The variable contribution plots, as well as the obtained antioxidant data, suggest punicalagins, granatin A, and ellagic acid as notable influencing ellagitannins. It is safe to speculate that extraction at 78 °C optimally influenced the ellagitannins and antioxidant profile of pomegranate fruit peel. Thus, this aqueous extraction temperature may be promising for optimally recovering the ellagitannins from pomegranate peel, without sacrificing the antioxidant capacity.

Magnolia champaca (L.) Baill. ex Pierre of family Magnoliaceae, is a perennial tree with aromatic, ethnobotanical, and medicinal uses. The M. champaca leaf is reported to have a myriad of therapeutic activities, however, there are limited reports available on the chemical composition of the leaf essential oil of M. champaca. The present study explored the variation in the yield and chemical composition of leaf essential oil isolated from 52 accessions of M. champaca. Through hydrodistillation, essential oil yield was obtained, varied in the range of 0.06 ± 0.003% and 0.31 ± 0.015% (v/w) on a fresh weight basis. GC-MS analysis identified a total of 65 phytoconstituents accounting for 90.23 to 98.90% of the total oil. Sesquiterpene hydrocarbons (52.83 to 65.63%) constituted the major fraction followed by sesquiterpene alcohols (14.71 to 22.45%). The essential oils were found to be rich in β-elemene (6.64 to 38.80%), γ-muurolene (4.63 to 22.50%), and β-caryophyllene (1.10 to 20.74%). Chemometrics analyses such as PCA, PLS-DA, sPLS-DA, and cluster analyses such as hierarchical clustering, i.e., dendrogram and partitional clustering, i.e., K-means classified the essential oils of M. champaca populations into three different chemotypes: chemotype I (β-elemene), chemotype II (γ-muurolene) and chemotype III (β-caryophyllene). The chemical polymorphism analyzed in the studied populations would facilitate the selection of chemotypes with specific compounds. The chemotypes identified in the M. champaca populations could be developed as promising bio-resources for conservation and pharmaceutical application and further improvement of the taxa.

Ellagitannins are the predominant bioactive tannins present in the peel of pomegranate. Studies have explored different extraction solvents to maximize recovery of the bioactive phytochemicals in pomegranate peel but lack proper statistical correlation between the extraction solvent, phytochemistry and bioactivity. This study employed advanced chemometrics models, including principal component analysis (PCA) and orthogonal projections to latent structures (OPLS) in determining the solvent extraction (among methanol, ethanol, acetone and water) that will yield optimum antioxidant and ellagitannins profiles from “Wonderful” pomegranate peel. Acetone extraction had the highest (p ˂ 0.05) total phenol (TFC) and flavonoid contents and strongest (p ˂ 0.05) Fe3+ reducing effect, while methanol extraction had the strongest (p ˂ 0.05) radical scavenging activity. The oxygen radical absorbance capacity (ORAC) and anti-lipid peroxidative activities of both solvent extractions outperformed that of the ethanol and water extractions. Tannins purified using Amberlite® XAD16N resin were highest for acetone and methanol extractions. LCMS-quantified ellagitannins in the tannins varied for the different extractions. PCA and OPLS models adequately characterized, described and predicted the variation and patterns in the antioxidant and ellagitannins datasets (up to 95% for PCA). OPLS bi-plot showed that the high ellagic acid constituents and total tannins yield of the methanol extraction influenced its potent radical scavenging activity, while the ellagitannin constituents including punicalagins, granatin A, geraniin and casuarinin influenced the high TFC, as well as the potent Fe3+ reducing, ORAC and anti-lipid peroxidative activities of acetone extraction. Acetone and methanol extraction of pomegranate peel are promising for optimum ellagitannins recovery and antioxidant profile.

The continuous deterioration of drinking water quality supplies by several anthropogenic activities is a serious global challenge in recent times. In this current study, the drinking water quality of Ikem rural agricultural area (southeastern Nigeria) was assessed using chemometrics and multiple indexical methods. Twenty-five groundwater samples were collected from hand-dug wells and analyzed for physicochemical parameters such as pH, major ions, and heavy metals. The pH of the samples (which ranged between 5.2 and 6.7) indicated that waters were slightly acidic. Cations and anions (except for phosphate) were within their respective standard limits. Except for Mn, heavy metals were also found to be below their maximum allowable limits. Factor analysis identified both geogenic processes and anthropogenic inputs as possible origins of the analyzed physicochemical parameters. Modified heavy metal index, geoaccumulation index, and overall index of pollution revealed that all the hand-dug wells were in excellent condition, and hence safe for drinking purposes. However, pollution load index, water quality index (WQI), and entropy-weighted water quality index (EWQI) revealed that some wells (about 8–12%) were slightly contaminated, and hence are placed in good water category. A hierarchical cluster analysis (HCA) was performed based on the integration of the WQI and EWQI results. The HCA revealed two major quality categories of the samples. While the first cluster comprises of samples classified as excellent drinking water by both WQI and EWQI models, the second cluster comprises of about 12% samples which were identified as good water by either the WQI or EWQI.