Chemometric Analysis for the Classification of some Groups of Drugs with Divergent Pharmacological Activity on the Basis of some Chromatographic and Molecular Modeling Parameters

2018 ◽  
Vol 21 (2) ◽  
pp. 125-137
Author(s):  
Jolanta Stasiak ◽  
Marcin Koba ◽  
Marcin Gackowski ◽  
Tomasz Baczek
Keyword(s):  
Correlation Analysis ◽  
Pharmacological Activity ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Cardiovascular Drugs ◽  
New Drugs ◽  
Analgesic Drugs ◽  
Starting Point ◽  
Chromatographic Parameters

Aim and Objective: In this study, chemometric methods as correlation analysis, cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA) have been used to reduce the number of chromatographic parameters (logk/logkw) and various (e.g., 0D, 1D, 2D, 3D) structural descriptors for three different groups of drugs, such as 12 analgesic drugs, 11 cardiovascular drugs and 36 “other” compounds and especially to choose the most important data of them. Material and Methods: All chemometric analyses have been carried out, graphically presented and also discussed for each group of drugs. At first, compounds’ structural and chromatographic parameters were correlated. The best results of correlation analysis were as follows: correlation coefficients like R = 0.93, R = 0.88, R = 0.91 for cardiac medications, analgesic drugs, and 36 “other” compounds, respectively. Next, part of molecular and HPLC experimental data from each group of drugs were submitted to FA/PCA and CA techniques. Results: Almost all results obtained by FA or PCA, and total data variance, from all analyzed parameters (experimental and calculated) were explained by first two/three factors: 84.28%, 76.38 %, 69.71% for cardiovascular drugs, for analgesic drugs and for 36 “other” compounds, respectively. Compounds clustering by CA method had similar characteristic as those obtained by FA/PCA. In our paper, statistical classification of mentioned drugs performed has been widely characterized and discussed in case of their molecular structure and pharmacological activity. Conclusion: Proposed QSAR strategy of reduced number of parameters could be useful starting point for further statistical analysis as well as support for designing new drugs and predicting their possible activity.

scholarly journals Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

2014 ◽  
Vol 38 (2) ◽  
pp. 372-385 ◽  
Author(s):  
Rodnei Rizzo ◽  
José A. M. Demattê ◽  
Fabrício da Silva Terra
Keyword(s):  
Spectral Data ◽  
Principal Components ◽  
Visual Analysis ◽  
Numerical Technique ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Soil Classification ◽  
Numerical Classification ◽  
Similarity Coefficients

Considering that information from soil reflectance spectra is underutilized in soil classification, this paper aimed to evaluate the relationship of soil physical, chemical properties and their spectra, to identify spectral patterns for soil classes, evaluate the use of numerical classification of profiles combined with spectral data for soil classification. We studied 20 soil profiles from the municipality of Piracicaba, State of São Paulo, Brazil, which were morphologically described and classified up to the 3rd category level of the Brazilian Soil Classification System (SiBCS). Subsequently, soil samples were collected from pedogenetic horizons and subjected to soil particle size and chemical analyses. Their Vis-NIR spectra were measured, followed by principal component analysis. Pearson's linear correlation coefficients were determined among the four principal components and the following soil properties: pH, organic matter, P, K, Ca, Mg, Al, CEC, base saturation, and Al saturation. We also carried out interpretation of the first three principal components and their relationships with soil classes defined by SiBCS. In addition, numerical classification of the profiles based on the OSACA algorithm was performed using spectral data as a basis. We determined the Normalized Mutual Information (NMI) and Uncertainty Coefficient (U). These coefficients represent the similarity between the numerical classification and the soil classes from SiBCS. Pearson's correlation coefficients were significant for the principal components when compared to sand, clay, Al content and soil color. Visual analysis of the principal component scores showed differences in the spectral behavior of the soil classes, mainly among Argissolos and the others soils. The NMI and U similarity coefficients showed values of 0.74 and 0.64, respectively, suggesting good similarity between the numerical and SiBCS classes. For example, numerical classification correctly distinguished Argissolos from Latossolos and Nitossolos. However, this mathematical technique was not able to distinguish Latossolos from Nitossolos Vermelho férricos, but the Cambissolos were well differentiated from other soil classes. The numerical technique proved to be effective and applicable to the soil classification process.

Molecular Modeling Descriptors and Analytical Chemistry Data of Antihistaminic Drugs

2012 ◽  
Vol 95 (3) ◽  
pp. 713-723 ◽  
Author(s):  
Lucyna Konieczna ◽  
Leszek Bober ◽  
Mariusz Belka ◽  
Omasz CIesielski ◽  
Tomasz Bączek
Keyword(s):  
Molecular Modeling ◽  
Principal Component ◽  
New Drugs ◽  
Retention Data ◽  
Biological Assays ◽  
Hyperchem Software ◽  
Pca Method ◽  
Chromatographic Parameters ◽  
Antihistaminic Drugs ◽  
Ir Spectroscopic

Abstract The relationships between experimental and computational descriptors of antihistamine drugs were studied using principal component analysis (PCA). Empirical data came from UV and IR spectroscopic measurements. Nonempirical data, such as structural molecular descriptors and chromatographic data, were obtained from HyperChem software. Another objective was to test whether the parameters used as independent variables (nonempirical and empirical-spectroscopic) could lead to attaining classification similar to that developed on the basis of the chromatographic parameters. To arrive at the answer to the question, a matrix of 18 × 49 data, including HPLC and UV and IR spectroscopic data, together with molecular modeling studies, was evaluated by the PCA method. The obtained clusters of drugs were consistent with the drugs' chemical structure classification. Moreover, the PCA method applied to the HPLC retention data and structural descriptors allowed for classification of the drugs according to their pharmacological properties; hence it may potentially help limit the number of biological assays in the search for new drugs.

Two isoprenylated flavonoids from Dorstenia psilurus activate AMPK, stimulate glucose uptake, inhibit glucose production and lower glycemia

2019 ◽  
Vol 476 (24) ◽  
pp. 3687-3704 ◽  
Author(s):  
Aphrodite T. Choumessi ◽  
Manuel Johanns ◽  
Claire Beaufay ◽  
Marie-France Herent ◽  
Vincent Stroobant ◽  
...  
Keyword(s):  
Glucose Uptake ◽  
Human Cancer ◽  
Glucose Production ◽  
Liver Mitochondria ◽  
New Drugs ◽  
Structural Isomer ◽  
Rat Liver Mitochondria ◽  
Ionization Mass ◽  
Ampk Activation ◽  
Starting Point

Root extracts of a Cameroon medicinal plant, Dorstenia psilurus, were purified by screening for AMP-activated protein kinase (AMPK) activation in incubated mouse embryo fibroblasts (MEFs). Two isoprenylated flavones that activated AMPK were isolated. Compound 1 was identified as artelasticin by high-resolution electrospray ionization mass spectrometry and 2D-NMR while its structural isomer, compound 2, was isolated for the first time and differed only by the position of one double bond on one isoprenyl substituent. Treatment of MEFs with purified compound 1 or compound 2 led to rapid and robust AMPK activation at low micromolar concentrations and increased the intracellular AMP:ATP ratio. In oxygen consumption experiments on isolated rat liver mitochondria, compound 1 and compound 2 inhibited complex II of the electron transport chain and in freeze–thawed mitochondria succinate dehydrogenase was inhibited. In incubated rat skeletal muscles, both compounds activated AMPK and stimulated glucose uptake. Moreover, these effects were lost in muscles pre-incubated with AMPK inhibitor SBI-0206965, suggesting AMPK dependency. Incubation of mouse hepatocytes with compound 1 or compound 2 led to AMPK activation, but glucose production was decreased in hepatocytes from both wild-type and AMPKβ1−/− mice, suggesting that this effect was not AMPK-dependent. However, when administered intraperitoneally to high-fat diet-induced insulin-resistant mice, compound 1 and compound 2 had blood glucose-lowering effects. In addition, compound 1 and compound 2 reduced the viability of several human cancer cells in culture. The flavonoids we have identified could be a starting point for the development of new drugs to treat type 2 diabetes.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Fingerprint of PGI Mantova Cucumis melo by ICP-MS and Chemometric Analysis

2020 ◽  
Vol 17 (1) ◽  
pp. 94-104
Author(s):  
Antonio F. Mottese ◽  
Maria R. Fede ◽  
Francesco Caridi ◽  
Giuseppe Sabatino ◽  
Giuseppe Marcianò ◽  
...  
Keyword(s):  
Cucumis Melo ◽  
Principal Component ◽  
Inorganic Elements ◽  
Discrimination Power ◽  
Icp Ms ◽  
High Discrimination ◽  
Strong Relation ◽  
Trace Element Contents ◽  
Safe Food

Background and Objectives: In this work, yellow and green varieties of Cucumis melo fruits belonging to different cultivars were studied. In detail, three Sicilian cultivars of winter melons tutelated by TAP (Traditional agro-alimentary products) labels were considered, whereas asun protected the Calabrian winter melon was studied too. With the aim to compare the selective uptakes of inorganic elements among winter and summer fruits, the “PGI Melone Mantovano” was investigated. The purpose of this work was to apply the obtained results i) to guarantee the quality and healthiness of fruits, ii) to producers defend, iii) to help the customers in safe food purchase. Method: All samples were analyzed by ICP-MS and the obtained results, subsequently, were subjected to Cluster analysis (CA), Principal component analysis (PCA) and Canonical discriminant analysis (CDA). Results: CA results were generally in agreement with samples origin, whereas the PCA elaboration has confirmed the presence of a strong relation between fruit origins and trace element contents. In particular, two principal components justified the 57.32% of the total variance (PC1= 40.95%, PC2= 16.37%). Finally, the CDA approach has provided several functions with high discrimination power, confirmed by the correct classification of all samples (100%). Conclusions: CA, PCA and CDA could represent an integrated to label to discriminate the origin of agri-food products and, thus, protect and guarantee their healthiness.

Design of Inhibitors for Glyceraldehyde-3-phosphate Dehydrogenase (GAPDH) Enzyme of Leishmania mexicana

2020 ◽  
Vol 16 (6) ◽  
pp. 784-795
Author(s):  
Krisnna M.A. Alves ◽  
Fábio José Bonfim Cardoso ◽  
Kathia M. Honorio ◽  
Fábio A. de Molfetta
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Binding Site ◽  
Point Of View ◽  
New Drugs ◽  
Leishmania Mexicana ◽  
Receptor Complexes ◽  
Starting Point ◽  
Dynamics Simulations ◽  
Selection Of

Background:: Leishmaniosis is a neglected tropical disease and glyceraldehyde 3- phosphate dehydrogenase (GAPDH) is a key enzyme in the design of new drugs to fight this disease. Objective:: The present study aimed to evaluate potential inhibitors of GAPDH enzyme found in Leishmania mexicana (L. mexicana). Methods: A search for novel antileishmanial molecules was carried out based on similarities from the pharmacophoric point of view related to the binding site of the crystallographic enzyme using the ZINCPharmer server. The molecules selected in this screening were subjected to molecular docking and molecular dynamics simulations. Results:: Consensual analysis of the docking energy values was performed, resulting in the selection of ten compounds. These ligand-receptor complexes were visually inspected in order to analyze the main interactions and subjected to toxicophoric evaluation, culminating in the selection of three compounds, which were subsequently submitted to molecular dynamics simulations. The docking results showed that the selected compounds interacted with GAPDH from L. mexicana, especially by hydrogen bonds with Cys166, Arg249, His194, Thr167, and Thr226. From the results obtained from molecular dynamics, it was observed that one of the loop regions, corresponding to the residues 195-222, can be related to the fitting of the substrate at the binding site, assisting in the positioning and the molecular recognition via residues responsible for the catalytic activity. Conclusion:: he use of molecular modeling techniques enabled the identification of promising compounds as inhibitors of the GAPDH enzyme from L. mexicana, and the results obtained here can serve as a starting point to design new and more effective compounds than those currently available.

scholarly journals Exceptional nonrelativistic effective field theories with enhanced symmetries

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Tomáš Brauner
Keyword(s):  
Effective Field Theories ◽  
Explicit Construction ◽  
Effective Field ◽  
Field Theories ◽  
Relativistic Case ◽  
Starting Point ◽  
Higher Power ◽  
Mere Presence ◽  
Spatial Rotations

Abstract We initiate the classification of nonrelativistic effective field theories (EFTs) for Nambu-Goldstone (NG) bosons, possessing a set of redundant, coordinate-dependent symmetries. Similarly to the relativistic case, such EFTs are natural candidates for “exceptional” theories, whose scattering amplitudes feature an enhanced soft limit, that is, scale with a higher power of momentum at long wavelengths than expected based on the mere presence of Adler’s zero. The starting point of our framework is the assumption of invariance under spacetime translations and spatial rotations. The setup is nevertheless general enough to accommodate a variety of nontrivial kinematical algebras, including the Poincaré, Galilei (or Bargmann) and Carroll algebras. Our main result is an explicit construction of the nonrelativistic versions of two infinite classes of exceptional theories: the multi-Galileon and the multi-flavor Dirac-Born-Infeld (DBI) theories. In both cases, we uncover novel Wess-Zumino terms, not present in their relativistic counterparts, realizing nontrivially the shift symmetries acting on the NG fields. We demonstrate how the symmetries of the Galileon and DBI theories can be made compatible with a nonrelativistic, quadratic dispersion relation of (some of) the NG modes.

scholarly journals Seed Set Patterns in East African Highland Cooking Bananas Are Dependent on Weather before, during and after Pollination

Horticulturae ◽  
2021 ◽  
Vol 7 (7) ◽  
pp. 165
Author(s):  
Allan Waniale ◽  
Rony Swennen ◽  
Settumba B. Mukasa ◽  
Arthur K. Tugume ◽  
Jerome Kubiriba ◽  
...  
Keyword(s):  
Critical Period ◽  
Floral Development ◽  
Seed Set ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Maximum Temperature ◽  
Fruit Pulp ◽  
East African ◽  
Average Temperature ◽  
Wild Banana

Seed set in banana is influenced by weather, yet the key weather attributes and the critical period of influence are unknown. We therefore investigated the influence of weather during floral development for a better perspective of seed set increase. Three East African highland cooking bananas (EAHBs) were pollinated with pollen fertile wild banana ‘Calcutta 4′. At full maturity, bunches were harvested, ripened, and seeds extracted from fruit pulp. Pearson’s correlation analysis was then conducted between seed set per 100 fruits per bunch and weather attributes at 15-day intervals from 105 days before pollination (DBP) to 120 days after pollination (DAP). Seed set was positively correlated with average temperature (P < 0.05–P < 0.001, r = 0.196–0.487) and negatively correlated with relative humidity (RH) (P < 0.05–P < 0.001, r = −0.158–−0.438) between 75 DBP and the time of pollination. After pollination, average temperature was negatively correlated with seed set in ‘Mshale’ and ‘Nshonowa’ from 45 to 120 DAP (P < 0.05–P < 0.001, r = −0.213–−0.340). Correlation coefficients were highest at 15 DBP for ‘Mshale’ and ‘Nshonowa’, whereas for ‘Enzirabahima’, the highest were at the time of pollination. Maximum temperature as revealed by principal component analysis at the time of pollination should be the main focus for seed set increase.

scholarly journals Semi-automated regional classification of the style of activity of slow rock-slope deformations using PS InSAR and SqueeSAR velocity data

Landslides ◽  
2021 ◽  
Author(s):  
Chiara Crippa ◽  
Elena Valbuzzi ◽  
Paolo Frattini ◽  
Giovanni B. Crosta ◽  
Margherita C. Spreafico ◽  
...  
Keyword(s):  
Rock Slope ◽  
Progressive Failure ◽  
Principal Component ◽  
Cost Effective ◽  
Displacement Rate ◽  
Machine Learning Classification ◽  
Management Perspective ◽  
Alpine Environments

AbstractLarge slow rock-slope deformations, including deep-seated gravitational slope deformations and large landslides, are widespread in alpine environments. They develop over thousands of years by progressive failure, resulting in slow movements that impact infrastructures and can eventually evolve into catastrophic rockslides. A robust characterization of their style of activity is thus required in a risk management perspective. We combine an original inventory of slow rock-slope deformations with different PS-InSAR and SqueeSAR datasets to develop a novel, semi-automated approach to characterize and classify 208 slow rock-slope deformations in Lombardia (Italian Central Alps) based on their displacement rate, kinematics, heterogeneity and morphometric expression. Through a peak analysis of displacement rate distributions, we characterize the segmentation of mapped landslides and highlight the occurrence of nested sectors with differential activity and displacement rates. Combining 2D decomposition of InSAR velocity vectors and machine learning classification, we develop an automatic approach to characterize the kinematics of each landslide. Then, we sequentially combine principal component and K-medoids cluster analyses to identify groups of slow rock-slope deformations with consistent styles of activity. Our methodology is readily applicable to different landslide datasets and provides an objective and cost-effective support to land planning and the prioritization of local-scale studies aimed at granting safety and infrastructure integrity.

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