Physicochemical, Interaction & Topological Descriptors vs. hMAO-A Inhibition of Aplysinopsin Analogs: A Boulevard to the Discovery of Semi-Synthetic Anti-Depression Agents

2021 ◽  
Vol 22 ◽  
Author(s):  
Rajeev K. Singla ◽  
Ghulam Md Ashraf ◽  
Magdah Ganash ◽  
Varadaraj Bhat G ◽  
Bairong Shen
Keyword(s):  
Model Development ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Topological Descriptors ◽  
Good Prediction ◽  
Physicochemical Interaction ◽  
Human Beings ◽  
2D Qsar

Background: Neurological disorder, depression is the globally 4th leading cause of chronic disabilities in human beings. Objective: This study aimed to model a 2D-QSAR equation that can facilitate the researchers to design better aplysinopsin analogs with potent hMAO-A inhibition. Methods: Aplysinopsin analogs dataset were subjected to ADME assessment for drug-likeness suitability using StarDrop software before modeled equation. 2D-QSAR equations were generated using VLife MDS 4.6. Dataset was segregated into training and test set using different methodologies, followed by variable selection. Model development was done using principal component regression, partial least square regression, and multiple regression. Results: The dataset has successfully qualified the drug-likeness criteria in ADME simulation, with more than 90% of molecules cleared the ideal conditions including intrinsic solubility, hydrophobicity, CYP3A4 2C9pKi, hERG pIC50, etc. 112 models were developed using multiparametric consideration of methodologies. The best six models were discussed with their extent of significance and prediction capabilities. ALP97 was emerged out as the most significant model out of all, with ~83% of the variance in the training set, the internal predictive ability of ~74% while having the external predictive capability of ~79%. Conclusion: ADME assessment suggested that aplysinopsin analogs are worth investigating. Interaction among the descriptors in a way of summation or multiplication products, are quite influential and yielding significant 2D-QSAR models with good prediction efficiency. This model can be used for the design of a more potent hMAO-A inhibitor having an aplysinopsin scaffold, which can then contribute to the treatment of depression and other neurological disorders.

scholarly journals Hyperspectral Spectroscopic Study of Soil Properties- A Review

2020 ◽  
pp. 14-25
Author(s):  
Chandan Goswami ◽  
Naorem Janaki Singh ◽  
Bijoy Krishna Handique
Keyword(s):  
Organic Carbon ◽  
Soil Properties ◽  
Soil Analysis ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Spectral Signature ◽  
Good Prediction ◽  
Organic Carbon Content

Soil analysis is required for efficient use of inputs viz. seeds, fertilizers, irrigation water and other agricultural planning. However, there are several disadvantages of soil analysis such as they are time consuming, expensive and labour intensive. Many approaches are developed to overcome these difficulties. Hyperspectral spectroscopy is emerging as a promising tool for studying soil, water and vegetation. Therefore, an attempt has been made to review the scope of using hyperspectral reflectance spectroscopy for estimation of soil properties as an alternative to traditional laboratory soil analysis methods. Spectral signature of soil can be used for fast and non destructive estimation of soil properties. Diffuse reflectance in 350-2500 nm range of electromagnetic spectra forms the basis of hyperspectral spectroscopy. An object is characterized by the characteristic absorptions and peaks in the electromagnetic spectra. A number of calibration techniques are applied for establishing relationship between reflectance spectra and soil properties. Multiple Linear Regression (MLR), Principal Component Regression (PCR) and Partial Least Square Regression (PLSR) are most commonly used techniques. MLR, PCR and PLSR are also used for prediction of several soil properties such as pH, soil organic carbon content, nitrogen, phosphorus, potassium, calcium, magnesium, sodium, iron, manganese, zinc, copper, boron, molybdenum, sand silt, clay and soil moisture. Some commonly used spectral indices are also applied for prediction of soil properties. Some of the soil physical properties viz. sand, silt and clay as well as chemical properties viz. pH and organic carbon could be estimated with good to very good prediction using pure spectra of soil. However, contrasting results of prediction of soil properties using multivariate analysis techniques have also been reported. The content of this review article will be helpful for researchers who are working on alternate methods of estimation of soil properties.

scholarly journals A Novel Validated Injectable Colistimethate Sodium Analysis Combining Advanced Chemometrics and Design of Experiments

Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1546
Author(s):  
Ioanna Dagla ◽  
Anthony Tsarbopoulos ◽  
Evagelos Gikas
Keyword(s):  
Design Of Experiments ◽  
High Performance ◽  
Similarity Index ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Distance Measures ◽  
Linear Regression Models ◽  
Colistimethate Sodium

Colistimethate sodium (CMS) is widely administrated for the treatment of life-threatening infections caused by multidrug-resistant Gram-negative bacteria. Until now, the quality control of CMS formulations has been based on microbiological assays. Herein, an ultra-high-performance liquid chromatography coupled to ultraviolet detector methodology was developed for the quantitation of CMS in injectable formulations. The design of experiments was performed for the optimization of the chromatographic parameters. The chromatographic separation was achieved using a Waters Acquity BEH C8 column employing gradient elution with a mobile phase consisting of (A) 0.001 M aq. ammonium formate and (B) methanol/acetonitrile 79/21 (v/v). CMS compounds were detected at 214 nm. In all, 23 univariate linear-regression models were constructed to measure CMS compounds separately, and one partial least-square regression (PLSr) model constructed to assess the total CMS amount in formulations. The method was validated over the range 100–220 μg mL−1. The developed methodology was employed to analyze several batches of CMS injectable formulations that were also compared against a reference batch employing a Principal Component Analysis, similarity and distance measures, heatmaps and the structural similarity index. The methodology was based on freely available software in order to be readily available for the pharmaceutical industry.

scholarly journals The inflammatory profile of cerebrospinal fluid, plasma, and saliva from patients with severe neuropathic pain and healthy controls-a pilot study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mika Jönsson ◽  
Björn Gerdle ◽  
Bijar Ghafouri ◽  
Emmanuel Bäckryd
Keyword(s):  
Cerebrospinal Fluid ◽  
Neuropathic Pain ◽  
Pain Intensity ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Therapeutic Interventions ◽  
Healthy Controls ◽  
Inflammatory Profile

Abstract Background Neuropathic pain (NeuP) is a complex, debilitating condition of the somatosensory system, where dysregulation between pro- and anti-inflammatory cytokines and chemokines are believed to play a pivotal role. As of date, there is no ubiquitously accepted diagnostic test for NeuP and current therapeutic interventions are lacking in efficacy. The aim of this study was to investigate the ability of three biofluids - saliva, plasma, and cerebrospinal fluid (CSF), to discriminate an inflammatory profile at a central, systemic, and peripheral level in NeuP patients compared to healthy controls. Methods The concentrations of 71 cytokines, chemokines and growth factors in saliva, plasma, and CSF samples from 13 patients with peripheral NeuP and 13 healthy controls were analyzed using a multiplex-immunoassay based on an electrochemiluminescent detection method. The NeuP patients were recruited from a clinical trial of intrathecal bolus injection of ziconotide (ClinicalTrials.gov identifier NCT01373983). Multivariate data analysis (principal component analysis and orthogonal partial least square regression) was used to identify proteins significant for group discrimination and protein correlation to pain intensity. Proteins with variable influence of projection (VIP) value higher than 1 (combined with the jack-knifed confidence intervals in the coefficients plot not including zero) were considered significant. Results We found 17 cytokines/chemokines that were significantly up- or down-regulated in NeuP patients compared to healthy controls. Of these 17 proteins, 8 were from saliva, 7 from plasma, and 2 from CSF samples. The correlation analysis showed that the most important proteins that correlated to pain intensity were found in plasma (VIP > 1). Conclusions Investigation of the inflammatory profile of NeuP showed that most of the significant proteins for group separation were found in the less invasive biofluids of saliva and plasma. Within the NeuP patient group it was also seen that proteins in plasma had the highest correlation to pain intensity. These preliminary results indicate a potential for further biomarker research in the more easily accessible biofluids of saliva and plasma for chronic peripheral neuropathic pain where a combination of YKL-40 and MIP-1α in saliva might be of special interest for future studies that also include other non-neuropathic pain states.

Identification and Quantification of Metamizole in Traditional Herbal Medicines using Spectroscopy FTIR-ATR combined with Chemometrics

2021 ◽  
pp. 4413-4419
Author(s):  
Dharmastuti Cahya Fatmarahmi ◽  
Ratna Asmah Susidarti ◽  
Respati Tri Swasono ◽  
Abdul Rohman
Keyword(s):  
Discriminant Analysis ◽  
Herbal Medicine ◽  
Ftir Spectroscopy ◽  
Herbal Medicines ◽  
Principal Component ◽  
Attenuated Total Reflection ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Precision Data

The study aims to develop an effective, efficient, and reliable method using Fourier Transform Infrared (FTIR) spectroscopy with Attenuated Total Reflection (ATR) combined with chemometric for identifying the synthetic drug in Indonesian herbal medicine known as Jamu. Jamu powders, Metamizole, and the binary mixture of Jamu and Metamizole were measured using FTIR-ATR at the mid-infrared region (4000-650 cm-1). The obtained spectra profiles were further analyzed by Principal Component Analysis, Partial Least Square Regression, Principal Component Regression, and Discriminant Analysis. Jamu Pegel Linu (JPL), Jamu Encok (JE), Jamu Sakit Pinggang (JSP), Metamizole (M), and adulterated Jamu by Metamizole were discriminated well on PCA score plot. PLSR and PCR showed the accuracy and precision data to quantify JPL, JE, and JSP, and each adulterated by M with R2 value > 0,995 and low value of RMSEC and RMSEP. Discriminant Analysis (DA) was successfully grouping Jamu and Metamizole without any misclassification. A combination of FTIR spectroscopy and chemometrics offered useful tools for detecting Metamizole in traditional herbal medicine.

scholarly journals Authentication of Greek PDO Kalamata Table Olives: A Novel Non-Target High Resolution Mass Spectrometric Approach

Molecules ◽  
2020 ◽  
Vol 25 (12) ◽  
pp. 2919
Author(s):  
Natasa P. Kalogiouri ◽  
Reza Aalizadeh ◽  
Marilena E. Dasenaki ◽  
Nikolaos S. Thomaidis
Keyword(s):  
High Performance ◽  
Model Development ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Table Olives ◽  
Method Performance ◽  
Validation Parameters ◽  
Targeted Screening ◽  
Mass Spectrometric Approach

Food science continually requires the development of novel analytical methods to prevent fraudulent actions and guarantee food authenticity. Greek table olives, one of the most emblematic and valuable Greek national products, are often subjected to economically motivated fraud. In this work, a novel ultra-high-performance liquid chromatography–quadrupole time of flight tandem mass spectrometry (UHPLC-QTOF-MS) analytical method was developed to detect the mislabeling of Greek PDO Kalamata table olives, and thereby establish their authenticity. A non-targeted screening workflow was applied, coupled to advanced chemometric techniques such as Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) in order to fingerprint and accurately discriminate PDO Greek Kalamata olives from Kalamata (or Kalamon) type olives from Egypt and Chile. The method performance was evaluated using a target set of phenolic compounds and several validation parameters were calculated. Overall, 65 table olive samples from Greece, Egypt, and Chile were analyzed and processed for the model development and its accuracy was validated. The robustness of the chemometric model was tested using 11 Greek Kalamon olive samples that were produced during the following crop year, 2018, and they were successfully classified as Greek Kalamon olives from Kalamata. Twenty-six characteristic authenticity markers were indicated to be responsible for the discrimination of Kalamon olives of different geographical origins.

scholarly journals Prediction of macro- and microelements content in Croatian common bean landraces (Phaseolus vulgaris L.) by NIR spectroscopy

Poljoprivreda ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 48-55
Author(s):  
Marina Vranić ◽  
Marko Petek ◽  
Krešimir Bošnjak ◽  
Boris Lazarević ◽  
Klaudija Carović Stanko
Keyword(s):  
Phaseolus Vulgaris ◽  
Common Bean ◽  
Near Infrared ◽  
Model Development ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Potassium Oxide ◽  
Phaseolus Vulgaris L

In this study, near-infrared spectroscopy (NIRS) was used to predict the contents of essential macro- and microelements in common bean (Phaseolus vulgaris L.) accessions of most widespread Croatian landraces. Total of 175 samples were used for the model development by modified partial least square (MPLS), principal component regression (PCR) and partial least square (PLS) techniques. Based on the coefficients of determination (R2), standard error of calibration (SEC) and error of prediction (SEP) the models developed were (i) nearly applicable for nitrogen (N) (0.89, 0.12 and 0.45 respectively), (ii) poor for iron (Fe), cinc (Zn), potassium oxide (K2O) and potassium (K), (iii) usable for phosphorus pentoxide (P2O5), phosphorus (P), phytic acid (PA) and manganese (Mn). The MPLS regression statistics suggested the most accurate models developed comparing with PLS and PCR. It was concluded that a wider set of common bean samples needs to be used for macro- and microelements prediction by NIRS.

scholarly journals Prediction of Soil Organic Carbon for Ethiopian Highlands Using Soil Spectroscopy

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Tadele Amare ◽  
Christian Hergarten ◽  
Hans Hurni ◽  
Bettina Wolfgramm ◽  
Birru Yitaferu ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Diffuse Reflectance ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Calibration Models ◽  
The Stability

Soil spectroscopy was applied for predicting soil organic carbon (SOC) in the highlands of Ethiopia. Soil samples were acquired from Ethiopia’s National Soil Testing Centre and direct field sampling. The reflectance of samples was measured using a FieldSpec 3 diffuse reflectance spectrometer. Outliers and sample relation were evaluated using principal component analysis (PCA) and models were developed through partial least square regression (PLSR). For nine watersheds sampled, 20% of the samples were set aside to test prediction and 80% were used to develop calibration models. Depending on the number of samples per watershed, cross validation or independent validation were used. The stability of models was evaluated using coefficient of determination (R2), root mean square error (RMSE), and the ratio performance deviation (RPD). The R2 (%), RMSE (%), and RPD, respectively, for validation were Anjeni (88, 0.44, 3.05), Bale (86, 0.52, 2.7), Basketo (89, 0.57, 3.0), Benishangul (91, 0.30, 3.4), Kersa (82, 0.44, 2.4), Kola tembien (75, 0.44, 1.9), Maybar (84. 0.57, 2.5), Megech (85, 0.15, 2.6), and Wondo Genet (86, 0.52, 2.7) indicating that the models were stable. Models performed better for areas with high SOC values than areas with lower SOC values. Overall, soil spectroscopy performance ranged from very good to good.

scholarly journals Chemometrics assisted spectrophotometric method for simultaneous determination of paracetamol and caffeine in pharmaceutical formulations

2019 ◽  
Vol 54 (3) ◽  
pp. 215-222 ◽  
Author(s):  
MN Uddin ◽  
A Mondol ◽  
MM Karim ◽  
RA Jahan ◽  
AA Rana
Keyword(s):  
Model Development ◽  
Pharmaceutical Formulations ◽  
Uv Spectra ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Orthogonal Experimental Design ◽  
Standard Samples ◽  
Drug Concentrations ◽  
Artificial Neural Network Ann

In this study, two chemometric techniques, partial least-square regression (PLSR) and artificial neural network (ANN) were developed and compared for the simultaneous assay of paracetamol (PCT) and caffeine (CAF) in pharmaceutical formulations by using spectrophotometric data. Six different concentrations of paracetamol and caffeine were considered to make mixture solutions of standard samples by using orthogonal experimental design (OED). UV spectra of these mixtures were recorded in the wavelength range of 205-300 nm versus a solvent blank and digitized absorbance was sampled at 1 nm intervals. Drug concentrations and instrumental spectra of 36 mixture solutions were used for model development and validation and finally 6 commercially available tablets were used to test the developed models. ANN shows better prediction efficiencies than that of PLSR with R2 value 99.28% for prediction and 99.13% for validation set. These two models were successfully applied to commercial pharmaceutical formulations, and it is found by ANN that the drugs contain 75 to 86% of paracetamol and 77 to 92% of caffeine of their label claim. Either of the proposed methods is simple and rapid and can be easily used in the quality assessment of drugs as an alternative analytical method. Bangladesh J. Sci. Ind. Res.54(3), 215-222, 2019

scholarly journals Prediction of Soil Texture Distributions by using PLSR and Reflectance Spectroscopy

2019 ◽  
Vol 8 (3) ◽  
pp. 7876-7881
Keyword(s):  
Statistical Analysis ◽  
Soil Texture ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Principal Component ◽  
Reflectance Spectroscopy ◽  
Soil Samples ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression

The texture of soil i.e. Sand, Silt and Clay are the most important physical properties of soil for agricultural management. In the agricultural practices to increase the productivity of soil, moisture-holding capacity, aeration and to support the agronomic decisions the knowledge of soil texture is an essential task. For this purpose, the present research gives better results and fast acquisition of soil information with the use of Visible and Near Infrared (Vis- NIR) Diffuse Reflectance Spectroscopy. A total of 30 soil samples from two different locations from Aurangabad, Maharashtra, India were collected and analyzed for soil texture. To detect the soil texture the Vis-NIR DRS has shown levels of accurate results compared to the traditional laboratory method with less time, cost and effort. To measure the reflectance of soil the ASD FieldSpec4 Spectroradiometer (350-2500nm) was used. By the observation of captured spectra by using Spectroradiometer it showed that on the basis of different textural classes the soil samples could be spectrally separable. For database collection and pre-processing, we have used RS3 and ViewSpec Pro software respectively. The statistical analysis by using the combination of Principal Component Analysis (PCA) and Partial Least Square Regression method gives accurate results. To determine the texture of soil sample thirteen features were calculated. The main goal of this research was to determine the soil texture by using statistical methods and to test the performance of VNIR-SWIR reflectance spectroscopy by using the ASD FieldSpec4 Spectroradiometer for estimation of the texture of the soil. The results showed that R2 = 0.99 gives maximum accuracy for clay content and R2 = 0.988 for silt content and R2 = 0.989 for sand. The Root Mean Square Values (RMSE) for clay, silt, and sand are 0.02392, 0.02399 and 0.02289 respectively. With the use of reflectance spectroscopy and statistical analysis by using regression models we can determine the soil properties accurately in very less time.

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