One pot synthesis of pyrrolidines type 3,7-diazabicyclo [3.3.0] octane and biological activity

Pyrrolidines type 2,4-disubstituted (alkyl, aryl or heteroaryl)-6,8-dioxo-3,7-diazabicyclo [3.3.0] octanes (8a-d) were successfully synthesized by an efficient one pot 1,3-dipolar cycloaddition of azomethine ylides (in situ generated from the reaction of aromatic aldehydes and methyl ester of alpha-amino acids) with dipolarophile (N-phenylmaleimide). The reaction of compounds 8a-d with hydrazine in ethanol at room temperature took place under nucleophilic substitution which furnished 5-amino-4,6-dioxo-octahydropyrrolo [3,4-b] pyrrole-3-carboxylic acid phenylamides (12a-d). Structures of the products were confirmed by IR and 1H NMR. The compounds (8b and 12a) were evaluated for antimicrobial (agar dilution method) and antioxidative (DPPH; 2,2-diphenyl-1-picrylhydrazyl and SOD; superoxide dismutase assays) activities. The results showed that at concentrations of 4-256 µg/mL, the tested compounds exhibited non-significant antimicrobial growth, whereas the 12a at 200 µg/mL began to exert some antioxidative activity

A new sulfoxide analog of 1,2,3,6-tetrahydrophenylpyridine and antimicrobial activity

Bioactivities of thiotetrahydropyridines were previously described. Herein, a novel bioactive sulfoxide analog; N-acetyl-2-(1-adamantylsulfoxo)-3-acetoxy-4-phenyl-6-hydroxy-1,2,3,6-tetrahydropyridine (3) from the deoxydative substitution of 4-phenylpyridine 1-oxide is reported. Its structure was elucidated using spectral data including 2D-NMR, MS, IR and UV. The sulfoxide 3 exhibited antibacterial activity against Moraxella catarrhalis and Streptococcus pyogenes with minimum inhibitory concentration of 128 and 256 μg/mL, respectively

Probing the origins of aromatase inhibitory activity of disubstituted coumarins via QSAR and molecular docking

This study investigated the quantitative structure-activity relationship (QSAR) of imidazole derivatives of 4,7-disubstituted coumarins as inhibitors of aromatase, a potential therapeutic protein target for the treatment of breast cancer. Herein, a series of 3,7- and 4,7-disubstituted coumarin derivatives (1-34) with R1 and R2 substituents bearing aromatase inhibitory activity were modeled as a function of molecular and quantum chemical descriptors derived from low-energy conformer geometrically optimized at B3LYP/6-31G(d) level of theory. Insights on origins of aromatase inhibitory activity was afforded by the computed set of 7 descriptors comprising of F10[N-O], Inflammat-50, Psychotic-80, H-047, BELe1, B10[C-O] and MAXDP. Such significant descriptors were used for QSAR model construction and results indicated that model 4 afforded the best statistical performance. Good predictive performance were achieved as verified from the internal (comprising the training and the leave-one-out cross-validation (LOO-CV) sets) and external sets affording the following statistical parameters: R2Tr = 0.9576 and RMSETr = 0.0958 for the training set; Q2CV = 0.9239 and RMSECV = 0.1304 for the LOO-CV set as well as Q2Ext = 0.7268 and RMSEExt = 0.2927 for the external set. Significant descriptors showed correlation with functional substituents, particularly, R1 in governing high potency as aromatase inhibitor. Molecular docking calculations suggest that key residues interacting with the coumarins were predominantly lipophilic or non-polar while a few were polar and positively-charged. Findings illuminated herein serve as the impetus that can be used to rationally guide the design of new aromatase inhibitors

Data mining for the identification of metabolic syndrome status

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/ understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS

Classification of P-glycoprotein-interacting compounds using machine learning methods

P-glycoprotein (Pgp) is a drug transporter that plays important roles in multidrug resistance and drug pharmacokinetics. The inhibition of Pgp has become a notable strategy for combating multidrug-resistant cancers and improving therapeutic outcomes. However, the polyspecific nature of Pgp, together with inconsistent results in experimental assays, renders the determination of endpoints for Pgp-interacting compounds a great challenge. In this study, the classification of a large set of 2,477 Pgp-interacting compounds (i.e., 1341 inhibitors, 913 noninhibitors, 197 substrates and 26 non-substrates) was performed using several machine learning methods (i.e., decision tree induction, artificial neural network modelling and support vector machine) as a function of their physicochemical properties. The models provided good predictive performance, producing MCC values in the range of 0.739-1 for internal cross-validation and 0.665-1 for external validation. The study provided simple and interpretable models for important properties that influence the activity of Pgp-interacting compounds, which are potentially beneficial for screening and rational design of Pgp inhibitors that are of clinical importance

Quantitative population-health relationship (QPHR) for assessing metabolic syndrome

Background: Metabolic syndrome (MS) is a condition that predisposes individuals to the development of cardiovascular diseases and type 2 diabetes mellitus. Methods: A cross-sectional investigation of 15,365 participants residing in metropolitan Bangkok who had received an annual health checkup in 2007 was used in this study. Individuals were classified as MS or non-MS according to the International Diabetes Federation criteria using BMI cutoff of ≥ 25 kg/m2 plus two or more MS components. This study explores the utility of quantitative population-health relationship (QPHR) for predicting MS status as well as discovers variables that frequently occur together. The former was achieved by decision tree (DT) analysis, artificial neural network (ANN), support vector machine (SVM) and principal component analysis (PCA) while the latter was obtained by association analysis(AA). Results: DT outperformed both ANN and SVM in MS classification as deduced from its accuracy value of 99 % as compared to accuracies of 98 % and 91 % for ANN and SVM, respectively. Furthermore, PCA was able to effectively classify individuals as MS and non-MS as observed from the scores plot. Moreover, AA was employed to analyze individuals with MS in order to elucidate pertinent rule from MS components that occur frequently together, which included TG+BP, BP+FPG and TG+FPG where TG, BP and FPG corresponds to triglyceride, blood pressure and fasting plasma glucose, respectively. Conclusion: QPHR was demonstrated to be useful in predicting the MS status of individuals from an urban Thai population. Rules obtained from AA analysis provided general guidelines (i.e. co-occurrences of TG, BP and FPG) that may be used in the prevention of MS in at risk individuals

Activities of thiotetrahydropyridines as antioxidant and antimicrobial agents

Tetrahydropyridines have been reported previously as important medicinal agents. The present study, thiotetrahydropyridines were prepared and tested for antioxidants (DPPH and SOD assays) and antimicrobials (agar dilution method). The results show that 1-acetyl-1,2,3,4- and 1,2,3,6-thiotetrahydropyridines 15a-b, 16, 17 and 18a are new antioxidants that scavenge superoxide and free radicals. Whereas the analogs 15a and 16 are novel antimicrobials. Significantly, 1-acetyl-2-(1-adamantylthio)-3,4-diacetoxy-1,2,3,4-tetrahydropyridine (15a) is the most potent compound that inhibits the growth of Streptococcus pyogenes and Moraxella catarrhalis with MIC of 32 µg/mL, of Corynebacterium diphtheriae NCTC 10356 and of Vibrio cholerae (MIC of 64 µg/mL). Remarkably, the analog 15a is the most potent antioxidant and antimicrobial agent. This finding reveals a new and unique group of 1-acetyl-1,2,3,4-thiotetrahydropyridines as interesting lead compound with potential to be further developed for medicinal applications

Determining a new formula for calculating low-density lipoprotein cholesterol: data mining approach

Low-density lipoprotein cholesterol (LDL-C) is a risk factor of coronary heart diseases. The estimation of LDL-C (LDL-Cal) level was performed using Friedewald’s equation for triglyceride (TG) level less than 400 mg/dL. Therefore, the aim of this study is to generate a new formula for LDL-Cal and validate the correlation coefficient between LDL-Cal and LDL-C directly measured (LDL-Direct). A data set of 1786 individuals receiving annual medical check-ups from the Faculty of Medical Technology, Mahidol University, Thailand in 2008 was used in this study. Lipid profiles including total cholesterol (TC), TG, high-density lipoprotein cholesterol (HDL-C) and LDL-C were determined using Roche/Hitachi modular system analyzer. The estimated LDL-C was obtained using Friedewald’s equation and the homogenous enzymatic method. The level of TG was divided into 6 groups (TG<200, <300, <400, <500, <600 and < 1000 mg/dL) for constructing the LDL-Cal formula. The pace regression model was used to construct the candidate formula for the LDL-Cal and determine the correlation coefficient (r) with the LDL-Direct. The candidate LDL-Cal formula was generated for 6 groups of TG levels that displayed well correlation between LDL-Cal and LDL-Direct. Interestingly, The TG level was less than 1000 mg/dL, the regression model was able to generate the equation as shown as strong r of 0.9769 with LDL-Direct. Furthermore, external data set (n = 666) with TG measurement (36-1480 mg/dL) was used to validate new formula which displayed high r of 0.971 between LDL-Cal and LDL-direct. This study explored a new formula for LDL-Cal which exhibited higher r of 0.9769 and far beyond the limitation of TG more than 1000 mg/dL and potential used for estimating LDL-C in routine clinical laboratories

Rational design of novel coumarins

Coumarins are well-known for their antioxidant effect and aromatic property, thus, they are one of ingredients commonly added in cosmetics and personal care products. Quantitative structure-activity relationships (QSAR) modeling is an in silico method widely used to facilitate rational design and structural optimization of novel drugs. Herein, QSAR modeling was used to elucidate key properties governing antioxidant activity of a series of the reported coumarin-based antioxidant agents (1-28). Several types of descriptors (calculated from 4 softwares i.e., Gaussian 09, Dragon, PaDEL and Mold2 softwares) were used to generate three multiple linear regression (MLR) models with preferable predictive performance (Q2LOO-CV = 0.813-0.908; RMSELOO-CV = 0.150-0.210; Q2Ext = 0.875-0.952; RMSEExt = 0.104-0.166). QSAR analysis indicated that number of secondary amines (nArNHR), polarizability (G2p), electronegativity (D467, D580, SpMin2_Bhe, and MATS8e), van der Waals volume (D491 and D461), and H-bond potential (SHBint4) are important properties governing antioxidant activity. The constructed models were also applied to guide in silico rational design of an additional set of 69 structurally modified coumarins with improved antioxidant activity. Finally, a set of 9 promising newly design compounds were highlighted for further development. Structure-activity analysis also revealed key features required for potent activity which would be useful for guiding the future rational design. In overview, our findings demonstrated that QSAR modeling could possibly be a facilitating tool to enhance successful development of bioactive compounds for health and cosmetic applications