2D-QSAR model development and analysis on variant groups of anti-tuberculosis drugs

A quantitative structure activity relationship study was performed on different groups of anti-tuberculosis drug compound for establishing quantitative relationship between biological activity and their physicochemical /structural properties. In recent years, a large number of herbal drugs are promoted in treatment of tuberculosis especially due to the emergence of MDR (multi drug resistance) and XDR (extensive drug resistance) tuberculosis. Multidrug-resistant TB (MDR-TB) is resistant to front-line drugs (isoniazid and rifampicin, the most powerful anti-TB drugs) and extensively drug-resistant TB (XDR-TB) is resistant to front-line and second-line drugs. The possibility of drug resistance TB increases when patient does not take prescribed drugs for defined time period. Natural products (secondary metabolites) isolated from the variety of sources including terrestrial and marine plants and animals, and microorganisms, have been recognized as having antituberculosis action and have recently been tested preclinically for their growth inhibitory activity towards Mycobacterium tuberculosis or related organisms. A quantitative structure activity relationship (QSAR) studies were performed to explore the antituberculosis compound from the derivatives of natural products . Theoretical results are in accord with the in vitro experimental data with reported growth inhibitory activity towards Mycobacterium tuberculosis or related organisms. Antitubercular activity was predicted through QSAR model, developed by forward feed multiple linear regression method with leave-one-out approach. Relationship correlating measure of QSAR model was 74% (R(2) = 0.74) and predictive accuracy was 72% (RCV(2) = 0.72). QSAR studies indicate that dipole energy and heat of formation correlate well with anti-tubercular activity. These results could offer useful references for understanding mechanisms and directing the molecular design of new lead compounds with improved anti-tubercular activity. The generated QSAR model revealed the importance of structural, thermodynamic and electro topological parameters. The quantitative structure activity relationship provides important structural insight in designing of potent antitubercular agent.     

2 D - QSAR studies on CYP26A1 inhibitory activity of 1-[benzofuran-2-yl-(4-alkyl/aryl-phenyl)-methyl]- 1 H-triazoles

The Quantitative Structure Activity Relationship (QSAR) study is performed over a set of 15, 4-alkyl/aryl-substituted 1- [benzofuran-2-yl-phenylmethyl]-1 H-triazoles derivatives. This study is based on the application of physicochemical parameters in QSAR. The parameters include (MR (molar refractivity), MW (molecular weight), Pc (parachor), St (surface tension), D (density), Ir (index of refraction) and log P (partition coefficient). The parameters describing physiochemical properties are used as independent variables and the biological activity (IC(50)) is considered as dependent variable in multiple regression analysis. Different models were generated with high co-efficient of determination (R(2)). The 2D-QSAR study identified compounds capable of inhibiting the metabolic breakdown of the retinoid (trans-retinoic acid (ATRA)) involved in the activation of specific nuclear Retinoic acid receptors (RARs). This study identifies R115866 as a potential inhibitor of the cytochrome P450 (CYP) mediated metabolism with increased RA levels for retinoid actions.     

Pharmacophore, QSAR, and ADME based semisynthesis and in vitro evaluation of ursolic acid analogs for anticancer activity

In the present work, QSAR models for predicting the activities of ursolic acid analogs against human lung (A-549) and CNS (SF-295) cancer cell lines were developed by a forward stepwise multiple linear regression method using a leave-one-out approach. The regression coefficient (r(2)) and the cross-validation regression coefficient (rCV(2)) of the QSAR model for cytotoxic activity against the human lung cancer cell line (A-549) were 0.85 and 0.80, respectively. The QSAR study indicated that the LUMO energy, ring count, and solvent-accessible surface area were strongly correlated with anticancer activity. Similarly, the QSAR model for cytotoxic activity against the human CNS cancer cell line (SF-295) also showed a high correlation (r(2) = 0.99 and rCV(2) = 0.96), and indicated that dipole vector and solvent-accessible surface area were strongly correlated with activity. Ursolic acid analogs that were predicted to be active against these cancer cell lines by the QSAR models were semisynthesized and characterized on the basis of their (1)H and (13)C NMR spectroscopic data, and were then tested in vitro against the human lung (A-549) and CNS (SF-295) cancer cell lines. The experimental results obtained agreed well with the predicted values.     

QSAR-based prediction of anti-HCV activity of thiourea derivatives

Quantitative structure–activity relationship (QSAR) studies were performed on a series of thioureas to explore the physico-chemical parameters responsible for their activity against the hepatitis C virus (HCV)-infected AVa5 cell. The physico-chemical parameters were calculated using WIN CAChe 6.1. Multiple linear regression analysis, after the variables selection by factor analysis, was performed to derive QSAR models which were further evaluated for their statistical significance and predictive power by internal and external validation. The developed QSAR model had the correlation coefficient (R) = 0.928 and cross-validated squared correlation coefficient (Q ²) = 0.751. The selected significant QSAR model indicates that hydrophobicity, dielectric energy, valence connectivity index (order 1), conformational minimum energy and highest occupied molecular orbital of the whole molecule play an important role in the anti-HCV activity of thioureas.     

155 3D QSAR and protein–protein interaction studies on neuraminidase against Clostridium perfringens: An approach toward target identification using structure-based drug designing

The rapid onset of resistance to new drugs and emergence of antibiotic resistant bacteria has led to resurgence in life-threatening bacterial infections. These problems have revitalized interest in antibiotics and lead to new research. To gain further insight between structural and biological activity of Clostridium perfringens, a gram-positive anaerobe responsible for food poisoning, mynecrosis in wound infections, and enterotoxemia in humans. We have considered various in silico approaches for developing new drug leads, based on small ligand structure. The importance of neuraminidases in the virulence of C. perfringens makes it a potent target for the studies of drug designing against this microbe. Natural products or their direct derivatives play crucial roles in many diseases. In the present study, 3D QSAR analysis using kNN-MFA method was performed on a series of pterocarpan derivatives as Clostridial neuraminidase inhibitors. Twenty-five compounds using random selection and sphere exclusion method for the division of dataset into training and test set were chosen. kNN-MFA methodology with stepwise, simulated annealing and genetic algorithm was used for model building and four predictive models have been generated. The most significant model has a high internal predictivity of 64.80% (q ²?=?0.6480) and an external predictivity of 95.46% (r ²?=?0.9546). Model showed that electrostatic and steric interactions play important role in determining neuraminidase inhibitory activity. The kNN-MFA plots provide further understanding of the relationship between structural features of substituted pterocarpan derivatives and their activities which were applied for designing new compounds as inhibitors. The drug likeliness of these compounds was checked through ADME property prediction and their interaction with the neuraminidase was checked by molecular docking studies. Moreover, analysis of protein–protein interaction network of NanI in C. perfringens was done.     

2D- and 3D-QSAR modelling,molecular docking and in vitro evaluation studies on 18β-glycyrrhetinic acid derivatives against triple-negative breast cancer cell line

Abstract

Triple-negative breast cancers (TNBCs) are one of the most aggressive and complex forms of cancers in women. TNBCs are commonly known for their complex heterogeneity and poor prognosis. The present work aimed to develop a predictive 2D and 3D quantitative structure–activity relationship (QSAR) models against metastatic TNBC cell line. The 2D-QSAR was based on multiple linear regression analysis and validated by Leave-One-Out (LOO) and external test set prediction approach. QSAR model presented regression coefficient values for training set (r²), LOO-based internal regression (q²) and external test set regression (pred_r²) which are 0.84, 0.82 and 0.75, respectively. Five properties, Epsilon4 (electronegativity), ChiV3cluster (valence molecular connectivity index), chi3chain (retention index for three-membered ring), TNN5 (nitrogen atoms separated through 5 bond distance) and nitrogen counts, were identified as important structural features responsible for anticancer activity of MDA-MB-231 inhibitors. Five novel derivatives of glycyrrhetinic acid (GA) named GA-1, GA-2, GA-3, GA-4 and GA-5 were semi-synthesised and screened through the QSAR model. Further, in vitro activities of the derivatives were analysed against human TNBC cell line, MDA-MB-231. The result showed that GA-1 exhibits improved cytotoxic activity to that of parent compound (GA). Further, atomic property field (APF)-based 3D-QSAR and scoring recognise C-30 carboxylic group of GA-1 as major influential factor for its anticancer activity. The significance of C-30 carboxylic group in GA derivatives was also confirmed by molecular docking study against cancer target glyoxalase-I. Finally, the oral bioavailability and toxicity of GA-1 were assessed by computational ADMET studies.

Communicated by Ramaswamy H. Sarma     

Human protein tyrosine phosphatase 1B inhibitors: QSAR by genetic function approximation

Protein tyrosine phosphatase 1B (PTP 1B), a negative regulator of insulin receptor signaling system, has emerged as a highly validated, attractive target for the treatment of non-insulin dependent diabetes mellitus (NIDDM) and obesity. As a result there is a growing interest in the development of potent and specific inhibitors for this enzyme. This quantitative structure-activity relationship (QSAR) study for a series of formylchromone derivatives as PTP lB inhibitors was performed using genetic function approximation (GFA) technique. The QSAR models were developed using a training set of 29 compounds and the predictive ability of the QSAR model was evaluated against a test set of 7 compounds. The internal and external consistency of the final QSAR model was 0.766 and 0.785. The statistical quality of QSAR models was assessed by statistical parameters r2, r2 (crossvalidated r2), r2pred (predictive r2) and lack of fit (LOF) measure. The results indicate that PTP lB inhibitory activity of the formylchromone derivatives is strongly dependent on electronic, thermodynamic and shape related parameters.     

蜂毒溶血肽类似物定量构效关系计算分析

采用HyperChem7．0结构分析软件,对蜂毒溶血肽类似物的分予体积等结构参数进行了计算分析．分别利用多元线性回归、BP-神经网络计算法进行统计分析,获得两个相关性好的QsAR（quantitative structure-function relationship）模型．结果显示,蜂毒肽溶血活性与生成热、键合能、表面积、分予体积、极化能、醇水分配系数、水舍能相关．为降低溶血作用,指出在设计蜂毒肽结构时应尽量避免螺旋状结构．少用疏水性氨基酸．     

Prediction of HIV-1 protease inhibitory activity of 4-hydroxy-5,6-dihydropyran-2-ones: QSAR study

Inhibition of human immunodeficiency virus 1 (HIV-1) protease is an important strategy for the treatment of HIV and acquired immune deficiency syndrome (AIDS). Therefore, HIV-1 protease inhibitory activity of dihydropyranone derivatives has been analyzed with different physico-chemical parameters. In the present work, QSAR studies were performed on a series of 4-hydroxy-5,6-dihydropyran-2-ones to explore the physico-chemical parameters responsible for their HIV-1 protease inhibitory activity. Physico-chemical parameters were calculated using WIN CAChe 6.1. Stepwise multiple linear regression analysis was performed to derive QSAR models which were further evaluated for statistical significance and predictive power by internal and external validation. The selected best QSAR model was having correlation coefficient (R)?=?0.875 and cross-validated squared correlation coefficient (Q²)?=?0.707. The developed significant QSAR model indicates that hydrophobicity of whole molecule and the substituent present at sixth position of dihydropyranones play an important role in the HIV-1 protease inhibitory activities of 4-hydroxy-5,6-dihydropyran-2-ones.     

3D QSAR,pharmacophore and molecular docking studies of known inhibitors and designing of novel inhibitors for M18 aspartyl aminopeptidase of <Emphasis Type="Italic">Plasmodium falciparum</Emphasis>

Background

The Plasmodium falciparum M18 Aspartyl Aminopeptidase (PfM18AAP) is only aspartyl aminopeptidase which is found in the genome of P. falciparum and is essential for its survival. The PfM18AAP enzyme performs various functions in the parasite and the erythrocytic host such as hemoglobin digestion, erythrocyte invasion, parasite growth and parasite escape from the host cell. It is a valid target to develop antimalarial drugs. In the present work, we employed 3D QSAR modeling, pharmacophore modeling, and molecular docking to identify novel potent inhibitors that bind with M18AAP of P. falciparum.

Results

The PLSR QSAR model showed highest value for correlation coefficient r² (88 %) and predictive correlation coefficient (pred_r2) =0.6101 for external test set among all QSAR models. The pharmacophore modeling identified DHRR (one hydrogen donor, one hydrophobic group, and two aromatic rings) as an essential feature of PfM18AAP inhibitors. The combined approach of 3D QSAR, pharmacophore, and structure-based molecular docking yielded 10 novel PfM18AAP inhibitors from ChEMBL antimalarial library, 2 novel inhibitors from each derivative of quinine, chloroquine, 8-aminoquinoline and 10 novel inhibitors from WHO antimalarial drugs. Additionally, high throughput virtual screening identified top 10 compounds as antimalarial leads showing G-scores -12.50 to -10.45 (in kcal/mol), compared with control compounds(G-scores -7.80 to -4.70) which are known antimalarial M18AAP inhibitors (AID743024). This result indicates these novel compounds have the best binding affinity for PfM18AAP.

Conclusion

The 3D QSAR models of PfM18AAP inhibitors provided useful information about the structural characteristics of inhibitors which are contributors of the inhibitory potency. Interestingly, In this studies, we extrapolate that the derivatives of quinine, chloroquine, and 8-aminoquinoline, for which there is no specific target has been identified till date, might show the antimalarial effect by interacting with PfM18AAP.

     

Prediction of biological activity of Aurora-A kinase inhibitors by multilinear regression analysis and support vector machine

Several QSAR (quantitative structure-activity relationships) models for predicting the inhibitory activity of 117 Aurora-A kinase inhibitors were developed. The whole dataset was split into a training set and a test set based on two different methods, (1) by a random selection; and (2) on the basis of a Kohonen’s self-organizing map (SOM). Then the inhibitory activity of 117 Aurora-A kinase inhibitors was predicted using multilinear regression (MLR) analysis and support vector machine (SVM) methods, respectively. For the two MLR models and the two SVM models, for the test sets, the correlation coefficients of over 0.92 were achieved.