Combinatorial Chemistry & High Throughput Screening - Volume 18, Issue 8, 2015
Volume 18, Issue 8, 2015
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Near Infrared Spectroscopic Combined with Partial Least Squares and Radial Basis Function Neural Network to Analyze Paclitaxel Concentration in Rat Plasma
Authors: Gaoyang Xing, Jiaming Cao, Di Wang, Jia Song, Jia-hui Lu, Qing-fan Meng, Guodong Yan and Le-sheng TengPaclitaxel is known as one of the most effective anticancer drugs. Near Infrared Spectroscopy (NIRS), a rapid, precise and non-destructive approach of analysis, has been widely used for qualitative and quantitative detection. The present study aims to analyze the plasma paclitaxel concentration with NIRS. Various batches of plasma samples were prepared and the concentration of paclitaxel was determined via high performance liquid chromatography tandem mass spectrometry (LC-MS/MS). The outliers and the number of calibration set were confirmed by Monte Carlo algorithm combined with partial least squares (MCPLS). Since NIR spectra may be contaminated by signals from background and noise, a series of preprocessing were performed to improve signal resolution. Moving window PLS and radical basis function neural network (RBFNN) methods were applied to establish calibration model. Although both PLS and RBFNN models are well-fitting, RBFNN-established model displayed better qualities on stability and predictive ability. The correlation coefficients of calibration curve and prediction set (Rc2 and Rp2) are 0.9482 and 0.9544, respectively. Moreover, independent verification test with 20 samples confirmed the well predictive ability of RBFNN model.
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Classification of natural estrogen-like isoflavonoids and diphenolics by QSAR tools
Authors: Feng Luan, Yuxi Lu, Huitao Liu and Maria N.D.S. CordeiroThis work reports a detailed study of the ability of linear and non-linear classification methods to estimate the estrogenic activities of a series of 55 natural estrogen-like isoflavonoid and diphenolic compounds. In doing so, we examined the use of linear discriminant analysis (LDA) and nonlinear support vector machines (SVMs) techniques along with feature selection algorithms. The structural characteristics of each of the studied compounds were calculated from the optimized molecular geometries. Both the LDA and SVMs models contain four descriptors, however, the SVMs model (total accuracy 89.1%) was found to be superior to the LDA model (total accuracy 80.0%). The analysis of molecular descriptors within our models provided essential insights towards a better understanding of the estrogenic mechanisms of natural estrogen-like phytoestrogens. Furthermore, the derived models can be applied in the future screening of other natural estrogen-like compounds.
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Chemometrics-assisted Spectrofluorimetric Determination of Two Co-administered Drugs of Major Interaction, Methotrexate and Aspirin, in Human Urine Following Acid-induced Hydrolysis
Authors: Hadir M. Maher, Marwa A.A. Ragab and Eman I. El-KimaryMethotrexate (MTX) is widely used to treat rheumatoid arthritis (RA), mostly along with non-steroidal anti-inflammatory drugs (NSAIDs), the most common of which is aspirin or acetyl salicylic acid (ASA). Since NSAIDs impair MTX clearance and increase its toxicity, it was necessary to develop a simple and reliable method for the monitoring of MTX levels in urine samples, when coadministered with ASA. The method was based on the spectrofluorimetric measurement of the acid-induced hydrolysis product of MTX, 4-amino-4-deoxy-10-methylpteroic acid (AMP), along with the strongly fluorescent salicylic acid (SA), a product of acid-induced hydrolysis of aspirin and its metabolites in urine. The overlapping emission spectra were resolved using the derivative method (D method). In addition, the corresponding derivative emission spectra were convoluted using discrete Fourier functions, 8-points sin xi polynomials, (D/FF method) for better elimination of interferences. Validation of the developed methods was carried out according to the ICH guidelines. Moreover, the data obtained using derivative and convoluted derivative spectra were treated using the non-parametric Theil's method (NP), compared with the least-squares parametric regression method (LSP). The results treated with Theil's method were more accurate and precise compared with LSP since the former is less affected by the outliers. This work offers the potential of both derivative and convolution using discrete Fourier functions in addition to the effectiveness of using the NP regression analysis of data. The high sensitivity obtained by the proposed methods was promising for measuring low concentration levels of the two drugs in urine samples. These methods were efficiently used to measure the drugs in human urine samples following their co-administration.
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Bio-AIMS Collection of Chemoinformatics Web Tools based on Molecular Graph Information and Artificial Intelligence Models
Authors: Cristian R. Munteanu, Humberto Gonzalez-Diaz, Rafael Garcia, Mabel Loza and Alejandro PazosThe molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction models for specific biological properties of molecules. These models connect the molecular structure information such as atom connectivity (molecular graphs) or physical-chemical properties of an atom/group of atoms to the molecular activity (Quantitative Structure - Activity Relationship, QSAR). Due to the complexity of the proteins, the prediction of their activity is a complicated task and the interpretation of the models is more difficult. The current review presents a series of 11 prediction models for proteins, implemented as free Web tools on an Artificial Intelligence Model Server in Biosciences, Bio-AIMS (http://bio-aims.udc.es/TargetPred.php). Six tools predict protein activity, two models evaluate drug - protein target interactions and the other three calculate protein - protein interactions. The input information is based on the protein 3D structure for nine models, 1D peptide amino acid sequence for three tools and drug SMILES formulas for two servers. The molecular graph descriptor-based Machine Learning models could be useful tools for in silico screening of new peptides/proteins as future drug targets for specific treatments.
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3D-QSAR analysis of MCD inhibitors by CoMFA and CoMSIA
Authors: Eslam Pourbasheer, Reza Aalizadeh, Amin Ebadi and Mohammad R. GanjaliThree-dimensional quantitative structure-activity relationship was developed for the series of compounds as malonyl-CoA decarboxylase antagonists (MCD) using the CoMFA and CoMSIA methods. The statistical parameters for CoMFA (q2=0.558, r2=0.841) and CoMSIA (q2= 0.615, r2 = 0.870) models were derived based on 38 compounds as training set in the basis of the selected alignment. The external predictive abilities of the built models were evaluated by using the test set of nine compounds. From obtained results, the CoMSIA method was found to have highly predictive capability in comparison with CoMFA method. Based on the given results by CoMSIA and CoMFA contour maps, some features that can enhance the activity of compounds as MCD antagonists were introduced and used to design new compounds with better inhibition activity.
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QSAR Analysis of Some Antagonists for p38 map kinase Using Combination of Principal Component Analysis and Artificial Intelligence
Authors: Elham Doosti and Mohsen ShahlaeiQuantitative relationships between structures of a set of p38 map kinase inhibitors and their activities were investigated by principal component regression (PCR) and principal componentartificial neural network (PC-ANN). Latent variables (called components) generated by principal component analysis procedure were applied as the input of developed Quantitative structure- activity relationships (QSAR) models. An exact study of predictability of PCR and PC-ANN showed that the later model has much higher ability to calculate the biological activity of the investigated molecules. Also, experimental and estimated biological activities of compounds used in model development step have indicated a good correlation. Obtained results show that a non-linear model explaining the relationship between the pIC50s and the calculated principal components (that extract from structural descriptors of the studied molecules) is superior than linear model. Some typical figures of merit for QSAR studies explaining the accuracy and predictability of the suggested models were calculated. Therefore, to design novel inhibitors of p38 map kinase with high potency and low undesired effects the developed QSAR models were used to estimate biological pIC50 of the studied compounds.
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RepurposeVS: A Drug Repurposing-Focused Computational Method for Accurate Drug-Target Signature Predictions
Authors: Naiem T. Issa, Oakland J. Peters, Stephen W. Byers and Sivanesan DakshanamurthyWe describe here RepurposeVS for the reliable prediction of drug-target signatures using X-ray protein crystal structures. RepurposeVS is a virtual screening method that incorporates docking, drug-centric and protein-centric 2D/3D fingerprints with a rigorous mathematical normalization procedure to account for the variability in units and provide high-resolution contextual information for drug-target binding. Validity was confirmed by the following: (1) providing the greatest enrichment of known drug binders for multiple protein targets in virtual screening experiments, (2) determining that similarly shaped protein target pockets are predicted to bind drugs of similar 3D shapes when RepurposeVS is applied to 2,335 human protein targets, and (3) determining true biological associations in vitro for mebendazole (MBZ) across many predicted kinase targets for potential cancer repurposing. Since RepurposeVS is a drug repurposing-focused method, benchmarking was conducted on a set of 3,671 FDA approved and experimental drugs rather than the Database of Useful Decoys (DUDE) so as to streamline downstream repurposing experiments. We further apply RepurposeVS to explore the overall potential drug repurposing space for currently approved drugs. RepurposeVS is not computationally intensive and increases performance accuracy, thus serving as an efficient and powerful in silico tool to predict drug-target associations in drug repurposing.
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Application of Multivariate Linear and Nonlinear Calibration and Classification Methods in Drug Design
Authors: Azizeh Abdolmaleki, Jahan B. Ghasemi, Fereshteh Shiri and Somayeh PirhadiData manipulation and maximum efficient extraction of useful information need a range of searching, modeling, mathematical, and statistical approaches. Hence, an adequate multivariate characterization is the first necessary step in investigation and the results are interpreted after multivariate analysis. Multivariate data analysis is capable of not only large dataset management but also interpret them surely and rapidly. Application of chemometrics and cheminformatics methods may be useful for design and discovery of new drug compounds. In this review, we present a variety of information sources on chemometrics, which we consider useful in different fields of drug design. This review describes exploratory analysis (PCA), classification and multivariate calibration (PCR, PLS) methods to data analysis. It summarizes the main facts of linear and nonlinear multivariate data analysis in drug discovery and provides an introduction to manipulation of data in this field. It handles the fundamental aspects of basic concepts of multivariate methods, principles of projections (PCA and PLS) and introduces the popular modeling and classification techniques. Enough theory behind these methods, more particularly concerning the chemometrics tools is included for those with little experience in multivariate data analysis techniques such as PCA, PLS, SIMCA, etc. We describe each method by avoiding unnecessary equations, and details of calculation algorithms. It provides a synopsis of the method followed by cases of applications in drug design (i.e., QSAR) and some of the features for each method.
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Acute and subchronic toxicity studies on safety assessment of Paecilomyces tenuipes N45 extracts
Authors: Linna Du, Yan Liu, Chungang Liu, Qingfan Meng, Jingjing Song, Di Wang, Jiahui Lu, Lirong Teng, Yulin Zhou and Lesheng TengPaecilomyces tenuipes, one of the commonly used Chinese medicinal fungus, has received much attention over the world, which possesses various active compounds and biological activities. However, little toxicological information is available. Therefore, the present study evaluated the potential toxicity of aqueous and ethanol extracts of Paecilomyces tenuipes N45 via acute and subchronic administration in mouse and rat, respectively. For improving the extraction rate of aqueous extract, response surface methodology (RSM) was employed to optimize the extraction condition first in this paper. The obtained optimal extract conditions were temperature 80 °C, liquid-solid ratio 50 mL·g-1 and time 3 h. In the acute toxicity test, aqueous and ethanol extracts caused neither mortality nor toxicological signs, and the maximum tolerance dose was estimated over 15 g/kg. No mortality or adverse effects was observed in subchronic toxicity studies. No significant difference in bodyweight, relative organ weight or hematological parameters was noted during the experiment. Comparing with nontreated rats, ALT, K and BUN levels were changed in experimental group detecting via biochemical analysis. No abnormality of internal organs was noted between treatment and control groups in gross and histopathological examinations. Our present study suggested that the tolerance dose of the Paecilomyces tenuipes N45 extracts were more than 15 g/kg and no-observed-adverse-effect level (NOAEL) of the extracts for both male and female rats after 90-day adminstation. Additionally, the extracts may possess renal-protective and hepato-protective effects.
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Artificial Neural Network Methods Applied to Drug Discovery for Neglected Diseases
Among the chemometric tools used in rational drug design, we find artificial neural network methods (ANNs), a statistical learning algorithm similar to the human brain, to be quite powerful. Some ANN applications use biological and molecular data of the training series that are inserted to ensure the machine learning, and to generate robust and predictive models. In drug discovery, researchers use this methodology, looking to find new chemotherapeutic agents for various diseases. The neglected diseases are a group of tropical parasitic diseases that primarily affect poor countries in Africa, Asia, and South America. Current drugs against these diseases cause side effects, are ineffective during the chronic stages of the disease, and are often not available to the needy population, have relative high toxicity, and face developing resistance. Faced with so many problems, new chemotherapeutic agents to treat these infections are much needed. The present review reports on neural network research, which studies new ligands against Chagas’ disease, sleeping sickness, malaria, tuberculosis, and leishmaniasis; a few of the neglected diseases.
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Volumes & issues
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Volume 28 (2025)
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Volume 27 (2024)
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Volume 26 (2023)
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Volume 25 (2022)
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Volume 24 (2021)
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Volume 23 (2020)
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Volume 22 (2019)
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Volume 21 (2018)
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Volume 20 (2017)
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Volume 19 (2016)
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Volume 18 (2015)
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Volume 17 (2014)
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Volume 16 (2013)
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Volume 15 (2012)
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Volume 14 (2011)
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Volume 13 (2010)
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Volume 12 (2009)
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Volume 11 (2008)
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Volume 10 (2007)
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Volume 9 (2006)
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Volume 8 (2005)
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Volume 7 (2004)
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Volume 6 (2003)
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Volume 5 (2002)
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Volume 4 (2001)
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Volume 3 (2000)
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