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Syed Zahir Idid Syed Osman Idid - One of the best experts on this subject based on the ideXlab platform.
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prediction of anticancer Activity of aliphatic nitrosoureas using quantum chemical quantitative Structure Activity Relation qsar methods
African Journal of Biotechnology, 2011Co-Authors: Ibrahim Ali Noorbatcha, F Hamzah, Hamzah Mohd Salleh, Syed Zahir Idid Syed Osman IdidAbstract:Design and development of new anticancer drugs with low toxicity is a very challenging task and computer aided methods are being increasingly used to solve this problem. In this study, we investigated the anticancer Activity of aliphatic nitrosoureas using quantum chemical quantitative Structure Activity Relation (QSAR) approach. In this method, the physic-chemical properties, known as descriptors, necessary for predicting quantitative Structure Activity Relations was obtained from semi empirical quantum chemical methods. We used Recife Model 1 to optimize the Structure of the molecules and to calculate the quantum chemical descriptors, while heuristic and best multilinear regression methods were applied to obtain the best corRelation. Two data sets containing aliphatic nitrosoureas and chloroethyl substituted nitrosoureas were used in the present calculations. The QSAR equations obtained here can be used to design new anticancer drugs prior to resorting to experimental Activity studies.
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Prediction of anticancer Activity of aliphatic nitrosoureas using quantum chemical quantitative Structure Activity Relation (QSAR) methods
'Academic Journals', 2011Co-Authors: Noorbatcha, Ibrahim Ali, Hamzah F., Mohd. Salleh Hamzah, Syed Zahir Idid Syed Osman IdidAbstract:Design and development of new anticancer drugs with low toxicity is a very challenging task and computer aided methods are being increasingly used to solve this problem. In this study, we investigated the anticancer Activity of aliphatic nitrosoureas using quantum chemical quantitative Structure Activity Relation (QSAR) approach. In this method, the physic-chemical properties, known as descriptors, necessary for predicting quantitative Structure Activity Relations was obtained from semi empirical quantum chemical methods. We used Recife Model 1 to optimize the Structure of the molecules and to calculate the quantum chemical descriptors, while heuristic and best multilinear regression methods were applied to obtain the best corRelation. Two data sets containing aliphatic nitrosoureas and chloroethyl substituted nitrosoureas were used in the present calculations. The QSAR equations obtained here can be used to design new anticancer drugs prior to resorting to experimental Activity studies. Key words: Quantitative Structure Activity Relationship (QSAR), best multi linear regression (BMLR), quantum chemical method, Recife Model 1 (RM1)
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Prediction of anticancer Activity of Aliphatic Nitrosoureas using Quantum Chemical QSAR methods
2011Co-Authors: Noorbatcha, Ibrahim Ali, Hamzah F., Mohd. Salleh Hamzah, Syed Zahir Idid Syed Osman IdidAbstract:Design and development of new anticancer drugs with low toxicity is a very challenging task and computer aided methods are being increasingly used to solve this problem. In this research we investigate the anticancer Activity of aliphatic nitrosoureas using quantum chemical quantitative Structure Activity Relation (qcQAR) approach. In this method the physic-chemical properties, known as descriptors, necessary for predicting quantitative Structure Activity Relations is obtained from semi empirical quantum chemical methods. We have used Recife Model 1 (RM1) to optimize the Structure of the molecules and to calculate the quantum chemical descriptors, while heuristic and best multilinear regression methodswere applied to obtain the best corRelation. Two data sets containing aliphatic nitrosoureas and chloroethyl substituted nitrosoureas, are used in the present calculations. The best QSAR equations obtained here can be used to design new anticancer drugs prior to resorting to experimental Activity studies
Steffen Hering - One of the best experts on this subject based on the ideXlab platform.
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nitrogenated honokiol derivatives allosterically modulate gabaa receptors and act as strong partial agonists
Bioorganic & Medicinal Chemistry, 2015Co-Authors: Marketa Bernaskova, Angela Schoeffmann, Wolfgang Schuehly, Antje Hufner, Igor Baburin, Steffen HeringAbstract:In traditional Asian medicinal systems, preparations of the root and stem bark of Magnolia species are widely used to treat anxiety and other nervous disturbances. The biphenyl-type neolignan honokiol together with its isomer magnolol are the main constituents of Magnolia bark extracts. We have previously identified a nitrogen-containing honokiol derivative (3-acetylamino-4'-O-methylhonokiol, AMH) as a high efficient modulator of GABAA receptors. Here we further elucidate the Structure-Activity Relation of a series of nitrogenated biphenyl-neolignan derivatives by analysing allosteric modulation and agonistic effects on α1β2γ2S GABAA receptors. The strongest IGABA enhancement was induced by compound 5 (3-acetamido-4'-ethoxy-3',5-dipropylbiphenyl-2-ol, Emax: 123.4±9.4% of IGABA-max) and 6 (5'-amino-2-ethoxy-3',5-dipropylbiphenyl-4'-ol, Emax: 117.7±13.5% of IGABA-max). Compound 5 displayed, however, a significantly higher potency (EC50=1.8±1.1 μM) than compound 6 (EC50=20.4±4.3 μM). Honokiol, AMH and four of the derivatives induced significant inward currents in the absence of GABA. Strong partial agonists were honokiol (inducing 78±6% of IGABA-max), AMH (63±6%), 5'-amino-2-O-methylhonokiol (1) (59±1%) and 2-methoxy-5'-nitro-3',5-dipropylbiphenyl-4'-ol (3) (52±1%). 3-N-Acetylamino-4'-ethoxy-3',5-dipropyl-biphenyl-4'-ol (5) and 3-amino-4'-ethoxy-3',5-dipropyl-biphenyl-4'-ol (7) were less efficacious but even more potent (5: EC50=6.9±1.0 μM; 7: EC50=33.2±5.1 μM) than the full agonist GABA.
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valerenic acid derivatives as novel subunit selective gabaa receptor ligands in vitro and in vivo characterization
British Journal of Pharmacology, 2010Co-Authors: Sophia Khom, Jurgen Ramharter, Barbara Strommer, Thomas Erker, Gerhard F. Ecker, Christoph Schwarzer, Johann Mulzer, Thomas Schwarz, Steffen HeringAbstract:BACKGROUND AND PURPOSE Subunit-specific modulators of γ-aminobutyric acid (GABA) type A (GABAA) receptors can help to assess the physiological function of receptors with different subunit composition and also provide the basis for the development of new drugs. Valerenic acid (VA) was recently identified as a β2/3 subunit-specific modulator of GABAA receptors with anxiolytic potential. The aim of the present study was to generate VA derivatives as novel GABAA receptor modulators and to gain insight into the Structure–Activity Relation of this molecule. EXPERIMENTAL APPROACH The carboxyl group of VA was substituted by an uncharged amide or amides with different chain length. Modulation of GABAA receptors composed of different subunit compositions by the VA derivatives was studied in Xenopus oocytes by means of the two-microelectrode voltage-clamp technique. Half-maximal stimulation of GABA-induced chloride currents (IGABA) through GABAA receptors (EC50) and efficacies (maximal stimulation of IGABA) were estimated. Anxiolytic Activity of the VA derivatives was studied in mice, applying the elevated plus maze test. KEY RESULTS Valerenic acid amide (VA-A) displayed the highest efficacy (more than twofold greater IGABA enhancement than VA) and highest potency (EC50= 13.7 ± 2.3 µM) on α1β3 receptors. Higher efficacy and potency of VA-A were also observed on α1β2γ2s and α3β3γ2s receptors. Anxiolytic effects were most pronounced for VA-A. CONCLUSIONS AND IMPLICATIONS Valerenic acid derivatives with higher efficacy and affinity can be generated. Greater in vitro action of the amide derivative correlated with a more pronounced anxiolytic effect in vivo. The data give further confidence in targeting β3 subunit containing GABAA receptors for development of anxiolytics.
Asad U Khan - One of the best experts on this subject based on the ideXlab platform.
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descriptors and their selection methods in qsar analysis paradigm for drug design
Drug Discovery Today, 2016Co-Authors: Asad U KhanAbstract:The screening of chemical libraries with traditional methods, such as high-throughput screening (HTS), is expensive and time consuming. Quantitative Structure–Activity Relation (QSAR) modeling is an alternative method that can assist in the selection of lead molecules by using the information from reference active and inactive compounds. This approach requires good molecular descriptors that are representative of the molecular features responsible for the relevant molecular Activity. The usefulness of these descriptors in QSAR studies has been extensively demonstrated, and they have also been used as a measure of structural similarity or diversity. In this review, we provide a brief explanation of descriptors and the selection approaches most commonly used in QSAR experiments. In addition, some studies have also demonstrated the positive influence of features selection for any drug development model.
Yongsheng Bai - One of the best experts on this subject based on the ideXlab platform.
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identification of potential antimicrobials against salmonella typhimurium and listeria monocytogenes using quantitative Structure Activity Relation modeling
PLOS ONE, 2017Co-Authors: Ethan Rath, Hunter Gill, Yongsheng BaiAbstract:The shelf-life of fresh carcasses and produce depends on the chemical and physical properties of antimicrobials currently used for treatment. For many years the gold standard of these antimicrobials has been Cetylpyridinium Chloride (CPC) a quaternary ammonium compound (QAC). CPC is very effective at removing bacterial pathogens from the surface of chicken but has not been approved for other products due to a toxic residue left behind after treatment. Currently there is also a rising trend in QAC resistant bacteria. In order to find new compounds that can combat both antimicrobial resistance and the toxic residue we have developed two Quantitative Structure-Activity Relationship (QSAR) models for Salmonella typhimurium and Listeria monocytogenes. These models have been shown to be accurate and reliable through multiple internal and external validation techniques. In processing these models we have also identified important descriptors and Structures that may be key in producing a viable compound. With these models, development and testing of new compounds should be greatly simplified.
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quantitative Structure Activity Relation studyof quaternary ammonium compounds inpathogen control computational methodsfor the discovery of food antimicrobials
Chemical Informatics, 2016Co-Authors: Ethan C Rath, Yongsheng BaiAbstract:Objective: Quaternary ammonium compounds (QACs) are surfactants that are made of at least one cationic nitrogen attached to a variety of different side groups, usually consisting of one or more hydrophobic chains. These compounds are generally used for surface decontamination, oral hygiene, and recently in carcass preservation. Recently there have been many studies that have implicated QACs in the development of resistance in bacteria as well as harmful environmental effects. One compound in particular, cetylpyridinium chloride (CPC), has recently gained acceptance as a safe and practical method for use in consumable raw poultry product decontamination. This compound is highly lipophilic and leaves a residue that is potentially toxic to consumers and the environment if not properly removed. Methods: Using computational methods, we propose the use of quantitative Structure-Activity Relation (QSAR) analysis to determine the antimicrobial effects of novel and untested QACs and QAC-like, Structures for further testing. Results: We developed a consensus model with an R2 and a slope of 0.98, which shows good linear Structure of its predictions of minimum inhibitory concentration (MIC). This model was validated by prediction of known antimicrobial data of QACs. Similar compounds to CPC were collected and their antimicrobial effects were predicted by this model. Many of these compounds were detected as possible antimicrobials. Conclusion: This study has identified several promising antimicrobial compounds worth of further study. By diversifying the available QACs we hope to develop better disinfectants, create more environmentally friendly compounds, and help to stall, or even halt, the development of antimicrobial resistance.
S Mohan - One of the best experts on this subject based on the ideXlab platform.
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synthesis vibrational nmr quantum chemical and Structure Activity Relation studies of 2 hydroxy 4 methoxyacetophenone
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2014Co-Authors: V Arjunan, L Devi, R Subbalakshmi, T Rani, S MohanAbstract:Abstract The stable geometry of 2-hydroxy-4-methoxyacetophenone is optimised by DFT/B3LYP method with 6-311++G∗∗ and cc-pVTZ basis sets. The structural parameters, thermodynamic properties and vibrational frequencies of the optimised geometry have been determined. The effects of substituents (hydroxyl, methoxy and acetyl groups) on the benzene ring vibrational frequencies are analysed. The vibrational frequencies of the fundamental modes of 2-hydroxy-4-methoxyacetophenone have been precisely assigned and analysed and the theoretical results are compared with the experimental vibrations. 1H and 13C NMR isotropic chemical shifts are calculated and assignments made are compared with the experimental values. The energies of important MO’s, the total electron density and electrostatic potential of the compound are determined. Various reActivity and selectivity descriptors such as chemical hardness, chemical potential, softness, electrophilicity, nucleophilicity and the appropriate local quantities are calculated.
