The Experts below are selected from a list of 138 Experts worldwide ranked by ideXlab platform
Leo Ghemtio - One of the best experts on this subject based on the ideXlab platform.
-
IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data
Journal of Cheminformatics, 2016Co-Authors: Ashenafi Legehar, Henri Xhaard, Leo GhemtioAbstract:Background The disposition of a pharmaceutical compound within an organism, i.e. its Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) properties and adverse effects, critically affects late stage failure of drug candidates and has led to the withdrawal of approved drugs. Computational methods are effective approaches to reduce the number of safety issues by analyzing possible links between chemical structures and ADMET or adverse effects, but this is limited by the size, quality, and heterogeneity of the data available from individual sources. Thus, large, clean and integrated databases of approved drug data, associated with fast and efficient predictive tools are desirable early in the drug discovery process.
-
IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data
Journal of Cheminformatics, 2016Co-Authors: Ashenafi Legehar, Henri Xhaard, Leo GhemtioAbstract:Background The disposition of a pharmaceutical compound within an organism, i.e. its Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) properties and adverse effects, critically affects late stage failure of drug candidates and has led to the withdrawal of approved drugs. Computational methods are effective approaches to reduce the number of safety issues by analyzing possible links between chemical structures and ADMET or adverse effects, but this is limited by the size, quality, and heterogeneity of the data available from individual sources. Thus, large, clean and integrated databases of approved drug data, associated with fast and efficient predictive tools are desirable early in the drug discovery process. Description We have built a relational database (IDAAPM) to integrate available approved drug data such as drug approval information, ADMET and adverse effects, chemical structures and molecular descriptors, targets, bioactivity and related references. The database has been coupled with a searchable web interface and modern data analytics platform (KNIME) to allow data access, data transformation, initial analysis and further predictive modeling. Data were extracted from FDA resources and supplemented from other publicly available databases. Currently, the database contains information regarding about 19,226 FDA approval applications for 31,815 products (small molecules and biologics) with their approval history, 2505 active ingredients, together with as many ADMET properties, 1629 molecular structures, 2.5 million adverse effects and 36,963 experimental drug-target bioactivity data. Conclusion IDAAPM is a unique resource that, in a single relational database, provides detailed information on FDA approved drugs including their ADMET properties and adverse effects, the corresponding targets with bioactivity data, coupled with a data analytics platform. It can be used to perform basic to complex drug-target ADMET or adverse effects analysis and predictive modeling. IDAAPM is freely accessible at http://idaapm.helsinki.fi and can be exploited through a KNIME workflow connected to the database. Graphical abstract FDA approved drug data integration for predictive modeling
S. Harishkumar - One of the best experts on this subject based on the ideXlab platform.
-
ANTIPROLIFERATIVE, ADME AND POTENTIAL IN SILICO G6PDH INHIBITORY ACTIVITY OF NOVEL 2-(1-BENZOFURAN-2-YL)-4-(5-PHENYL-4H-1, 2, 4-TRIAZOL-3-YL) QUINOLINE DERIVATIVES
International Journal of Pharmacy and Pharmaceutical Sciences, 2016Co-Authors: S. Santoshkumar, Nayak D. Satyanarayan, S. Harishkumar, K. Manjulatha, R. Anantacharya, H N Harishkumar, S. Yallappa, Bhadrapura Lakkappa DhananjayaAbstract:Objectives: Synthesis of new 2-(1-benzofuran-2-yl)-4-(5-phenyl-4 H -1, 2, 4-triazol-3-yl) quinoline and its derivatives for antiproliferative potential against cancer cells. Methods: The general methods were employed for the synthesis and the structures were confirmed by IR, 1 H - NMR, 13 C - NMR and mass spectral analysis. The antiproliferative activity was performed by 3-(4,5- di methyl thiazol -2-yl)-2,5-di phenyl tetrazolium bromide (MTT) assay and molecular docking study were performed by Auto Dock Tools. In silico Absorption-Distribution-Metabolism-Excretion-Toxicity (ADMET) study for the drug, likeliness was carried out on ACD/lab-2. Results: The compound 3l showed 44, 44, 38 and 37 % inhibition against MCF - 7, HepG2, Colo205 and HeLa cell lines, respectively; whereas, the compounds 3i and 3j exhibited 49 and 42 % inhibition against MCF - 7 cell line. The molecular docking study revealed that the compound 3i has the lowest binding energy ( - 8.60 Kcal mol -1 ), suggesting to be potentially best inhibitor of Glucose - 6 - phosphate dehydrogenase (G6PDH). The in silico ADME analysis also revealed that compound 3i does not violate any of the Lipinski rules of five and has the best stimulative human colonic absorption up to 95 %. Conclusion: The study reveals that the compounds containing benzofuran coupled nitrogen heterocycles are essential for activity as they possess excellent drug - like characteristics.
-
ANTIMICROBIAL AND IN SILICO ADMET SCREENING OF NOVEL (E)-N-(2-(1H-INDOL-3-YL-AMINO) VINYL)-3-(1-METHYL-1H-INDOL-3-YL)-3-PHENYLPROPANAMIDE DERIVATIVES
International Journal of Pharmacy and Pharmaceutical Sciences, 2016Co-Authors: K. S. Manjunatha, Nayak D. Satyanarayan, S. HarishkumarAbstract:Objective: Synthesis, in silico absorption, distribution, metabolism, excretion, Toxicity (ADMET) and in vitro antimicrobial screening of ( E )- N -(2-(1 H -indol-3-ylamino) vinyl)-3-(1-methyl-1 H -indol-3-yl)-3-phenylpropanamide derivatives. Methods: ( E )- N -(2-(1 H -indol-3-ylamino) vinyl)-3-(1-methyl-1 H -indol-3-yl)-3 phenylpropane-amide derivatives were synthesized by combining indole ethanolamine and substituted Meldrum’s adduct. The synthesized compounds were subjected to in vitro antimicrobial study by cup plate method and in silico ADMET properties using ACD/I-Lab 2.0. Results: The in vitro antimicrobial screening against precarious pathogenic microorganisms viz , Pseudomonas aureginosa , Staphylococcus aureus, Escherichia coli, Vibrio cholerae , and the antifungal activity against Candida albicans, Aspergillus niger , Penicillin chrysogenum and Cladosporium oxysporum strains. The results revealed that compounds 5b, 5c, 5d and 5e showed good antimicrobial property and obeyed the in silico pharmacokinetic parameters. Conclusion: The encouraging results exhibited by the compounds ( E )- N -(2-(1 H -indol-3-ylamino) vinyl)-3-(1-methyl-1 H -indol-3-yl)-3-phenyl propanamide derivatives, 5(a-e) can be explored as possible hits in antimicrobial therapy. The molecules obey the Lipinski rule of five when tested in silico and can be used in understanding the quantitative structure-activity relationship (QSAR) parameters.
Ashenafi Legehar - One of the best experts on this subject based on the ideXlab platform.
-
IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data
Journal of Cheminformatics, 2016Co-Authors: Ashenafi Legehar, Henri Xhaard, Leo GhemtioAbstract:Background The disposition of a pharmaceutical compound within an organism, i.e. its Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) properties and adverse effects, critically affects late stage failure of drug candidates and has led to the withdrawal of approved drugs. Computational methods are effective approaches to reduce the number of safety issues by analyzing possible links between chemical structures and ADMET or adverse effects, but this is limited by the size, quality, and heterogeneity of the data available from individual sources. Thus, large, clean and integrated databases of approved drug data, associated with fast and efficient predictive tools are desirable early in the drug discovery process.
-
IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data
Journal of Cheminformatics, 2016Co-Authors: Ashenafi Legehar, Henri Xhaard, Leo GhemtioAbstract:Background The disposition of a pharmaceutical compound within an organism, i.e. its Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) properties and adverse effects, critically affects late stage failure of drug candidates and has led to the withdrawal of approved drugs. Computational methods are effective approaches to reduce the number of safety issues by analyzing possible links between chemical structures and ADMET or adverse effects, but this is limited by the size, quality, and heterogeneity of the data available from individual sources. Thus, large, clean and integrated databases of approved drug data, associated with fast and efficient predictive tools are desirable early in the drug discovery process. Description We have built a relational database (IDAAPM) to integrate available approved drug data such as drug approval information, ADMET and adverse effects, chemical structures and molecular descriptors, targets, bioactivity and related references. The database has been coupled with a searchable web interface and modern data analytics platform (KNIME) to allow data access, data transformation, initial analysis and further predictive modeling. Data were extracted from FDA resources and supplemented from other publicly available databases. Currently, the database contains information regarding about 19,226 FDA approval applications for 31,815 products (small molecules and biologics) with their approval history, 2505 active ingredients, together with as many ADMET properties, 1629 molecular structures, 2.5 million adverse effects and 36,963 experimental drug-target bioactivity data. Conclusion IDAAPM is a unique resource that, in a single relational database, provides detailed information on FDA approved drugs including their ADMET properties and adverse effects, the corresponding targets with bioactivity data, coupled with a data analytics platform. It can be used to perform basic to complex drug-target ADMET or adverse effects analysis and predictive modeling. IDAAPM is freely accessible at http://idaapm.helsinki.fi and can be exploited through a KNIME workflow connected to the database. Graphical abstract FDA approved drug data integration for predictive modeling
Areejit Samal - One of the best experts on this subject based on the ideXlab platform.
-
IMPPAT: A curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics
Scientific Reports, 2018Co-Authors: Karthikeyan Mohanraj, Bagavathy Shanmugam Karthikeyan, R. P. Vivek-ananth, R. P. Bharath Chand, S. R. Aparna, Pattulingam Mangalapandi, Areejit SamalAbstract:Phytochemicals of medicinal plants encompass a diverse chemical space for drug discovery. India is rich with a flora of indigenous medicinal plants that have been used for centuries in traditional Indian medicine to treat human maladies. A comprehensive online database on the phytochemistry of Indian medicinal plants will enable computational approaches towards natural product based drug discovery. In this direction, we present, IMPPAT, a manually curated database of 1742 I ndian M edicinal P lants, 9596 P hytochemicals, A nd 1124 T herapeutic uses spanning 27074 plant-phytochemical associations and 11514 plant-therapeutic associations. Notably, the curation effort led to a non-redundant in silico library of 9596 phytochemicals with standard chemical identifiers and structure information. Using cheminformatic approaches, we have computed the physicochemical, ADMET (absorption, distribution, metabolism, excretion, Toxicity) and drug-likeliness properties of the IMPPAT phytochemicals. We show that the stereochemical complexity and shape complexity of IMPPAT phytochemicals differ from libraries of commercial compounds or diversity-oriented synthesis compounds while being similar to other libraries of natural products. Within IMPPAT, we have filtered a subset of 960 potential druggable phytochemicals, of which majority have no significant similarity to existing FDA approved drugs, and thus, rendering them as good candidates for prospective drugs. IMPPAT database is openly accessible at: https://cb.imsc.res.in/imppat .
Henri Xhaard - One of the best experts on this subject based on the ideXlab platform.
-
IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data
Journal of Cheminformatics, 2016Co-Authors: Ashenafi Legehar, Henri Xhaard, Leo GhemtioAbstract:Background The disposition of a pharmaceutical compound within an organism, i.e. its Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) properties and adverse effects, critically affects late stage failure of drug candidates and has led to the withdrawal of approved drugs. Computational methods are effective approaches to reduce the number of safety issues by analyzing possible links between chemical structures and ADMET or adverse effects, but this is limited by the size, quality, and heterogeneity of the data available from individual sources. Thus, large, clean and integrated databases of approved drug data, associated with fast and efficient predictive tools are desirable early in the drug discovery process.
-
IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data
Journal of Cheminformatics, 2016Co-Authors: Ashenafi Legehar, Henri Xhaard, Leo GhemtioAbstract:Background The disposition of a pharmaceutical compound within an organism, i.e. its Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) properties and adverse effects, critically affects late stage failure of drug candidates and has led to the withdrawal of approved drugs. Computational methods are effective approaches to reduce the number of safety issues by analyzing possible links between chemical structures and ADMET or adverse effects, but this is limited by the size, quality, and heterogeneity of the data available from individual sources. Thus, large, clean and integrated databases of approved drug data, associated with fast and efficient predictive tools are desirable early in the drug discovery process. Description We have built a relational database (IDAAPM) to integrate available approved drug data such as drug approval information, ADMET and adverse effects, chemical structures and molecular descriptors, targets, bioactivity and related references. The database has been coupled with a searchable web interface and modern data analytics platform (KNIME) to allow data access, data transformation, initial analysis and further predictive modeling. Data were extracted from FDA resources and supplemented from other publicly available databases. Currently, the database contains information regarding about 19,226 FDA approval applications for 31,815 products (small molecules and biologics) with their approval history, 2505 active ingredients, together with as many ADMET properties, 1629 molecular structures, 2.5 million adverse effects and 36,963 experimental drug-target bioactivity data. Conclusion IDAAPM is a unique resource that, in a single relational database, provides detailed information on FDA approved drugs including their ADMET properties and adverse effects, the corresponding targets with bioactivity data, coupled with a data analytics platform. It can be used to perform basic to complex drug-target ADMET or adverse effects analysis and predictive modeling. IDAAPM is freely accessible at http://idaapm.helsinki.fi and can be exploited through a KNIME workflow connected to the database. Graphical abstract FDA approved drug data integration for predictive modeling
