The Experts below are selected from a list of 3039 Experts worldwide ranked by ideXlab platform
Robert Preissner - One of the best experts on this subject based on the ideXlab platform.
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SuperPred: update on Drug Classification and target prediction
Nucleic acids research, 2014Co-Authors: Janette Nickel, Mathias Dunkel, Bjoern-oliver Gohlke, Jevgeni Erehman, Priyanka Banerjee, Wen Wei Rong, Andrean Goede, Robert PreissnerAbstract:The SuperPred web server connects chemical similarity of Drug-like compounds with molecular targets and the therapeutic approach based on the similar property principle. Since the first release of this server, the number of known compound–target interactions has increased from 7000 to 665 000, which allows not only a better prediction quality but also the estimation of a confidence. Apart from the addition of quantitative binding data and the statistical consideration of the similarity distribution in all Drug classes, new approaches were implemented to improve the target prediction. The 3D similarity as well as the occurrence of fragments and the concordance of physico-chemical properties is also taken into account. In addition, the effect of different fingerprints on the prediction was examined. The retrospective prediction of a Drug class (ATC code of the WHO) allows the evaluation of methods and descriptors for a well-characterized set of approved Drugs. The prediction is improved by 7.5% to a total accuracy of 75.1%. For query compounds with sufficient structural similarity, the web server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. SuperPred is publicly available without registration at: http://prediction.charite.de.
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SuperPred: Drug Classification and target prediction.
Nucleic Acids Research, 2008Co-Authors: Mathias Dunkel, Stefan Günther, Jessica Ahmed, Burghardt Wittig, Robert PreissnerAbstract:The Drug Classification scheme of the World Health Organization (WHO) [Anatomical Therapeutic Chemical (ATC)-code] connects chemical Classification and therapeutic approach. It is generally accepted that compounds with similar physicochemical properties exhibit similar biological activity. If this hypothesis holds true for Drugs, then the ATC-code, the putative medical indication area and potentially the medical target should be predictable on the basis of structural similarity. We have validated that the prediction of the Drug class is reliable for WHOclassified Drugs. The reliability of the predicted medical effects of the compounds increases with a rising number of (physico-) chemical properties similar to a Drug with known function. The webserver translates a user-defined molecule into a structural fingerprint that is compared to about 6300 Drugs, which are enriched by 7300 links to molecular targets of the Drugs, derived through text mining followed by manual curation. Links to the affected pathways are provided. The similarity to the medical compounds is expressed by the Tanimoto coefficient that gives the structural similarity of two compounds. A similarity score higher than 0.85 results in correct ATC prediction for 81% of all cases. As the biological effect is well predictable, if the structural similarity is sufficient, the web-server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. Availability: the system is freely accessible at http:// bioinformatics.charite.de/superpred. SuperPred can be obtained via a Creative Commons Attribution Noncommercial-Share Alike 3.0 License.
Mathias Dunkel - One of the best experts on this subject based on the ideXlab platform.
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SuperPred: update on Drug Classification and target prediction
Nucleic acids research, 2014Co-Authors: Janette Nickel, Mathias Dunkel, Bjoern-oliver Gohlke, Jevgeni Erehman, Priyanka Banerjee, Wen Wei Rong, Andrean Goede, Robert PreissnerAbstract:The SuperPred web server connects chemical similarity of Drug-like compounds with molecular targets and the therapeutic approach based on the similar property principle. Since the first release of this server, the number of known compound–target interactions has increased from 7000 to 665 000, which allows not only a better prediction quality but also the estimation of a confidence. Apart from the addition of quantitative binding data and the statistical consideration of the similarity distribution in all Drug classes, new approaches were implemented to improve the target prediction. The 3D similarity as well as the occurrence of fragments and the concordance of physico-chemical properties is also taken into account. In addition, the effect of different fingerprints on the prediction was examined. The retrospective prediction of a Drug class (ATC code of the WHO) allows the evaluation of methods and descriptors for a well-characterized set of approved Drugs. The prediction is improved by 7.5% to a total accuracy of 75.1%. For query compounds with sufficient structural similarity, the web server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. SuperPred is publicly available without registration at: http://prediction.charite.de.
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SuperPred: Drug Classification and target prediction.
Nucleic Acids Research, 2008Co-Authors: Mathias Dunkel, Stefan Günther, Jessica Ahmed, Burghardt Wittig, Robert PreissnerAbstract:The Drug Classification scheme of the World Health Organization (WHO) [Anatomical Therapeutic Chemical (ATC)-code] connects chemical Classification and therapeutic approach. It is generally accepted that compounds with similar physicochemical properties exhibit similar biological activity. If this hypothesis holds true for Drugs, then the ATC-code, the putative medical indication area and potentially the medical target should be predictable on the basis of structural similarity. We have validated that the prediction of the Drug class is reliable for WHOclassified Drugs. The reliability of the predicted medical effects of the compounds increases with a rising number of (physico-) chemical properties similar to a Drug with known function. The webserver translates a user-defined molecule into a structural fingerprint that is compared to about 6300 Drugs, which are enriched by 7300 links to molecular targets of the Drugs, derived through text mining followed by manual curation. Links to the affected pathways are provided. The similarity to the medical compounds is expressed by the Tanimoto coefficient that gives the structural similarity of two compounds. A similarity score higher than 0.85 results in correct ATC prediction for 81% of all cases. As the biological effect is well predictable, if the structural similarity is sufficient, the web-server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. Availability: the system is freely accessible at http:// bioinformatics.charite.de/superpred. SuperPred can be obtained via a Creative Commons Attribution Noncommercial-Share Alike 3.0 License.
E. A. Walker - One of the best experts on this subject based on the ideXlab platform.
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In vivo apparent affinity and efficacy estimates for μ opiates in a rat tail-withdrawal assay
Psychopharmacology, 1998Co-Authors: E. A. Walker, Gerald Zernig, Alice M. YoungAbstract:Experiments in a rat tail-withdrawal assay tested the hypothesis that the magnitude and pattern of antagonism of μ opiate agonists by the insurmountable μ opioid antagonist clocinnamox are inversely related to agonist efficacy. In addition, these experiments examined whether this antagonism could be quantified to yield apparent affinity and efficacy estimates for the pharmacological characterization of five opiate agonists. Etonitazene, etorphine, morphine, buprenorphine, and GPA 1657 produced dose-dependent increases in tail-withdrawal latency until 100% maximum possible effect (%MPE) was obtained. Morphine required a higher dose of clocinnamox for a 50% reduction in maximal antinociceptive effect than did buprenorphine or GPA 1657. In contrast, no dose of clocinnamox tested decreased the%MPE for etonitazene or etorphine. These data suggest a rank order of relative efficacy of etonitazene ≥ etorphine > morphine ≥ GPA 1657 ≥ buprenorphine. Similarly, numerical analysis of these data yielded the following apparent affinity and efficacy estimates: etonitazene (0.38 mg/kg, 128); etorphine (0.68 mg/kg, 125); morphine (50 mg/kg, 38), GPA 1657 (6.6, 39); and buprenorphine (0.042 mg/kg, 2.2). These data illustrate that in vivo affinity and efficacy estimates for a number of agonists are remarkably similar across different methods of analysis and are useful for Drug Classification.
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Buprenorphine antagonism of mu opioids in the rhesus monkey tail-withdrawal procedure.
The Journal of pharmacology and experimental therapeutics, 1995Co-Authors: E. A. Walker, Gerald Zernig, J H WoodsAbstract:The apparent in vivo dissociation constant (KA) and relative efficacy values for alfentanil, etonitazene, morphine, and nalbuphine were determined by comparing the effects of these agonists in the presence of buprenorphine with the effects of these agonists alone in the rhesus monkey tail-withdrawal procedure. Initial time course studies of buprenorphine alone indicated that 3.2 and 10 mg/kg produced increases in tail-withdrawal latencies when studied with 48 degrees C water for 48 hr. No increases in tail-withdrawal latency were found with either dose studied with 55 degrees C water. Buprenorphine produced dose-dependent shifts to the right for the antinociceptive effects of alfentanil, etonitazene, morphine and nalbuphine 72 hr after administration and decreased the maximal effects of morphine in 48 degrees C water and those of alfentanil and etonitazene in 55 degrees C water. Buprenorphine administration decreased the receptors available for agonist interaction to approximately 2%. The average apparent in vivo dissociation constant (KA) values for alfentanil, etonitazene, morphine and nalbuphine were 3.3, 0.073, 60 and 31 mg/kg, respectively. High efficacy estimates were determined for alfentanil (149-203) and etonitazene (174-203), whereas lower efficacy estimates were determined for nalbuphine (57) and morphine (17). The apparent in vivo dissociation constant of a pseudoirreversible antagonist (KB) value for buprenorphine averaged 0.15 mg/kg across agonists, temperatures and buprenorphine doses. These data extend and emphasize the significance of in vivo estimates of affinity and relative efficacy for Drug Classification.
R. R. Neubig - One of the best experts on this subject based on the ideXlab platform.
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International Union of Pharmacology Committee on Receptor Nomenclature and Drug Classification. XXXVIII. Update on Terms and Symbols in Quantitative Pharmacology
Pharmacological Reviews, 2003Co-Authors: R. R. NeubigAbstract:The recommendations that follow have been updated from the proposals of a Technical Subcommittee set up by the International Union of Pharmacology Committee on Receptor Nomenclature and Drug Classification (Jenkinson DH, Barnard EA, Hoyer D, Humphrey PPA, Leff P, and Shankley NP (1995) International Union of Pharmacology Committee on Receptor Nomenclature and Drug Classification. IX. Recommendations on terms and symbols in quantitative pharmacology. Pharmacol Rev 47:255-266).
Gordon L Amidon - One of the best experts on this subject based on the ideXlab platform.
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modern bioavailability bioequivalence and biopharmaceutics Classification system new scientific approaches to international regulatory standards
European Journal of Pharmaceutics and Biopharmaceutics, 2000Co-Authors: Raimar Lobenberg, Gordon L AmidonAbstract:In the last decade, the regulatory bioequivalence (BE) requirements of Drug products have undergone major changes. The introduction of the biopharmaceutics Drug Classification system (BCS) into the guidelines of the Food and Drug Administration (FDA) is a major step forward to classify the biopharmaceutical properties of Drugs and Drug products. Based on mechanistic approaches to the Drug absorption and dissolution processes, the BCS enables the regulatory bodies to simplify and improve the Drug approval process. The knowledge of the BCS characteristics of a Drug in a formulation can also be utilized by the formulation scientist to develop a more optimized dosage form based on fundamental mechanistic, rather than empirical, information. This report gives a brief overview of the BCS and its implications.
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a theoretical basis for a biopharmaceutic Drug Classification the correlation of in vitro Drug product dissolution and in vivo bioavailability
Pharmaceutical Research, 1995Co-Authors: Gordon L Amidon, Vinod P Shah, Hans Lennernas, John R CrisonAbstract:A biopharmaceutics Drug Classification scheme for correlating in vitro Drug product dissolution and in vivo bioavailability is proposed based on recognizing that Drug dissolution and gastrointestinal permeability are the fundamental parameters controlling rate and extent of Drug absorption. This analysis uses a transport model and human permeability results for estimating in vivo Drug absorption to illustrate the primary importance of solubility and permeability on Drug absorption. The fundamental parameters which define oral Drug absorption in humans resulting from this analysis are discussed and used as a basis for this Classification scheme. These Biopharmaceutic Drug Classes are defined as: Case 1. High solubility-high permeability Drugs, Case 2. Low solubility-high permeability Drugs, Case 3. High solubility-low permeability Drugs, and Case 4. Low solubility-low permeability Drugs. Based on this Classification scheme, suggestions are made for setting standards for in vitro Drug dissolution testing methodology which will correlate with the in vivo process. This methodology must be based on the physiological and physical chemical properties controlling Drug absorption. This analysis points out conditions under which no in vitro-in vivo correlation may be expected e.g. rapidly dissolving low permeability Drugs. Furthermore, it is suggested for example that for very rapidly dissolving high solubility Drugs, e.g. 85% dissolution in less than 15 minutes, a simple one point dissolution test, is all that may be needed to insure bioavailability. For slowly dissolving Drugs a dissolution profile is required with multiple time points in systems which would include low pH, physiological pH, and surfactants and the in vitro conditions should mimic the in vivo processes. This Classification scheme provides a basis for establishing in vitro-in vivo correlations and for estimating the absorption of Drugs based on the fundamental dissolution and permeability properties of physiologic importance.
