LEADER 01321nam 2200385Ka 450 001 9910695363203321 005 20060929111734.0 035 $a(CKB)5470000002369117 035 $a(OCoLC)71801880 035 $a(EXLCZ)995470000002369117 100 $a20060929d2002 ua 0 101 0 $aeng 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAmeriCorps*VISTA member handbook$b[electronic resource] 210 1$a[Washington, D.C.] :$c[AmeriCorps*VISTA],$d[2002] 215 $a99 pages $cdigital, PDF file 300 $aTitle from title screen (viewed on Sept. 29, 2006). 300 $a"May 2002." 300 $aIncludes index. 606 $aNational service$zUnited States$vHandbooks, manuals, etc 606 $aVolunteer workers in community development$zUnited States$vHandbooks, manuals, etc 606 $aVolunteer workers in social service$zUnited States$vHandbooks, manuals, etc 608 $aHandbooks and manuals.$2lcgft 615 0$aNational service 615 0$aVolunteer workers in community development 615 0$aVolunteer workers in social service 712 02$aAmeriCorps*VISTA. 801 0$bGPO 801 1$bGPO 906 $aBOOK 912 $a9910695363203321 996 $aVISTA member handbook$93212681 997 $aUNINA LEADER 05192nam 2200625 450 001 9910830717703321 005 20230721033137.0 010 $a1-281-94698-2 010 $a9786611946982 010 $a3-527-62267-5 010 $a3-527-62268-3 035 $a(CKB)1000000000551934 035 $a(EBL)481533 035 $a(OCoLC)277153234 035 $a(SSID)ssj0000112294 035 $a(PQKBManifestationID)11142796 035 $a(PQKBTitleCode)TC0000112294 035 $a(PQKBWorkID)10086817 035 $a(PQKB)10524340 035 $a(MiAaPQ)EBC481533 035 $a(EXLCZ)991000000000551934 100 $a20160819h20082008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aBiosimulation in drug development /$fedited by Martin Bertau, Erik Mosekilde, and Hans V. Westerhoff 210 1$aWeinheim, Germany :$cWILEY-VCH Verlag GmbH & Co. KGaA,$d2008. 210 4$dİ2008 215 $a1 online resource (542 p.) 300 $aIncludes index. 311 $a3-527-31699-X 327 $aBiosimulation in Drug Development; Contents; Preface; List of Contributors; Part I Introduction; 1 Simulation in Clinical Drug Development; 1.1 Introduction; 1.2 Models for Simulations; 1.3 Simulations in Clinical Drug Development: Practical Examples; 1.3.1 Predicting the Outcome of Phase I Studies of Erythropoietin Receptor Agonists; 1.3.2 Simulations for Antimicrobial Dose Selection; 1.3.3 Optimizing the Design of Phase II Dose Finding Studies; 1.3.4 Predicting the Outcome of Phase III Trials Using Phase II Data; 1.4 Conclusions; 2 Modeling of Complex Biomedical Systems; 2.1 Introduction 327 $a2.2 Pulsatile Secretion of Insulin2.3 Subcutaneous Absorption of Insulin; 2.4 Bursting Pancreatic ?-Cells; 2.5 Conclusions; 3 Biosimulation of Drug Metabolism; 3.1 Introduction; 3.2 Experimental Approaches; 3.2.1 Animal Test Models; 3.2.2 Microbial Models; 3.3 The Biosimulation Approach; 3.4 Ethical Issues; 3.5 PharmBiosim - a Computer Model of Drug Metabolism in Yeast; 3.5.1 General Concept; 3.5.1.1 Chemical Abstraction; 3.5.1.2 Biological Abstraction; 3.5.2 Initial Steps - Experimental Results; 3.5.2.1 Dehalogenation (Pathways II and III); 3.5.2.2 Retro-Claisen Condensation (Pathway IV) 327 $a3.5.2.3 Ester Hydrolysis (Pathway VI)3.5.2.4 Competing Pathways and Stereoselectivity; 3.6 Computational Modeling; 3.6.1 Selection of the Modeling Software; 3.6.2 SBML-compatible Software; 3.6.2.1 Cellware; 3.6.2.2 Copasi; 3.6.2.3 Ecell; 3.6.2.4 JigCell; 3.6.2.5 JSim; 3.6.2.6 Systems Biology Workbench; 3.6.2.7 Virtual Cell; 3.6.2.8 XPPAUT; 3.6.3 CellML-compatible Software; 3.6.4 Kinetic Model; 3.6.4.1 Methods; 3.6.4.2 Model Derivation; 3.6.4.3 Results; 3.6.5 Stoichiometric Model; 3.6.5.1 Methods; 3.6.5.2 Model Derivation; 3.6.5.3 Results 327 $a3.7 Application of the Model to Predict Drug Metabolism3.8 Conclusions; Part II Simulating Cells and Tissues; 4 Correlation Between In Vitro, In Situ, and In Vivo Models; 4.1 Introduction; 4.2 Biophysical Models of Intestinal Absorption; 4.2.1 Colon; 4.2.2 Small Intestine; 4.2.3 Stomach; 4.3 Influence of Surfactants on Intestinal Permeability; 4.3.1 Absorption Experiments in Presence of Surfactants; 4.3.1.1 Colon; 4.3.1.2 Intestine; 4.3.1.3 Stomach; 4.4 Modeling and Predicting Fraction Absorbed from Permeability Values; 4.4.1 Mass Balance, Time-independent Models 327 $a4.4.2 Prediction of the Fraction of Dose Absorbed from In Vitro and In Situ Data4.4.3 Prediction from In Situ Absorption Rate Constant Determined with Closed Loop Techniques; 4.4.4 Prediction from Permeabilities Through Caco-2 Cell Lines; 4.4.5 Prediction from the PAMPA In Vitro System; 4.5 Characterization of Active Transport Parameters; 4.5.1 In Situ Parameter Estimation; 4.5.2 In Vitro-In Situ Correlation; 5 Core-Box Modeling in the Biosimulation of Drug Action; 5.1 Introduction; 5.2 Core-Box Modeling; 5.2.1 Shortcomings of Gray-Box and Minimal Modeling 327 $a5.2.1.1 Full-Scale Mechanistic Gray-Box Modeling 330 $aThis first comprehensive survey to cover all pharmaceutically relevant topics provides a comprehensive introduction to this novel and revolutionary tool, presenting both concepts and application examples of biosimulated cells, organs and organisms.Following an introduction to the role of biosimulation in drug development, the authors go on to discuss the simulation of cells and tissues, as well as simulating drug action and effect. 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