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1. |
Record Nr. |
UNINA9910146243203321 |
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Titolo |
Drug bioavailability [[electronic resource] ] : estimation of solubility, permeability, absorption and bioavailability / / edited by Han van de Waterbeemd, Hans Lennernàˆs and Per Artursson |
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Pubbl/distr/stampa |
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Weinheim, : Wiley-VCH, c2003 |
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ISBN |
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1-280-52024-8 |
9786610520244 |
3-527-60515-0 |
3-527-60147-3 |
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Descrizione fisica |
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1 online resource (605 p.) |
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Collana |
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Methods and principles in medicinal chemistry ; ; v. 18 |
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Altri autori (Persone) |
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WaterbeemdHan van de |
LennernàˆsHans |
ArturssonPer |
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Disciplina |
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Soggetti |
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Drugs - Bioavailability |
Drugs - Solubility |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Drug Bioavailability Estimation of Solubility, Permeability, Absorption and Bioavailability; Contents; Preface; Foreword; List of Authors; I Studies of Membrane Permeability and Oral Absorption; 1 Physico-chemical Approaches to Drug Absorption; Abbreviations; Symbols; 1.1 Introduction; 1.2 Drug-like Properties; 1.3 Dissolution and Solubility; 1.3.1 Calculated Solubility; 1.4 Ionization (pK(a)); 1.5 Lipophilicity; 1.5.1 Calculated log P; 1.6 Molecular Size and Shape; 1.6.1 Calculated Size Descriptors; 1.7 Hydrogen Bonding; 1.7.1 Calculated Hydrogen-Bonding Descriptors; 1.8 Amphiphilicity |
1.9 Permeability1.9.1 Artificial Membranes; 1.9.2 IAM, ILC, MEKC, and BMC; 1.9.3 Liposome Partitioning; 1.9.4 Biosensors; 1.9.5 Ghost Erythrocytes and Diffusion Constants; References; 2 High-throughput |
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Measurement of log D and pK(a); Abbreviations; Symbols; 2.1 Introduction; 2.2 Relationship between Ionization and Lipophilicity; 2.3 Measuring log D; 2.3.1 Shake-flask Method; 2.3.2 pH-metric Method; 2.3.3 Direct Chromatographic Methods; 2.3.3.1 Chromatographic Hydrophobicity Index (CHI); 2.3.3.2 Microemulsion Electrokinetic Chromatography (MEEKC) |
2.3.3.3 Chromatography in the Presence of Octanol2.3.3.4 Reversed-Phase Chromatography; 2.3.3.5 Liquid-Liquid Partition Chromatography; 2.4 Measuring pK(a); 2.4.1 Review of Methods; 2.4.2 The Effect of Co-solvents on pK(a); 2.4.3 pH-Metric Titration; 2.4.4 Hybrid pH-Metric/UV Method; 2.4.5 Other Methods; 2.4.6 pH Gradient Titration; 2.5 Some Thoughts about High-throughput Analytical Chemistry; Acknowledgments; References; 3 High-throughput Measurement of Permeability Profiles; Abbreviations; Symbols; 3.1 Introduction |
3.2 Key Historical Developments in Artificial-Membrane Permeability Measurement3.3 The Ideal in vitro Artificial Membrane Permeability Model; 3.3.1 Lipid Compositions in Biological Membranes; 3.3.2 Permeability-pH Considerations; 3.3.3 Role of Serum Proteins; 3.3.4 Effects of Cosolvents, Bile Acids, and other Surfactants; 3.3.5 Components of the Ideal; 3.4 New Directions in PAMPA; 3.4.1 Concentrated and Charged Phospholipid Membranes; 3.4.2 Gradient-pH Permeability Equation; 3.4.3 Permeability Measurements: High-phospholipid in Surfactant-free Solutions |
3.4.4 Membrane Retention Measurements: High-phospholipid in Surfactant-free Solutions3.4.5 Egg Lecithin and the Degree of Negative Charge; 3.4.6 Summary: Increasing Phospholipid Content in the Absence of Sink Conditions; 3.4.7 Effects of Surfactant on High-phospholipid Membrane Permeability and Retention; 3.4.8 Quality and Usefulness of the UV Spectra; 3.4.9 Iso-pH and Gradient-pH Mapping in 2% DOPC-Dodecane; 3.4.10 Iso-pH Mapping in 20% Soy Lecithin-Dodecane, with Surfactant |
3.4.11 Predictions of in vivo Human Jejunal Permeabilities using the Improved 20% Soy Lecithin with Surfactant in vitro PAMPA Technique |
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Sommario/riassunto |
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The peroral application (swallowing) of a medicine means that the body must first resorb the active substance before it can begin to take effect. The efficacy of drug uptake depends on the one hand on the chemical characteristics of the active substance, above all on its solubility and membrane permeability. On the other hand, it is determined by the organism's ability to absorb pharmaceuticals by way of specific transport proteins or to excrete them. Since many pharmacologically active substances are poorly suited for oral intake, a decisive criterion for the efficacy of a medicine is its so- |
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2. |
Record Nr. |
UNISA996418262103316 |
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Autore |
Tao Jili |
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Titolo |
DNA Computing Based Genetic Algorithm [[electronic resource] ] : Applications in Industrial Process Modeling and Control / / by Jili Tao, Ridong Zhang, Yong Zhu |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (IX, 274 p. 187 illus., 108 illus. in color.) |
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Disciplina |
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Soggetti |
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Computer mathematics |
Control engineering |
Artificial intelligence |
Computational Science and Engineering |
Control and Systems Theory |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introduction -- DNA computing based RNA-GA -- DNA double-helix based hybrid genetic algorithm -- DNA computing based multi-objective genetic algorithm -- Parameter identification and optimization for chemical process -- RBF neural network for nonlinear SISO system -- T-S Fuzzy neural network for nonlinear SISO system -- PCA & GA based ARX plus RBF Modeling for Nonlinear DPS -- GA based predictive control design -- MOGA based PID controller design -- Concluding Remarks. |
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Sommario/riassunto |
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This book focuses on the implementation, evaluation and application of DNA/RNA-based genetic algorithms in connection with neural network modeling, fuzzy control, the Q-learning algorithm and CNN deep learning classifier. It presents several DNA/RNA-based genetic algorithms and their modifications, which are tested using benchmarks, as well as detailed information on the implementation steps and program code. In addition to single-objective optimization, here genetic algorithms are also used to solve multi-objective optimization for neural network modeling, fuzzy control, model predictive control |
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and PID control. In closing, new topics such as Q-learning and CNN are introduced. The book offers a valuable reference guide for researchers and designers in system modeling and control, and for senior undergraduate and graduate students at colleges and universities. . |
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