| Document Type
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BL
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| Record Number
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878170
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| Title & Author
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Quantitative systems pharmacology : : models and model-based systems with applications /\ edited by Davide Manca.
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| Publication Statement
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Amsterdam, The Netherlands :: Elsevier,, [2018]
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| Series Statement
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Computer aided chemical engineering ;; volume 42
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| Page. NO
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1 online resource
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| ISBN
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0444639675
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: 9780444639677
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0444639640
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9780444639646
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| Notes
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Includes index.
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| Contents
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Front Cover; Quantitative Systems Pharmacology: Models and Model-Based Systems with Applications; Copyright; Contents; Contributors; Preface; Acknowledgments; Section 1: Introduction to quantitative systems pharmacology; Chapter 1: Quantitative systems pharmacology: Extending the envelope through systems engineering; 1. Introduction; 2. The emergence of QSP modeling; 2.1. Multiscale modeling: Beyond the drug target; 2.2. Modeling the disease state; 3. Modeling drug exposure and drug response at the systemic level; 4. Modeling biological and drug interactions at the molecular level
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4.1. Omics data4.2. Genomics; 4.3. Transcriptomics; 4.4. Proteomics; 4.5. Metabolomics; 4.6. Omics network using pathway enrichment; 4.7. Case study: Pathway enrichment for synthetic MPL; 5. Summary of the model development process; 6. QSP in context; 6.1. Case study: Cortisol regulation in the context of environmental clues: Next challenges; 7. How systems engineering can enable QSP; 8. Final comments; Acknowledgments; References; Section 2: Modeling and applications of systemic pharmacokinetics and pharmacodynamics
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5. Case study II: Design of more effective clinical tests for the study of VWD6. Conclusions; References; Chapter 4: On the Identifiability of Physiological Models: Optimal Design of Clinical Tests; 1. Introduction; 2. The concept of identifiability; 3. Identifiability tests; 3.1. A priori tests for parametric identifiability; 3.2. A posteriori tests for parametric identifiability; 3.3. Practical identifiability of parametric models; 4. Identifiability in the development of compartmental models; 5. Optimal design of clinical tests for guaranteed identifiability
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5.1.1. Model parameters5.1.2. PK simulation; 6. Mathematical formulation of PD models; 6.1. Direct effect model: Hill equation; 6.2. Indirect response models; 6.3. Irreversible effect models; 7. Conclusions; References; Chapter 3: Advanced Techniques for the Optimal Design of Experiments in Pharmacokinetics; 1. Introduction; 2. Identifying a physiological model: The need for experimental design; 3. Design of experiments under constraints for physiological models; 3.1. Design procedure; 3.2. Design of experimental protocols under uncertainty; 4. Case study I: Identification of a PK-PD model
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Chapter 2: An engineering oriented approach to physiologically based pharmacokinetic and pharmacodynamic modeling1. Introduction; 2. Classic compartmental pharmacokinetic models; 3. Physiologically based pharmacokinetic models; 3.1. Individualization of the pharmacokinetic prediction; 3.2. Model identification; 3.2.1. The rationale of model-assisted experiments; 3.2.2. Linearization method; 3.2.3. Monte Carlo method; 3.2.4. Bootstrap method; 3.2.5. A posteriori identifiability; 4. Introduction to pharmacodynamics; 5. Mathematical formulation of a PBPK model; 5.1. The PBPK model
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| Subject
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Pharmacology.
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| Subject
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Systems engineering.
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| Subject
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MEDICAL-- Pharmacology.
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Pharmacology.
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Systems engineering.
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| Dewey Classification
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615.1
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| LC Classification
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RM300
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RM301.25.Q33 2018
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| Added Entry
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Manca, Davide
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