Personalized Medicine

 
 
Academic Press
  • 1. Auflage
  • |
  • erschienen am 28. Januar 2016
  • |
  • 398 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-0-12-805211-2 (ISBN)
 
The Advances in Protein Chemistry and Structural Biology series is an essential resource for protein chemists. Each volume brings forth new information about protocols and analysis of proteins, with each thematically organized volume guest edited by leading experts in a broad range of protein-related topics.
  • Provides cutting-edge developments in protein chemistry and structural biology
  • Chapters are written by authorities in their field
  • Targeted to a wide audience of researchers, specialists, and students
1876-1623
  • Englisch
  • San Diego
  • |
  • USA
Elsevier Science
  • 16,22 MB
978-0-12-805211-2 (9780128052112)
0128052112 (0128052112)
weitere Ausgaben werden ermittelt
  • Front Cover
  • Personalized Medicine
  • Copyright
  • Contents
  • Contributors
  • Preface
  • Chapter One: High-Performance Affinity Chromatography: Applications in Drug-Protein Binding Studies and Personalized Medicine
  • 1. Introduction
  • 1.1. Drug-Protein Interactions in Blood
  • 1.2. Preparation of HPAC Columns for Drug-Protein Binding Studies
  • 2. Frontal Analysis Studies of Drug-Protein Interactions
  • 2.1. General Principles of Frontal Analysis
  • 2.2. Characterization of Simple Interactions by Frontal Analysis
  • 2.3. Characterization of Complex Interactions by Frontal Analysis
  • 3. Zonal Elution Studies of Drug-Protein Interactions
  • 3.1. General Principles of Zonal Elution
  • 3.2. Characterization of Drug-Protein Interactions by Zonal Elution
  • 4. Other Methods for Examining Drug-Protein Interactions
  • 4.1. Peak Decay Method
  • 4.2. Ultrafast Affinity Extraction
  • 4.3. Chromatographic Immunoassays
  • 5. Conclusion
  • Acknowledgments
  • References
  • Chapter Two: Role of Proteomics in the Development of Personalized Medicine
  • 1. Introduction
  • 2. Protein Biomarkers
  • 2.1. Proteomic Technologies for Discovery of Biomarkers
  • 3. Protein Biochips
  • 3.1. Role of Protein Biochips in Personalized Medicine
  • 3.2. Nanoproteomics
  • 4. Role of Proteomics-Based Molecular Diagnostics in Personalized Medicine
  • 4.1. Integration of Diagnostics with Therapeutics
  • 5. Pharmacoproteomics
  • 5.1. Pharmacoproteomics and Personalized Medicine
  • 6. Concluding Remarks About Application of Proteomics for Personalized Medicine
  • References
  • Chapter Three: Metabolomics and Personalized Medicine
  • 1. Introduction
  • 2. The Value of ``Omics´´ Technologies in the Development of Personalized Medicine
  • 3. What is Metabolomics?
  • 3.1. Analytical Methods Most Often Used for Metabolomics
  • 3.2. Statistical Approaches
  • 4. The Application of Metabolomics Toward Personalized Medicine
  • 4.1. Identification of Disease Biomarkers for Metabolomics-Based Diagnostics
  • 4.2. Pharmacometabonomics
  • 4.2.1. Metabolomics and the Elucidation of Drug Mechanisms
  • 4.2.2. Metabolomics and Understanding Response to Treatment
  • 4.2.3. Metabolomics and Understanding Drug Toxicology
  • 5. Concluding Remarks
  • Acknowledgments
  • References
  • Chapter Four: Clinical Perspectives on Targeting Therapies for Personalized Medicine
  • 1. Introduction
  • 2. What Are Personalized Medicines?
  • 3. Rare Diseases
  • 4. Evidence for Precision Medicines from Real World Data
  • 5. Using ``Real World´´ Data
  • 6. Ethical Concerns
  • 7. Adaptive Trial Design
  • 8. Companion Diagnostics
  • 9. Biological Treatments
  • 10. Case Studies: Targets for Precision Medicines and Companion Diagnostics
  • 10.1. Drug Transporter Proteins
  • 10.2. Pleiotropic Effects of Statins
  • 10.3. Flecainide: Potential Off-Target Mechanism for Sudden Death
  • 10.4. EGF Receptor 2 Antagonism with Trastuzmab
  • 10.5. Diagnostic Markers to Guide Selection of Anti-cancer Tyrosine Kinase Receptor Inhibitors
  • 10.6. Multiple Companion Diagnostics to Guide Selection of Anti-cancer Tyrosine Kinase Receptor Inhibitors to Treat Color...
  • 10.7. Immunological Targets in Neuropsychiatry
  • 10.7.1. Inflammation as a Target in Acute Psychosis
  • 10.7.2. Anti-inflammatory Treatment of Neuropsychiatric Disorders
  • 10.7.3. New Targets for Treating Dementia
  • 10.7.4. Systemic Inflammation as a Target for Treating Neurodegeneration
  • 10.8. Targeting the Genetic Abnormality in Cystic Fibrosis
  • 10.9. Drug Selection Based on Pharmacogenetic Variants in Drug-Metabolizing Enzymes
  • 10.10. Histocompatibility Antigen Companion Diagnostics
  • 11. Network Pharmacology
  • 12. Future Developments
  • Acknowledgments
  • References
  • Chapter Five: Personalized Medicine in Respiratory Disease: Role of Proteomics
  • 1. Introduction
  • 2. Personalized Medicine
  • 3. Proteomics
  • 4. Respiratory Proteomics
  • 5. Asthma
  • 6. Chronic Obstructive Pulmonary Disease
  • 7. Idiopathic Pulmonary Fibrosis
  • 8. Aspirin-Exacerbated Respiratory Disease
  • 9. Cystic Fibrosis
  • 10. Lung Cancer
  • 11. Conclusions
  • Acknowledgments
  • References
  • Chapter Six: Computational Approaches to Accelerating Novel Medicine and Better Patient Care from Bedside to Benchtop
  • 1. Introduction
  • 2. Currently Approved Products Targeting Protein Kinases in Oncology
  • 3. Tumor Heterogeneity
  • 3.1. Disease and Molecular Heterogeneity
  • 3.2. Temporal and Spatial Heterogeneity
  • 4. Computational Methods for Identifying Driver Mutations
  • 4.1. Frequency-Based Methods
  • 4.2. Functional-Based Methods
  • 4.3. Pathway-Based Methods
  • 4.4. Comparison and Meta-Analysis
  • 5. Literature Mining in Oncology Research
  • 5.1. Biomedical Literature Mining
  • 5.2. Literature Mining for Integrating Molecular Events at the Cellular Level
  • 5.3. Literature Mining to Construct Informed Knowledge Bases for Cancer Research
  • 6. Precise Genome Editing
  • 6.1. Genome Editing Using TALENs
  • 6.2. Genome Editing Using CRISPR/Cas9 Systems
  • 7. Conclusion
  • Supplementary Material
  • References
  • Chapter Seven: Molecular Dynamics: New Frontier in Personalized Medicine
  • 1. Introduction
  • 2. Single Nucleotide Polymorphisms in Drug Response
  • 3. Drug Discovery
  • 4. Advent of Personalized Medicine
  • 5. Evolution of Molecular dynamics in the Field of Macromolecule and Binding Interaction Analysis
  • 6. Molecular Dynamics-A Boon in Personalized Medicine
  • 6.1. Force Fields
  • 6.2. Solvation
  • 6.3. Energy Minimization and Periodic Boundary
  • 6.4. Temperature and Pressure Differences
  • 7. MD Trajectories Analysis
  • 7.1. Root Mean Square Deviation
  • 7.2. Root Mean Square Fluctuation
  • 7.3. Radial Distribution Function
  • 7.4. Hydrogen Bonds
  • 7.5. Radius of Gyration
  • 7.6. Secondary Structure Analysis
  • 7.7. Contact Maps
  • 7.8. Free Energy
  • 7.9. Covariance Matrix
  • 7.10. Principal Component Analysis
  • 7.11. Electrostatic Interactions
  • 8. Application of MD in SNP Analysis Toward Drug Discovery
  • 9. Plausible Ways to Overcome the Disadvantages of Molecular Dynamics
  • 10. Conclusion
  • Acknowledgments
  • References
  • Chapter Eight: Personalized Pharmacoperones for Lysosomal Storage Disorder: Approach for Next-Generation Treatment
  • 1. Introduction
  • 2. LSDs Classification
  • 2.1. Lysosomal Glycan Degradation Disorder
  • 2.1.1. Sialidosis (Mucolipidosis Type I)
  • 2.1.2. Galactosialidosis
  • 2.1.3. a-Mannosidosis
  • 2.1.4. ß-Mannosidosis
  • 2.1.5. Fucosidosis
  • 2.1.6. Schindler Disease
  • 2.1.7. Aspartylglucosaminuria
  • 2.1.8. GM1 Gangliosidosis
  • 2.1.9. Tay-Sachs Disease/GM2 Gangliosidosis 1
  • 2.1.10. Sandhoff Disease/GM2 Gangliosidosis 2
  • 2.1.11. GM2 Gangliosidosis AB Variant
  • 2.1.12. Gaucher Disease
  • 2.1.13. Metachromatic Leukodystrophy
  • 2.1.14. Multiple Sulfatase Deficiencies
  • 2.1.15. Globoid Cell Leukodystrophy/Krabbe Disease
  • 2.1.16. Fabry Disease
  • 2.1.17. MPS I (Hurler-Scheie Syndrome)
  • 2.1.18. MPS II (Hunter Syndrome)
  • 2.1.19. MPS III
  • 2.1.20. MPS IVA-Morquio A
  • 2.1.21. MPS IVB-Morquio B
  • 2.1.22. MPS VI (Maroteaux-Lamy Syndrome)
  • 2.1.23. MPS VII (Sly Syndrome)
  • 2.1.24. MPS IX (Hyaluronidase Deficiency)
  • 2.1.25. Pompe Disease
  • 2.2. Lysosomal Lipid Degradation Disorder
  • 2.2.1. Niemann-Pick Type A and B
  • 2.2.2. Farber Lipogranulomatosis
  • 2.2.3. Wolman/Cholesteryl Ester Storage Disease
  • 2.3. Lysosomal Protein Degradation Disorder
  • 2.3.1. Pycnodysostosis
  • 2.3.2. Neuronal Ceroid Lipofuscinosis 1 and 2
  • 2.4. Lysosomal Protein Degradation Disorder
  • 2.4.1. Cystinosis
  • 2.4.2. Salla/Sialic Acid Storage Disease
  • 2.5. Lysosomal-Trafficking Protein Degradation Disorder
  • 2.5.1. Mucolipidosis Type II and III
  • 2.5.2. Mucolipidosis Type IV
  • 2.5.3. Danon Disease
  • 2.5.4. Niemann-Pick Type C1
  • 2.5.5. Batten Disease/Neuronal Ceroid Lipofuscinosis 3, 6, and 8
  • 2.5.6. Chediak-Higashi Syndrome
  • 2.5.7. Hermansky-Pudlak Syndrome 1 and 2
  • 3. Treatment of LSDs
  • 3.1. Enzyme Replacement Therapy
  • 3.2. Substrate Reduction Therapy
  • 3.3. Bone Marrow Transplantation
  • 3.4. Hematopoietic Stem Cell Transplantation
  • 3.5. Gene Therapy
  • 3.6. Chemical Chaperone Therapy
  • 3.7. Pharmacological Chaperone Therapy
  • 4. Pharmacological Chaperones Versus Chemical Chaperones
  • 5. Combination Therapy for LSDs
  • 6. How Can PCT Personalized?
  • 7. Computational Studies in PCT
  • 8. Advantages and Disadvantages of Various Therapies in LSDs
  • 9. Future of PCTs
  • 10. Conclusion
  • Acknowledgments
  • References
  • Chapter Nine: Investigating the Inhibitory Effect of Wortmannin in the Hotspot Mutation at Codon 1047 of PIK3CA Kinase Do ...
  • 1. Introduction
  • 2. Materials and Methods
  • 2.1. In Silico Prediction of Pathogenicity and Stability
  • 2.2. Molecule Preparation
  • 2.3. Receptor-Ligand Docking Analysis
  • 2.4. Molecular Dynamics Simulation
  • 3. Results
  • 3.1. Prediction of nsSNPs Deleterious Nature
  • 3.2. Molecular Docking Analysis
  • 3.3. Molecular Dynamics Analysis
  • 4. Discussion
  • 5. Conclusion
  • Acknowledgments
  • References
  • Chapter Ten: Role of von Willebrand Factor-A1 Domain Variants P1266L, H1268D, C1272R, and C1272F in VWD: A Molecular Mode ...
  • 1. Introduction
  • 2. Material and Methods
  • 2.1. Dataset
  • 2.2. Computational Methods for Finding Deleterious nsSNPs/Variants
  • 2.3. Modeling of the Mutant Protein Structure
  • 2.4. Molecular Dynamics Simulation
  • 2.5. Principal Component Analysis
  • 3. Results
  • 3.1. Screening of Deleterious nsSNPs/Variants
  • 3.2. Biophysical Validation of nsSNPs/Variants
  • 3.3. Modeling of nsSNPs/Variants
  • 3.4. Molecular Dynamics Simulation Studies
  • 4. Discussion
  • 5. Conclusion
  • Acknowledgment
  • References
  • Author Index
  • Subject Index
  • Back Cover

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