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Advances in Multiple Sclerosis Research-Series I
| Advances in Multiple Sclerosis Research-Series I |
| Autore | Matsoukas John |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (334 p.) |
| Soggetto topico | Medicine |
| Soggetto non controllato |
multiple sclerosis
inflammation oxidative biomarker sample size autoimmune encephalitis plasma exchange autoimmunity immunotherapeutics clinical outcomes major depression bupropion S-adenosylmethionine vitamin D3 yoga craniopharyngioma fractionated stereotactic radiation treatments sphenoid sinusitis cranial nerve-VI palsy autoimmune diseases immune thrombocytopenic purpura alemtuzumab antibodies against GluR3 peptide cognitive impairment diagnosis neuropsychological assessment short intracortical inhibition intracortical facilitation fampridine walking disability TSPAN32 tetraspanins cellular immunity memory T cells tDCS neuroimaging positron emission tomography cerebral blood flow probiotics Streptococcus thermophilus ST285 MBP83–99 peptide mannan immune modulation agonist peptide gut microbiome gut–brain axis metagenomics disease-modifying treatments MS vaccine immunomodulation carriers B cell receptor delivery methods immunotherapy monoclonal antibodies T cell receptor tolerance diagnostic markers immunoglobulins kappa free light chains antigen-specific immunotherapies tolerogenic vaccines tolerance induction central nervous system myelin peptides myelin basic protei proteolipid protein myelin oligodendrocyte glycoprotein nanotechnology drug delivery nanosystems lipids polymers vaccines nanoparticles antigen-specific immunotherapy experimental autoimmune encephalomyelitis neurodegeneration chloroquine EAE dendritic cells microglia astrocytes oligodendrocytes conformational analysis peptides altered peptide ligands NMR spectroscopy NOE-constraints molecular dynamic trimolecular complex Multiple Sclerosis early-onset adult-onset Human Leucocyte Antigens immunogenetics clinical phenotype clinical outcome therapeutics antibody detection ELISA multivalency N-glucosylated peptide epitopes peptide conjugation MOG35-55 Graphite/SiO2 electrode voltammetry HPLC MS drugs |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557385403321 |
Matsoukas John
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Bioinformatics tools for pharmaceutical drug product development / / edited by Vivek Chavda, Krishnan Anand, and Vasso Apostolopoulos
| Bioinformatics tools for pharmaceutical drug product development / / edited by Vivek Chavda, Krishnan Anand, and Vasso Apostolopoulos |
| Pubbl/distr/stampa | Hoboken, NJ : , : Wiley, , ℗2023 |
| Descrizione fisica | 1 online resource (440 pages) |
| Disciplina | 570.285 |
| Soggetto topico |
Drug development
Bioinformatics |
| ISBN |
1-119-86572-7
1-119-86571-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Part I: Bioinformatics Tools -- Chapter 1 Introduction to Bioinformatics, AI, and ML for Pharmaceuticals -- 1.1 Introduction -- 1.2 Bioinformatics -- 1.2.1 Limitations of Bioinformatics -- 1.2.2 Artificial Intelligence (AI) -- 1.3 Machine Learning (ML) -- 1.3.1 Applications of ML -- 1.3.2 Limitations of ML -- 1.4 Conclusion and Future Prospects -- References -- Chapter 2 Artificial Intelligence and Machine Learning-Based New Drug Discovery Process with Molecular Modelling -- 2.1 Introduction -- 2.2 Artificial Intelligence in Drug Discovery -- 2.2.1 Training Dataset Used in Medicinal Chemistry -- 2.2.2 Availability and Quality of Initial Data -- 2.3 AI in Virtual Screening -- 2.4 AI for De Novo Design -- 2.5 AI for Synthesis Planning -- 2.6 AI in Quality Control and Quality Assurance -- 2.7 AI-Based Advanced Applications -- 2.7.1 Micro/Nanorobot Targeted Drug Delivery System -- 2.7.2 AI in Nanomedicine -- 2.7.3 Role of AI in Market Prediction -- 2.8 Discussion and Future Perspectives -- 2.9 Conclusion -- References -- Chapter 3 Role of Bioinformatics in Peptide-Based Drug Design and Its Serum Stability -- 3.1 Introduction -- 3.2 Points to be Considered for Peptide-Based Delivery -- 3.3 Overview of Peptide-Based Drug Delivery System -- 3.4 Tools for Screening of Peptide Drug Candidate -- 3.5 Various Strategies to Increase Serum Stability of Peptide -- 3.5.1 Cyclization of Peptide -- 3.5.2 Incorporation of D Form of Amino Acid -- 3.5.3 Terminal Modification -- 3.5.4 Substitution of Amino Acid Which is Not Natural -- 3.5.5 Stapled Peptides -- 3.5.6 Synthesis of Stapled Peptides -- 3.6 Method/Tools for Serum Stability Evaluation -- 3.7 Conclusion -- 3.8 Future Prospects -- References -- Chapter 4 Data Analytics and Data Visualization for the Pharmaceutical Industry -- 4.1 Introduction.
4.2 Data Analytics -- 4.3 Data Visualization -- 4.4 Data Analytics and Data Visualization for Formulation Development -- 4.5 Data Analytics and Data Visualization for Drug Product Development -- 4.6 Data Analytics and Data Visualization for Drug Product Life Cycle Management -- 4.7 Conclusion and Future Prospects -- References -- Chapter 5 Mass Spectrometry, Protein Interaction and Amalgamation of Bioinformatics -- 5.1 Introduction -- 5.2 Mass Spectrometry - Protein Interaction -- 5.2.1 The Prerequisites -- 5.2.2 Finding Affinity Partner (The Bait) -- 5.2.3 Antibody-Based Affinity Tags -- 5.2.4 Small Molecule Ligands -- 5.2.5 Fusion Protein-Based Affinity Tags -- 5.3 MS Analysis -- 5.4 Validating Specific Interactions -- 5.5 Mass Spectrometry - Qualitative and Quantitative Analysis -- 5.6 Challenges Associated with Mass Analysis -- 5.7 Relative vs. Absolute Quantification -- 5.8 Mass Spectrometry - Lipidomics and Metabolomics -- 5.9 Mass Spectrometry - Drug Discovery -- 5.10 Conclusion and Future Scope -- 5.11 Resources and Software -- Acknowledgement -- References -- Chapter 6 Applications of Bioinformatics Tools in Medicinal Biology and Biotechnology -- 6.1 Introduction -- 6.2 Bioinformatics Tools -- 6.3 The Genetic Basis of Diseases -- 6.4 Proteomics -- 6.5 Transcriptomic -- 6.6 Cancer -- 6.7 Diagnosis -- 6.8 Drug Discovery and Testing -- 6.9 Molecular Medicines -- 6.10 Personalized (Precision) Medicines -- 6.11 Vaccine Development and Drug Discovery in Infectious Diseases and COVID-19 Pandemic -- 6.12 Prognosis of Ailments -- 6.13 Concluding Remarks and Future Prospects -- Acknowledgement -- References -- Chapter 7 Clinical Applications of "Omics" Technology as a Bioinformatic Tool -- Abbreviations -- 7.1 Introduction -- 7.2 Execution Method -- 7.3 Overview of Omics Technology -- 7.4 Genomics -- 7.5 Nutrigenomics -- 7.6 Transcriptomics. 7.7 Proteomics -- 7.8 Metabolomics -- 7.9 Lipomics or Lipidomics -- 7.10 Ayurgenomics -- 7.11 Pharmacogenomics -- 7.12 Toxicogenomic -- 7.13 Conclusion and Future Prospects -- Acknowledgement -- References -- Part II: Bioinformatics Tools for Pharmaceutical Sector -- Chapter 8 Bioinformatics and Cheminformatics Tools in Early Drug Discovery -- Abbreviations -- 8.1 Introduction -- 8.2 Informatics and Drug Discovery -- 8.3 Computational Methods in Drug Discovery -- 8.3.1 Homology Modeling -- 8.3.2 Docking Studies -- 8.3.3 Molecular Dynamics Simulations -- 8.3.4 De Novo Drug Design -- 8.3.5 Quantitative Structure Activity Relationships -- 8.3.6 Pharmacophore Modeling -- 8.3.7 Absorption, Distribution, Metabolism, Excretion and Toxicity Profiling -- 8.4 Conclusion -- References -- Chapter 9 Artificial Intelligence and Machine Learning-Based Formulation and Process Development for Drug Products -- 9.1 Introduction -- 9.2 Current Scenario in Pharma Industry and Quality by Design (QbD) -- 9.3 AI- and ML-Based Formulation Development -- 9.4 AI- and ML-Based Process Development and Process Characterization -- 9.5 Concluding Remarks and Future Prospects -- References -- Chapter 10 Artificial Intelligence and Machine Learning-Based Manufacturing and Drug Product Marketing -- Abbreviations -- 10.1 Introduction to Artificial Intelligence and Machine Learning -- 10.1.1 AI and ML in Pharmaceutical Manufacturing -- 10.1.2 AI and ML in Drug Product Marketing -- 10.2 Different Applications of AI and ML in the Pharma Field -- 10.2.1 Drug Discovery -- 10.2.2 Pharmaceutical Product Development -- 10.2.3 Clinical Trial Design -- 10.2.4 Manufacturing of Drugs -- 10.2.5 Quality Control and Quality Assurance -- 10.2.6 Product Management -- 10.2.7 Drug Prescription -- 10.2.8 Medical Diagnosis -- 10.2.9 Monitoring of Patients -- 10.2.10 Drug Synergism and Antagonism Prediction. 10.2.11 Precision Medicine -- 10.3 AI and ML-Based Manufacturing -- 10.3.1 Continuous Manufacturing -- 10.3.2 Process Improvement and Fault Detection -- 10.3.3 Predictive Maintenance (PdM) -- 10.3.4 Quality Control and Yield -- 10.3.5 Troubleshooting -- 10.3.6 Supply Chain Management -- 10.3.7 Warehouse Management -- 10.3.8 Predicting Remaining Useful Life -- 10.3.9 Challenges -- 10.4 AI and ML-Based Drug Product Marketing -- 10.4.1 Product Launch -- 10.4.2 Real-Time Personalization and Consumer Behavior -- 10.4.3 Better Customer Relationships -- 10.4.4 Enhanced Marketing Measurement -- 10.4.5 Predictive Marketing Analytics -- 10.4.6 Price Dynamics -- 10.4.7 Market Segmentation -- 10.4.8 Challenges -- 10.5 Future Prospects and Way Forward -- 10.6 Conclusion -- References -- Chapter 11 Artificial Intelligence and Machine Learning Applications in Vaccine Development -- 11.1 Introduction -- 11.2 Prioritizing Proteins as Vaccine Candidates -- 11.3 Predicting Binding Scores of Candidate Proteins -- 11.4 Predicting Potential Epitopes -- 11.5 Design of Multi-Epitope Vaccine -- 11.6 Tracking the RNA Mutations of a Virus -- Conclusion -- References -- Chapter 12 AI, ML and Other Bioinformatics Tools for Preclinical and Clinical Development of Drug Products -- Abbreviations -- 12.1 Introduction -- 12.2 AI and ML for Pandemic -- 12.3 Advanced Analytical Tools Used in Preclinical and Clinical Development -- 12.3.1 Spectroscopic Techniques -- 12.3.2 Chromatographic Techniques -- 12.3.3 Electrochemical Techniques -- 12.3.4 Electrophoretic Techniques -- 12.3.5 Hyphenated Techniques -- 12.4 AI, ML, and Other Bioinformatics Tools for Preclinical Development of Drug Products -- 12.4.1 Various Computational Tools Used in Pre-Clinical Drug Development -- 12.5 AI, ML, and Other Bioinformatics Tools for Clinical Development of Drug Products. 12.5.1 Role of AI, ML, and Bioinformatics in Clinical Research -- 12.5.2 Role of AI and ML in Clinical Study Protocol Optimization -- 12.5.3 Role of AI and ML in the Management of Clinical Trial Participants -- 12.5.4 Role of AI and ML in Clinical Trial Data Collection and Management -- 12.6 Way Forward -- 12.7 Conclusion -- References -- Part III: Bioinformatics Tools for Healthcare Sector -- Chapter 13 Artificial Intelligence and Machine Learning in Healthcare Sector -- Abbreviations -- 13.1 Introduction -- 13.2 The Exponential Rise of AI/ML Solutions in Healthcare -- 13.3 AI/ML Healthcare Solutions for Doctors -- 13.4 AI/ML Solution for Patients -- 13.5 AI Solutions for Administrators -- 13.6 Factors Affecting the AI/ML Implementation in the Healthcare Sector -- 13.6.1 High Cost -- 13.6.2 Lack of Creativity -- 13.6.3 Errors Potentially Harming Patients -- 13.6.4 Privacy Issues -- 13.6.5 Increase in Unemployment -- 13.6.6 Lack of Ethics -- 13.6.7 Promotes a Less-Effort Culture Among Human Workers -- 13.7 AI/ML Based Healthcare Start-Ups -- 13.8 Opportunities and Risks for Future -- 13.8.1 Patient Mobility Monitoring -- 13.8.2 Clinical Trials for Drug Development -- 13.8.3 Quality of Electronic Health Records (EHR) -- 13.8.4 Robot-Assisted Surgery -- 13.9 Conclusion and Perspectives -- References -- Chapter 14 Role of Artificial Intelligence in Machine Learning for Diagnosis and Radiotherapy -- Abbreviations -- 14.1 Introduction -- 14.2 Machine Learning Algorithm Models -- 14.2.1 Supervised Learning -- 14.2.2 Unsupervised Learning -- 14.2.3 Semi-Supervised Learning -- 14.2.4 Reinforcement Learning (RL) -- 14.3 Artificial Learning in Radiology -- 14.3.1 Types of Radiation Therapy -- 14.3.1.1 External Radiation Therapy -- 14.3.1.2 Internal Radiation Therapy -- 14.3.1.3 Systemic Radiation Therapy -- 14.3.2 Mechanism of Action. 14.4 Application of Artificial Intelligence and Machine Learning in Radiotherapy. |
| Record Nr. | UNINA-9910830949103321 |
| Hoboken, NJ : , : Wiley, , ℗2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Nanocarrier Vaccines : Biopharmaceutics-Based Fast Track Development
| Nanocarrier Vaccines : Biopharmaceutics-Based Fast Track Development |
| Autore | Chavda Vivek P |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (519 pages) |
| Altri autori (Persone) | ApostolopoulosVasso |
| ISBN | 9781394175468 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Preface -- Part 1 General -- Chapter 1 History of Nanoparticles -- 1.1 Introduction -- 1.2 History of Nanoparticles -- 1.3 Modern Development of Nanoparticles -- 1.4 Type of Nanoparticles -- 1.5 Properties of Nanoparticles -- 1.5.1 Size -- 1.5.2 Shape -- 1.5.3 Surface Area -- 1.6 Importance of Nanoparticles -- 1.7 Conclusion and Future Prospect -- References -- Chapter 2 Composition of Nanoparticles -- 2.1 Introduction -- 2.2 Types of Nanoparticles -- 2.2.1 Polymeric Nanoparticles -- 2.2.1.1 Polymeric Micelles -- 2.2.1.2 Dendrimer -- 2.2.1.3 Nanosphere -- 2.2.1.4 Nanocapsule -- 2.2.1.5 Polymersome -- 2.2.1.6 Nanocomplex -- 2.2.1.7 Nanogel -- 2.2.2 Inorganic Nanoparticle -- 2.2.2.1 Gold Nanoparticle -- 2.2.2.2 Silica Nanoparticle -- 2.2.2.3 Magnetic Nanoparticle -- 2.2.2.4 Quantum Dots -- 2.2.2.5 Nanocarbon -- 2.2.3 Hybrid Nanoparticle -- 2.2.3.1 Cell Membrane Coated Nanoparticle -- 2.2.3.2 Lipid Polymer Nanoparticle -- 2.2.3.3 Organic-Inorganic Nanocomposite -- 2.2.4 Bioinspired Nanoparticle -- 2.2.4.1 Exosomes -- 2.2.4.2 Protein Nanoparticle -- 2.2.4.3 DNA Nanostructure -- 2.2.5 Lipid-Based Nanoparticle -- 2.2.5.1 Liposome -- 2.2.5.2 Lipoplex -- 2.2.5.3 Solid Lipid Nanoparticle -- 2.3 Composition of Nanoparticles -- 2.3.1 Chitosan -- 2.3.2 Albumin -- 2.3.3 Polylactic Acid -- 2.3.4 Polylactide-co-glycolide (PLGA) -- 2.3.5 Polyacrylate -- 2.4 Synthesis of Nanoparticles -- 2.4.1 Top-Down Approach -- 2.4.1.1 Ball Milling -- 2.4.1.2 Physical Vapor Deposition (PVD) -- 2.4.1.3 Melt Mixing -- 2.4.1.4 Pulse Laser Ablation -- 2.4.2 Bottom-Up Approach -- 2.4.2.1 Chemical Vapor Deposition (CVD) -- 2.4.2.2 Thermal Decomposition Method -- 2.4.2.3 Chemical Methods -- 2.4.2.4 Biological Methods -- 2.5 Nanoparticle Characterization by Various Instrumental Techniques.
2.5.1 Dynamic Light Scattering (DLS) -- 2.5.2 Zeta Potential -- 2.5.3 Microscopic Techniques to Characterize Nanoparticles -- 2.5.3.1 Scanning Electron Microscopy (SEM) -- 2.5.3.2 Transmission Electron Microscopy (TEM) -- 2.5.4 Spectroscopic Techniques to Characterize Nanoparticles -- 2.5.4.1 Ultraviolet-Visible Spectroscopy (UV-Vis) -- 2.5.4.2 Raman Spectroscopy -- 2.5.4.3 Fourier Transform Infrared Spectroscopy (FTIR) -- 2.5.5 X-Ray Diffraction Method (XRD) -- 2.6 Understanding Nanotoxicity: Potential Risks and Implications -- 2.7 Conclusion -- References -- Chapter 3 Nanotechnology and Vaccine Development -- 3.1 Introduction -- 3.2 Overview of Vaccine Development -- 3.3 Advantages of Nanoparticles in Vaccine Delivery -- 3.4 Types of Nanoparticles as Vaccine Carriers -- 3.4.1 Liposomes -- 3.4.2 Polymer-Based Nanoparticles -- 3.4.3 Virus-Like Particles (VLPs) -- 3.4.4 Nanogels -- 3.4.5 Inorganic Nanoparticles -- 3.5 Development of Nanoparticle-Based Vaccine -- 3.5.1 Viral Vector-Based Nanoparticle -- 3.5.2 Lipid-Based Nanoparticles -- 3.5.3 DNA-Based Nanoparticles -- 3.5.4 mRNA-Based Nanoparticles -- 3.5.5 Protein-Based Nanoparticles -- 3.6 Adjuvants and their Role in Vaccine Development -- 3.7 Nanoscale Adjuvants -- 3.8 Advantages -- 3.9 Techniques for Nanoscale Adjuvants -- 3.10 Route of Administration for Vaccines -- 3.11 Recent Advances in Nanotechnology-Based Vaccines -- 3.12 The Regulatory Perspective of Nanoparticle-Based Vaccine Development -- 3.13 Future Prospects -- 3.14 Conclusion -- References -- Chapter 4 Nanoparticle Formulations: A Sustainable Approach to Biodegradable and Non-Biodegradable Products -- 4.1 Introduction -- 4.2 Types of Nanoparticles -- 4.3 Preparation of Nanoparticles -- 4.4 Factors Affecting Selection of Method -- 4.4.1 Pressure -- 4.4.2 Particle Shape and Size -- 4.4.3 Environment -- 4.4.4 Pore Size. 4.4.5 Particular Method or Technique -- 4.4.6 Cost of Preparation -- 4.4.7 Proximity -- 4.4.8 Time -- 4.4.9 Other Variables -- 4.5 Polymers Used in NP Formulation -- 4.6 Nanoparticle Formulations Based on Biodegradable Polymers -- 4.7 Nanoparticle Formulations Based on Non-Biodegradable Polymers -- 4.8 Nanoparticle Formulations Based on Natural Polymers -- 4.9 Challenges in NPs from Laboratory to Industrial Scale-Up -- 4.10 Nanoparticle-Based Approved & -- Marketed Formulations -- 4.11 Future Aspects & -- Conclusion -- References -- Chapter 5 Nanoparticle Properties: Size, Shape, Charge, Inertness, Efficacy, Morphology -- 5.1 Introduction -- 5.2 Applications of Nanoparticle Formulations -- 5.3 Interaction with Cells -- 5.4 Properties of Nanoparticles -- 5.4.1 Classification of Nanoparticle Properties -- 5.4.1.1 Physicochemical Properties -- 5.4.1.2 Optical Properties -- 5.4.1.3 Magnetic Properties -- 5.4.1.4 Catalytic Properties -- 5.4.1.5 Mechanical Properties -- 5.4.2 Different Properties -- 5.4.2.1 Size -- 5.4.2.2 Shape -- 5.4.2.3 Charge -- 5.4.2.4 Inertness -- 5.4.2.5 Efficacy -- 5.4.2.6 Morphology -- 5.5 Role of Physicochemical Properties in Nanoparticle Toxicity -- 5.6 Conclusion -- References -- Part 2 Nanoparticles to Deliver Antigen -- Chapter 6 Viral Vector-Based Nanoparticles -- 6.1 Introduction -- 6.2 Characteristics of Viral Vector-Based Nanoparticles -- 6.3 Applications -- 6.3.1 Viral Nanoparticles for Drug Delivery -- 6.3.1.1 Antimicrobial Therapies -- 6.3.1.2 Cardiovascular Therapies -- 6.3.2 Viral Nanoparticles for Imaging -- 6.3.2.1 Nanoparticles are Used in PET/SPECT Scans -- 6.3.2.2 Nanoparticles Used in Ultrasonic Tests -- 6.3.2.3 Nanoparticles Utilized in CT Scans -- 6.3.2.4 Nanoparticles Employed in MRI Biomedical Applications -- 6.3.2.5 Illustrations of Nanoparticles Utilized in Fluorescence-Based Biological Applications. 6.3.3 Viral Nanoparticles for Immunotherapy -- 6.3.4 Viral Nanoparticles for Theranostic Applications -- 6.4 Novel Advancements in Applications of Viral Nanoparticles -- 6.5 Limitations and Prospects of Viral Vector-Based Nanoparticle Approach -- 6.6 Conclusion -- Acknowledgment -- References -- Chapter 7 Lipid-Based Nanoparticles -- 7.1 Introduction -- 7.2 Types of Lipid-Based Nanoparticles -- 7.2.1 Solid Lipid Nanoparticles (SLNs) -- 7.2.2 Nanostructured Lipid Carriers (NLCs) -- 7.3 Synthesis of Lipid-Based Nanoparticles -- 7.3.1 Introduction to Lipids -- 7.3.2 Methods for Formulating Lipid Nanoparticles -- 7.3.2.1 High-Pressure Homogenization -- 7.3.2.2 Solvent Emulsification-Evaporation -- 7.3.2.3 Microemulsion-Based Method -- 7.3.2.4 Hot-Melt Homogenization -- 7.3.2.5 Spray Drying -- 7.3.2.6 Solvent Injection Method -- 7.3.2.7 Microfludics -- 7.4 Characterization of Lipid Nanoparticles -- 7.4.1 Size and Shape -- 7.4.2 Surface Charge -- 7.4.2.1 Analytical Techniques for Surface Charge Characterization -- 7.4.2.2 Zeta Potential Measurement -- 7.4.2.3 Electrophoresis -- 7.4.2.4 Isoelectric Focusing -- 7.4.3 Encapsulation Efficiency -- 7.4.3.1 Factors Affecting Encapsulation Efficiency -- 7.4.3.2 Analytical Techniques for Encapsulation Efficiency Characterization -- 7.4.4 Stability -- 7.4.4.1 Factors Affecting Stability -- 7.4.4.2 Analytical Techniques for Stability Characterization -- 7.5 Applications of Lipid-Based Nanoparticles in Vaccines -- 7.5.1 Enhancement of Immune Response -- 7.5.2 Targeted Delivery -- 7.5.2.1 Cancer Immunotherapy -- 7.5.2.2 mRNA-Based Vaccines -- 7.5.2.3 Gene Therapy -- 7.5.3 Adjuvant Effects -- 7.5.3.1 mRNA COVID-19 Vaccines -- 7.5.3.2 Human Papillomavirus (HPV) Vaccine -- 7.5.3.3 Influenza Vaccine -- 7.6 Challenges and Future Directions -- 7.6.1 Safety and Toxicity Concerns -- 7.6.1.1 Preclinical Safety Evaluation. 7.6.1.2 Human Pharmacology Studies -- 7.6.1.3 Postmarketing Surveillance -- 7.6.1.4 Adverse Event Reporting -- 7.6.2 Stability Issues -- 7.6.2.1 Formulation Optimization -- 7.6.2.2 Analytical Method Development -- 7.6.2.3 Accelerated Stability Studies -- 7.6.2.4 Quality by Design (QbD) -- 7.6.3 Scale-Up Production Challenges -- 7.6.3.1 Equipment Design -- 7.6.3.2 Process Optimization -- 7.6.3.3 Regulatory Compliance -- 7.6.4 Opportunities for Future Research -- 7.6.4.1 Novel Antigen and Adjuvant Formulations -- 7.6.4.2 Targeted Delivery -- 7.6.4.3 Manufacturing Process Optimization -- 7.6.4.4 Immunological Mechanisms -- 7.6.4.5 Opportunities for Future Research -- 7.7 Conclusion -- References -- Chapter 8 Nanoparticle-Based mRNA Vaccines: Are We One Step Closer to Targeted Cancer Therapy? -- 8.1 Introduction -- 8.2 Use of mRNA in Vaccines: Advantages and Challenges -- 8.3 How Do mRNA Vaccines Work? -- 8.4 Nanocarriers for mRNA Delivery -- 8.4.1 Liposomes and RNA Lipoplexes -- 8.4.2 Lipid Nanoparticles -- 8.4.3 Polymer-Based Nanoparticles -- 8.4.4 Hybrid Nanoparticles -- 8.5 Nanoparticle-Based mRNA Vaccines in Cancer Therapy -- 8.5.1 Breast Cancer -- 8.5.2 Colorectal Cancer -- 8.5.3 Lung Cancer -- 8.5.4 Glioma Tumor -- 8.5.5 Other Tumors -- 8.6 Clinical Trials -- 8.6.1 Considerations for Clinical Translation -- 8.7 Conclusion -- References -- Chapter 9 Protein Delivery by Nanoparticles -- 9.1 Introduction -- 9.2 Major Challenges in Protein Delivery -- 9.3 Nanotechnology -- 9.4 Nanoparticles -- 9.4.1 Nanocarriers -- 9.4.2 Protein Nanocarrier -- 9.4.3 Protein and Its Type Used to Produce Protein Nanoparticles -- 9.4.3.1 Silk Protein Fibroin -- 9.4.3.2 Human Serum Albumin -- 9.4.3.3 Gliadin -- 9.4.3.4 Gelatin -- 9.4.3.5 Legumin -- 9.4.3.6 30Kc19 Protein Obtained from Silkworm Hemolymph -- 9.4.3.7 Ferritin -- 9.5 Methods of Preparation. 9.5.1 Chemical Methods. |
| Record Nr. | UNINA-9910877230703321 |
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Chavda Vivek P
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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