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Reverse Vaccinology
Concept, Methods and Advancement
- 1st Edition - July 6, 2024
- Editors: Jayashankar Das, Sushma Dave, Siomar de Castro Soares, Sandeep Tiwari
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 3 3 9 5 - 4
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 3 3 9 6 - 1
Reverse Vaccinology: Concept, Methods, and Advancement presents the development strategy of new vaccines through genome sequencing bioinformatics analysis. This book promis… Read more
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Request a sales quoteReverse Vaccinology: Concept, Methods, and Advancement presents the development strategy of new vaccines through genome sequencing bioinformatics analysis. This book promises to revolutionize vaccine development, especially for pathogens to which the classical applications of Pasteur’s principles have failed, and it is explained in detail in this book.
This book is split into three sections: the first, Concept, brings the basis of reverse vaccinology, vaccine antigen discovery, and subunit vaccine; the second, Tools and Methods, describes immunoinformatic, proteomics for epitope-vaccine design, databases, network analysis, machine learning, and NGS-driven antigen screening technology; and the last one, Disease Case Study, discusses real-world examples in the development of new vaccines for diverse diseases.
It is a valuable resource for bioinformaticians, researchers, students, and members of the biomedical and medical fields who want to learn more about a new and agile process for the development of new vaccines.
This book is split into three sections: the first, Concept, brings the basis of reverse vaccinology, vaccine antigen discovery, and subunit vaccine; the second, Tools and Methods, describes immunoinformatic, proteomics for epitope-vaccine design, databases, network analysis, machine learning, and NGS-driven antigen screening technology; and the last one, Disease Case Study, discusses real-world examples in the development of new vaccines for diverse diseases.
It is a valuable resource for bioinformaticians, researchers, students, and members of the biomedical and medical fields who want to learn more about a new and agile process for the development of new vaccines.
- Explains the fundamentals of reverse vaccinology and how it can save time in the development of new vaccines
- Focuses on the efforts to develop a vaccine candidate against various pathogens using computational approaches
- Presents databases and web servers for conducting reverse vaccinology
- Describes the screening process of potential vaccine candidate through machine learning
Graduate students and researchers in bioinformatics and systems biology, bioinformaticians, Graduate students and researchers in biotechnology
Chapter 1: Personalized vaccinology
Samiksha Garse, Sneha Dokhale, Gurnain Kaur Bhandari, Vishwa Kapadiya, Kavya Prabhakar, and Shine Devarajan
1 Introduction
1.1 Vaccinology and vaccines: Back in time
1.2 Traditional vaccinology
2 Factors of immunological response to vaccines
2.1 Age
2.2 Gender
2.3 Obesity
3 Limitations of current vaccinology
3.1 Inadequate knowledge of immunity development
3.2 Host and pathogen variability
3.3 Physical and psychological factors
4 Technical advances and big data impact on vaccinology
5 Personalized vaccinology
5.1 Precision immunization
6 Important factors, salient features, and benefits of personalized vaccinology
6.1 Genetic polymorphism and immune response
6.2 Reverse vaccinology for personalized markers
7 Current status of personalized vaccinology
7.1 Designing personalized vaccines
7.2 Personalized vaccines for different disease types
7.3 Personalized cancer vaccines targeting the cancer mutanome (neoantigens)
8 Benefits of personalized vaccinology
9 Challenges
10 Conclusion
References
Chapter 2: Novel vaccine adjuvants formulations and mechanisms of action
Susu M. Zughaier, Amna Hashim, Nidal H. Khodr, Abdul Rahman Al Abiad, and Mohannad Abu Haweeleh
1 Introduction
1.1 History of vaccination
1.2 Vaccine adjuvants
2 Licensed adjuvants
3 Novel adjuvants formulation patents
3.1 Amino acids and peptides
3.2 Nucleotides and nucleic acids
3.3 Bacterial and viral protein components
3.4 Ligands for cell surface receptors
3.5 Cytokines and chemokines
3.6 Polymers
3.7 Saponins and tensoactive compounds
4 Novel vaccines technologies
4.1 Recombinant protein vaccines
4.2 Synthetic peptide vaccines
4.3 Nucleic acid vaccines
4.4 RNA vaccines
5 Vaccine adjuvants and metabolomics
6 Vaccine adjuvants for personalized vaccine design
7 Conclusion
Conflict of interest
References
Chapter 3: Technologies to measure vaccine immune response against infectious diseases
Mahbuba Rahman
1 Introduction
2 Types of immunization
2.1 Vaccines against infections
3 Immune responses to vaccines
3.1 Innate immune response
3.2 Adaptive immune response
3.3 Humoral immune response
4 Conventional technologies to detect vaccine response
4.1 Neutralizing antibody assay
4.2 Effector functions of antibodies
4.3 Enzyme-linked immunosorbent assay
5 Conventional high-throughput technologies to measure vaccine immune response
5.1 Enzyme-linked immunospot (ELISPOT)
5.2 Cytokine multiplex
5.3 Multiparametric flow cytometry
5.4 Time-of-flight mass cytometry—CyTOF
6 High-throughput technologies to detect vaccine immune response
6.1 Genomics
6.2 Transcriptomics
6.3 Proteomics
6.4 Metabolomics
7 Metabolomics of immune cells in response to vaccination
7.1 Metabolomics of T cells
7.2 Metabolomics of B cells
7.3 Metabolomics of B-cell and T-cell collaboration
8 Selected example of vaccine-induced metabolites
8.1 Metabolites in response to vaccine immunogenicity
8.2 Metabolites in response to adverse event following immunization (AEFI)
9 Current challenge and future direction
References
Chapter 4: Vaccines for cancer
Farah Ayman Sukareh, Ruba Al-Nemi, Peter Karagiannis, Hiba Nabil Asfour, Amita Verma, Mariusz Jaremko, and Abdul-Hamid Emwas
1 Introduction
2 Cancer statistics
2.1 Cancer rates
2.2 Cancer causes
2.3 Cancer prevention
3 Cancer treatment
4 Cancer treatment modalities
4.1 Conventional cancer therapies
4.2 Advanced cancer therapies
5 Overview of vaccines
5.1 Types of vaccines
6 Importance of vaccination
7 Cancer vaccines
7.1 Preventive vaccines
7.2 Therapeutic cancer vaccines
8 Cancer antigen targets
8.1 Tumor-associated antigens (TAAs)
8.2 Tumor-specific antigens (TSAs)
8.3 Cancer germline antigens (CGAs)
8.4 Virus-associated antigens
9 The tumor-immune cycle induced by cancer vaccines
10 Cancer vaccine platforms
10.1 Cell vaccines (tumor cell vaccines and dendritic cell vaccines)
10.2 Protein/peptide vaccines
10.3 Nucleic acid vaccines (DNA, RNA, and viral vectors)
11 Importance of adjuvants with cancer vaccines
12 Classification of adjuvants
12.1 Immunostimulatory adjuvants
12.2 Delivery system adjuvants
13 Barriers to vaccine therapy: Immune resistance
13.1 Tumor-intrinsic resistance
13.2 Tumor-extrinsic resistance
14 Combination approaches to increase cancer vaccine effectiveness
14.1 Combining several adjuvants to increase the scope of immune responses
14.2 Combining chemotherapy with cancer vaccines
14.3 Improving patient lifestyle to increase vaccine potency
14.4 Combining cancer vaccines with other cancer treatments
15 Metabolic profiling for vaccine development
16 The current status of other cancer immunotherapies
16.1 Tumor-infiltrating lymphocyte (TIL) therapy
16.2 CAR-T-cell therapy
16.3 Oncolytic virotherapy (OV)
17 Summary and future perspectives
Acknowledgments
References
Chapter 5: Vaccines against autoimmune diseases
Divya Jyothi Madipally and Janna R Pathi
1 Introduction
1.1 Etiology of autoimmune diseases
1.2 Role of metabolomics in autoimmune diseases
2 Rheumatoid arthritis
2.1 Role of metabolomics in RA
2.2 Vaccines against RA
3 Systemic lupus erythematosus
3.1 Etiology and pathophysiology of SLE
3.2 Metabolomics in SLE
3.3 Treatment for SLE
3.4 Vaccines against SLE
4 Psoriasis
4.1 Current treatment strategies for psoriasis
4.2 Vaccines against psoriasis
5 Multiple sclerosis
5.1 Metabolomics research on MS
5.2 Vaccines against MS
6 Myasthenia gravis
6.1 Vaccines against MG
7 Type 1 diabetes
7.1 Pathophysiology of T1D
7.2 Metabolomics in T1D
7.3 Vaccines against T1D
8 Critical discussion
References
Chapter 6: Vaccines for allergy
Mahbuba Rahman
1 Introduction
2 Involvement of the immune system in allergic diseases
3 Genetic and epigenetic factors associated with allergic disease
4 Therapeutic approaches for allergic diseases
4.1 Construction of AIT
4.2 Formulations to delivery AIT
5 Routes of administration of immunotherapy
6 Immune tolerance to allergens
6.1 Immune regulation to allergy immunotherapy
6.2 Selected example of immune tolerance by AIT
6.3 Predictive biomarkers of AIT
7 Metabolomics of immune cells during allergy inflammation
8 Metabolomics for identification of biomarkers in allergic diseases
8.1 Allergic rhinitis
8.2 Asthma
8.3 Food allergy
9 Examples of studies where metabolomics might reveal prognostic biomarkers related to AIT treatment
9.1 SCIT for allergy asthma
9.2 SCIT for HDM
9.3 SLIT for AR
9.4 SCIT for pollen allergy
10 Conclusion
References
Chapter 7: Virus vaccine production using cell-based technology
Mahbuba Rahman
1 Introduction
2 Vaccine manufacture based on cell culture
2.1 Selection and development of cell lines
2.2 Culture media
2.3 Microcarrier culture
3 Process optimization and production of virus in bioreactor
3.1 Process optimization to increase virus yield
3.2 Physical and chemical parameters
3.3 Monitoring systems
4 Bioprocess scale up
4.1 Bioprocess operation mode
4.2 Metabolomics of cell culture in bioreactors
4.3 Large-scale bioreactors for cell culture
5 Large-scale cultures of different types of viruses
5.1 SARS-CoV-2
5.2 Influenza virus
5.3 Poliovirus
5.4 Tropical virus
5.5 Enterovirus
5.6 Rabies virus
6 Conclusion
References
Chapter 8: Dendritic cell-based cancer vaccine production
Mahbuba Rahman
1 Introduction
2 Overview of DCs in the immune system
3 DCs in the tumor microenvironment
3.1 Metabolomics of DC in TME
4 Vaccination for therapeutic purposes employing DCs
4.1 Ex vivo approach
4.2 DC maturation/stimulation approaches
4.3 Antigen loading
4.4 Route of administration, migration, and preconditioning of the vaccine site
4.5 Assessment of the end product and conducting quality control examinations
4.6 In vivo targeting
5 Challenges
5.1 Dysregulation of DC in TME
5.2 Metabolic factors of the TME affecting the efficacy of DC vaccine
6 Conclusion
Samiksha Garse, Sneha Dokhale, Gurnain Kaur Bhandari, Vishwa Kapadiya, Kavya Prabhakar, and Shine Devarajan
1 Introduction
1.1 Vaccinology and vaccines: Back in time
1.2 Traditional vaccinology
2 Factors of immunological response to vaccines
2.1 Age
2.2 Gender
2.3 Obesity
3 Limitations of current vaccinology
3.1 Inadequate knowledge of immunity development
3.2 Host and pathogen variability
3.3 Physical and psychological factors
4 Technical advances and big data impact on vaccinology
5 Personalized vaccinology
5.1 Precision immunization
6 Important factors, salient features, and benefits of personalized vaccinology
6.1 Genetic polymorphism and immune response
6.2 Reverse vaccinology for personalized markers
7 Current status of personalized vaccinology
7.1 Designing personalized vaccines
7.2 Personalized vaccines for different disease types
7.3 Personalized cancer vaccines targeting the cancer mutanome (neoantigens)
8 Benefits of personalized vaccinology
9 Challenges
10 Conclusion
References
Chapter 2: Novel vaccine adjuvants formulations and mechanisms of action
Susu M. Zughaier, Amna Hashim, Nidal H. Khodr, Abdul Rahman Al Abiad, and Mohannad Abu Haweeleh
1 Introduction
1.1 History of vaccination
1.2 Vaccine adjuvants
2 Licensed adjuvants
3 Novel adjuvants formulation patents
3.1 Amino acids and peptides
3.2 Nucleotides and nucleic acids
3.3 Bacterial and viral protein components
3.4 Ligands for cell surface receptors
3.5 Cytokines and chemokines
3.6 Polymers
3.7 Saponins and tensoactive compounds
4 Novel vaccines technologies
4.1 Recombinant protein vaccines
4.2 Synthetic peptide vaccines
4.3 Nucleic acid vaccines
4.4 RNA vaccines
5 Vaccine adjuvants and metabolomics
6 Vaccine adjuvants for personalized vaccine design
7 Conclusion
Conflict of interest
References
Chapter 3: Technologies to measure vaccine immune response against infectious diseases
Mahbuba Rahman
1 Introduction
2 Types of immunization
2.1 Vaccines against infections
3 Immune responses to vaccines
3.1 Innate immune response
3.2 Adaptive immune response
3.3 Humoral immune response
4 Conventional technologies to detect vaccine response
4.1 Neutralizing antibody assay
4.2 Effector functions of antibodies
4.3 Enzyme-linked immunosorbent assay
5 Conventional high-throughput technologies to measure vaccine immune response
5.1 Enzyme-linked immunospot (ELISPOT)
5.2 Cytokine multiplex
5.3 Multiparametric flow cytometry
5.4 Time-of-flight mass cytometry—CyTOF
6 High-throughput technologies to detect vaccine immune response
6.1 Genomics
6.2 Transcriptomics
6.3 Proteomics
6.4 Metabolomics
7 Metabolomics of immune cells in response to vaccination
7.1 Metabolomics of T cells
7.2 Metabolomics of B cells
7.3 Metabolomics of B-cell and T-cell collaboration
8 Selected example of vaccine-induced metabolites
8.1 Metabolites in response to vaccine immunogenicity
8.2 Metabolites in response to adverse event following immunization (AEFI)
9 Current challenge and future direction
References
Chapter 4: Vaccines for cancer
Farah Ayman Sukareh, Ruba Al-Nemi, Peter Karagiannis, Hiba Nabil Asfour, Amita Verma, Mariusz Jaremko, and Abdul-Hamid Emwas
1 Introduction
2 Cancer statistics
2.1 Cancer rates
2.2 Cancer causes
2.3 Cancer prevention
3 Cancer treatment
4 Cancer treatment modalities
4.1 Conventional cancer therapies
4.2 Advanced cancer therapies
5 Overview of vaccines
5.1 Types of vaccines
6 Importance of vaccination
7 Cancer vaccines
7.1 Preventive vaccines
7.2 Therapeutic cancer vaccines
8 Cancer antigen targets
8.1 Tumor-associated antigens (TAAs)
8.2 Tumor-specific antigens (TSAs)
8.3 Cancer germline antigens (CGAs)
8.4 Virus-associated antigens
9 The tumor-immune cycle induced by cancer vaccines
10 Cancer vaccine platforms
10.1 Cell vaccines (tumor cell vaccines and dendritic cell vaccines)
10.2 Protein/peptide vaccines
10.3 Nucleic acid vaccines (DNA, RNA, and viral vectors)
11 Importance of adjuvants with cancer vaccines
12 Classification of adjuvants
12.1 Immunostimulatory adjuvants
12.2 Delivery system adjuvants
13 Barriers to vaccine therapy: Immune resistance
13.1 Tumor-intrinsic resistance
13.2 Tumor-extrinsic resistance
14 Combination approaches to increase cancer vaccine effectiveness
14.1 Combining several adjuvants to increase the scope of immune responses
14.2 Combining chemotherapy with cancer vaccines
14.3 Improving patient lifestyle to increase vaccine potency
14.4 Combining cancer vaccines with other cancer treatments
15 Metabolic profiling for vaccine development
16 The current status of other cancer immunotherapies
16.1 Tumor-infiltrating lymphocyte (TIL) therapy
16.2 CAR-T-cell therapy
16.3 Oncolytic virotherapy (OV)
17 Summary and future perspectives
Acknowledgments
References
Chapter 5: Vaccines against autoimmune diseases
Divya Jyothi Madipally and Janna R Pathi
1 Introduction
1.1 Etiology of autoimmune diseases
1.2 Role of metabolomics in autoimmune diseases
2 Rheumatoid arthritis
2.1 Role of metabolomics in RA
2.2 Vaccines against RA
3 Systemic lupus erythematosus
3.1 Etiology and pathophysiology of SLE
3.2 Metabolomics in SLE
3.3 Treatment for SLE
3.4 Vaccines against SLE
4 Psoriasis
4.1 Current treatment strategies for psoriasis
4.2 Vaccines against psoriasis
5 Multiple sclerosis
5.1 Metabolomics research on MS
5.2 Vaccines against MS
6 Myasthenia gravis
6.1 Vaccines against MG
7 Type 1 diabetes
7.1 Pathophysiology of T1D
7.2 Metabolomics in T1D
7.3 Vaccines against T1D
8 Critical discussion
References
Chapter 6: Vaccines for allergy
Mahbuba Rahman
1 Introduction
2 Involvement of the immune system in allergic diseases
3 Genetic and epigenetic factors associated with allergic disease
4 Therapeutic approaches for allergic diseases
4.1 Construction of AIT
4.2 Formulations to delivery AIT
5 Routes of administration of immunotherapy
6 Immune tolerance to allergens
6.1 Immune regulation to allergy immunotherapy
6.2 Selected example of immune tolerance by AIT
6.3 Predictive biomarkers of AIT
7 Metabolomics of immune cells during allergy inflammation
8 Metabolomics for identification of biomarkers in allergic diseases
8.1 Allergic rhinitis
8.2 Asthma
8.3 Food allergy
9 Examples of studies where metabolomics might reveal prognostic biomarkers related to AIT treatment
9.1 SCIT for allergy asthma
9.2 SCIT for HDM
9.3 SLIT for AR
9.4 SCIT for pollen allergy
10 Conclusion
References
Chapter 7: Virus vaccine production using cell-based technology
Mahbuba Rahman
1 Introduction
2 Vaccine manufacture based on cell culture
2.1 Selection and development of cell lines
2.2 Culture media
2.3 Microcarrier culture
3 Process optimization and production of virus in bioreactor
3.1 Process optimization to increase virus yield
3.2 Physical and chemical parameters
3.3 Monitoring systems
4 Bioprocess scale up
4.1 Bioprocess operation mode
4.2 Metabolomics of cell culture in bioreactors
4.3 Large-scale bioreactors for cell culture
5 Large-scale cultures of different types of viruses
5.1 SARS-CoV-2
5.2 Influenza virus
5.3 Poliovirus
5.4 Tropical virus
5.5 Enterovirus
5.6 Rabies virus
6 Conclusion
References
Chapter 8: Dendritic cell-based cancer vaccine production
Mahbuba Rahman
1 Introduction
2 Overview of DCs in the immune system
3 DCs in the tumor microenvironment
3.1 Metabolomics of DC in TME
4 Vaccination for therapeutic purposes employing DCs
4.1 Ex vivo approach
4.2 DC maturation/stimulation approaches
4.3 Antigen loading
4.4 Route of administration, migration, and preconditioning of the vaccine site
4.5 Assessment of the end product and conducting quality control examinations
4.6 In vivo targeting
5 Challenges
5.1 Dysregulation of DC in TME
5.2 Metabolic factors of the TME affecting the efficacy of DC vaccine
6 Conclusion
- No. of pages: 600
- Language: English
- Edition: 1
- Published: July 6, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780443133954
- eBook ISBN: 9780443133961
JD
Jayashankar Das
Dr. Das received his PhD in biotechnology and served as a Scientist at the IBSD, DBT, Government of India. He is the Founder and CEO of Valnizen which deals with regulatory documents and healthcare compliances and support services to African and southeast Asian countries. He has served as a Joint Director of the Gujarat State Biotechnology Mission, DST, and Joint Director to Gujarat Biotechnology Research Centre, DST, both from the Government of Gujarat. He has served as a Director of the Savli Technology and Business Incubator, DST, Government of Gujarat, India. He was actively involved in the development and implementation of various policies and action plans like biotechnology policy, innovation policy, interpole disaster management policy, start-up policy for many universities and governments. His research team is involved in addressing societal challenges via cutting-edge research, namely, the development of molecular diagnostics for infectious diseases, the development of universal vaccine candidate for emerging diseases, the development of miRNA-based targeted therapeutics, and artificial intelligence in healthcare applications.
Affiliations and expertise
Director of Research, Valnizen Healthcare Pvt Ltd, IndiaSD
Sushma Dave
Dr. Sushma Dave received a master of science and PhD in analytical, electrochemistry, and environmental chemistry from the Biosensor Lab in the Chemistry Department of Jai Narayan Vyas University, Jodhpur. She is involved continuously in the field of higher education teaching pure, applied chemistry, cheminformatics, nanotechnology, electrochemistry, biology, solid waste management, wastewater treatment, and environmental chemistry to students of engineering and basic sciences. She also served as a Research Associate in the Soil Biochemistry and Microbiology Division, CAZRI, Jodhpur. She has published and presented over 50 papers in international and national journals, conferences and participated in various workshops and training programs. Her areas of interest are electrochemistry, biosensors environmental science, nanotechnology, biochemistry, cheminformatics, immunoinformatics, and drug repurposing.
Affiliations and expertise
Associate Professor, Department of Applied Sciences, JIET, IndiaSS
Siomar de Castro Soares
Dr. Soares obtained his MSc and PhD in genetics from the Federal University of Minas Gerais with 1-year experience in the Center for Biotechnology (CeBiTec) of Universität Bielefeld. He obtained a second PhD and postdoc research in bioinformatics. He was a Senior Bioinformatics’ Researcher at the Official Laboratory of the Ministry of Fisheries. Dr. Soares is currently Secretary of the Southeast Regional of the Brazilian Society of Genetics (2017–present), Substitute Coordinator of the Bachelor’s in Biomedicine of UFTM (2017–present), Director of the Department for the Development of Research and Technological Innovation of UFTM (2019–present), and Affiliate Member of the Brazilian Academy of Sciences (2018–present). His areas of expertise are molecular genetics, genomic sequencing, and microbial comparative genomics, mainly focused on pan-genomics, the role of pathogenicity islands and virulence factors in genome plasticity, phylogenomics, molecular epidemiology, reverse vaccinology, and software development (Perl and Java languages).
Affiliations and expertise
Department of Microbiology, Immunology and Parasitology, Institute of Biological and Natural Sciences, Federal University of Triângulo Mineiro, Uberaba, MG, BrazilST
Sandeep Tiwari
Dr. Tiwari obtained his BSc from Deen Dayal Upadhyaya Gorakhpur University, MSc in bioinformatics from Devi Ahilya University, and PhD in bioinformatics from the Department of Genetics, Ecology and Evolution, Federal University of Minas Gerais, Brazil. He is a Guest Associate Editorial Board of Frontier in Genetics and frequently serves as an external reviewer for PeerJ and Frontiers. His main areas of expertise are molecular genetics, genomic sequencing, and comparative genomics of microorganisms, with a focus on pan-genomics, genomic plasticity in the identification of pathogenicity islands and virulence factors for drug discovery, phylogenomic, molecular epidemiology, reverse vaccinology, and immunoinformatic.
Affiliations and expertise
Visiting Adjunct Professor, Department of Microbiology, Institute of Biology, Federal University of Bahia, Salvador, BA, Brazil; Department of Immunology, Institute of Health Sciences, Federal University of Bahia, Salvador, BA, BrazilRead Reverse Vaccinology on ScienceDirect