Translational Biotechnology: A Journey from Laboratory to Clinics

Cover
Translational Biotechnology
Copyright
Contents
List of contributors
Preface
1 Translational biotechnology: A transition from basic biology to evidence-based research
	1.1 Introduction
		1.1.1 Background and emergence of the field
	1.2 The phases of translational research
	1.3 Challenges to solutions
	1.4 Applications
		1.4.1 Drug development
			1.4.1.1 Protein drugs
			1.4.1.2 Hormones
			1.4.1.3 Monoclonal antibodies
			1.4.1.4 Cytokines
			1.4.1.5 Vaccines
		1.4.2 Nanomedicine
		1.4.3 Gene therapy
		1.4.4 Precision medicine and biomarker development
		1.4.5 Microbial engineering for bio-therapeutics
		1.4.6 Application of big data and translational bioinformatics
	1.5 Conclusion and future directions
	1.6 Highlights
	Conflict of interest
	References
2 Biotechnology-based therapeutics
	2.1 Introduction
	2.2 Human gene therapy
		2.2.1 Somatic cell gene therapy
		2.2.2 Germline gene therapy
		2.2.3 Gene transfer system
			2.2.3.1 Nonbiological delivery system
				2.2.3.1.1 Physical method
					Sonoporation
					Electroporation
					Magnetofection
					Hydroporation
					Gene gun
				2.2.3.1.2 Chemical method
					Liposomes
					Polymers
					Heat shock
				2.2.3.1.3 Biological method
					Bacterial vector
					Viral vector
					Retroviral vectors
					Adenoviral vectors
					Adeno-associated vectors
					Herpes simplex virus
		2.2.4 Gene-editing technology
			2.2.4.1 Zinc-finger nucleases
			2.2.4.2 Transcription activator-like effector nucleases
			2.2.4.3 Clustered regularly interspaced short palindromic repeat–Cas-associated nucleases
		2.2.5 Ethical issue
	2.3 Stem cell therapy
		2.3.1 Sources of stem cells
			2.3.1.1 Pluripotent stem cells
			2.3.1.2 Multipotent stem cells
		2.3.2 Benefits of stem cell therapy in various disorder
			2.3.2.1 Retinal diseases
			2.3.2.2 Heart diseases
			2.3.2.3 Neural disease
			2.3.2.4 Lung disorder
			2.3.2.5 Liver disease
		2.3.3 Challenges and problems
	2.4 Nanomedicine
		2.4.1 Nano therapeutic applications
			2.4.1.1 Nano drug delivery
				2.4.1.1.1 Hydrogel
				2.4.1.1.2 Micelle
				2.4.1.1.3 Dendrimers
				2.4.1.1.4 Polymers
				2.4.1.1.5 Liposomes
			2.4.1.2 Nanosensor
		2.4.2 Tissue engineering
		2.4.3 Nanoimaging
	2.5 Drug designing and delivery
		2.5.1 Rational drug design
		2.5.2 Computer-aided drug design
			2.5.2.1 In silico drug design
			2.5.2.2 Machine learning in drug design
				2.5.2.2.1 Artificial intelligence in drug design
				2.5.2.2.2 Artificial neural network in drug design
		2.5.3 Drug delivery
	2.6 Recombinant therapeutic proteins and vaccines
		2.6.1 Recombinant protein
		2.6.2 Expression system
			2.6.2.1 Bacteria
			2.6.2.2 Yeast
			2.6.2.3 Mammals
		2.6.3 Recombinant protein as a treatment
			2.6.3.1 Anemia
			2.6.3.2 Diabetes
			2.6.3.3 Human growth hormone
			2.6.3.4 Hepatitis B
			2.6.3.5 Ovulation and pregnancy
			2.6.3.6 Gene therapy
		2.6.4 Recombinant vaccine
			2.6.4.1 Live-attenuated vaccine
			2.6.4.2 Subunit vaccine
			2.6.4.3 Vector vaccine
	2.7 Conclusion and future applications
	Conflicts of interest
	Author’s contribution
	References
3 Advanced biotechnology-based therapeutics
	3.1 Introduction
	3.2 Technologies that lead to the discovery of therapy
		3.2.1 Genome editing technologies
		3.2.2 Role of nanomedicine in drug discovery approaches
		3.2.3 Antibody–drug conjugates
	3.3 Molecular diagnostics
		3.3.1 Translational bioinformatics
		3.3.2 Organoids—tools for disease models
	3.4 Cell-based therapy
	3.5 Nanotechnology and its uses in biomedicine
	3.6 Genome-scale metabolic modeling
	3.7 Critical processes in the flow from basic science to practical application in the clinic via clinical trials and transl...
	3.8 Major pitfalls in translational research
	3.9 Advancement in devices, biologics, and vaccines as an introduction to biotechnology products that are being used in therapy
	3.10 Conclusion and summary
	References
4 Human in vitro disease models to aid pathway and target discovery for neurological disorders
	4.1 Introduction
	4.2 Generation of human disease models using iPSCs/patient fibroblasts
		4.2.1 Directed differentiation into neural cells
		4.2.2 Direct differentiation into neurons/glia
		4.2.3 Direct lineage reprogramming/transdifferentiation into neurons
	4.3 Modeling neurodevelopmental disorders
		4.3.1 Rett syndrome
		4.3.2 Fragile X syndrome
		4.3.3 Autism spectrum disorders
		4.3.4 Schizophrenia
	4.4 Modeling neurodegenerative diseases
		4.4.1 Amyotrophic lateral sclerosis
		4.4.2 Alzheimer’s disease
		4.4.3 Parkinson’s disease
	4.5 Cerebral organoids and the future of human in vitro disease modeling
	4.6 From bench to bedside—identification of pathways and drug targets for designing therapies
	4.7 Future perspectives
	Keyword definitions
	References
5 Importance of targeted therapies in acute myeloid leukemia
	5.1 Introduction
		5.1.1 Conventional therapy for acute myeloid leukemia
		5.1.2 Significance of target discovery
	5.2 Approaches in target discovery
		5.2.1 Systems approach
			5.2.1.1 Zebrafish (Danio rerio)
			5.2.1.2 Mouse (Mus musculus)
		5.2.2 Molecular approach
			5.2.2.1 Proteomic technologies
				5.2.2.1.1 Antibody-based approaches
					5.2.2.1.1.1 Immunophenotyping
					5.2.2.1.1.2 Multiparameter flow cytometry
					5.2.2.1.1.3 Mass cytometry
					5.2.2.1.1.4 Antibody arrays
				5.2.2.1.2 Mass spectrometry–based approaches
					5.2.2.1.2.1 Two-dimensional difference gel electrophoresis
					5.2.2.1.2.2 Stable isotope labeling with amino acids in cell culture
					5.2.2.1.2.3 Isotope-coded affinity tags
					5.2.2.1.2.4 Isobaric tags for relative and absolute quantification
					5.2.2.1.2.5 Multiple reaction monitoring mass spectrometry
			5.2.2.2 Genomic technologies
				5.2.2.2.1 Next-generation sequencing
					5.2.2.2.1.1 Whole-genome sequencing
					5.2.2.2.1.2 Exome sequencing
					5.2.2.2.1.3 Transcriptome sequencing
				5.2.2.2.2 Microarray
				5.2.2.2.3 RNA interference
				5.2.2.2.4 Genome-editing technologies
					5.2.2.2.4.1 Zinc-finger nucleases and transcription activator-like effector nucleases
					5.2.2.2.4.2 CRISPR/Cas system
	5.3 Acute myeloid leukemia–targeted therapies in clinics
		5.3.1 BCL-2 inhibitors
		5.3.2 Isocitrate dehydrogenase inhibitors
		5.3.3 PML-RARα targeted therapy
		5.3.4 Targeting FLT3-mutated acute myeloid leukemia: from bench to bedside (a case study)
	5.4 Hurdles and emerging targeted therapies
	5.5 Conclusion
	References
6 Biological therapeutic modalities
	6.1 Introduction to biological therapeutic modalities
	6.2 History of classical modalities
	6.3 New modalities
		6.3.1 Small molecules
		6.3.2 Nucleic acid therapeutics
		6.3.3 Therapeutic proteins
			6.3.3.1 Therapeutic peptides
			6.3.3.2 Therapeutic enzymes
		6.3.4 Antibodies
			6.3.4.1 Monoclonal antibodies
			6.3.4.2 Engineered multispecific antibodies
		6.3.5 Cell-based immunotherapies
		6.3.6 Stem cells
		6.3.7 Phage therapies
		6.3.8 Microbiome-based therapeutics
	6.4 Future of biological therapeutics
	6.5 Case study—bio-therapeutic modalities in COVID-19 treatment
	6.6 Conclusion
	References
7 The journey of noncoding RNA from bench to clinic
	7.1 Introduction
		7.1.1 Noncoding RNAs and their classification
		7.1.2 In silico ncRNA prediction tools
		7.1.3 Screening and characterization of ncRNAs
		7.1.4 Small noncoding RNAs (miRNAs and siRNAs)
			7.1.4.1 Biogenesis of miRNAs and siRNAs
			7.1.4.2 Working mechanism of miRNAs and siRNAs
			7.1.4.3 Expression profile of miRNAs in disease pathology
			7.1.4.4 miRNAs and siRNAs—from bench to clinic
				7.1.4.4.1 Miravirsen for the treatment of Hepatitis C
				7.1.4.4.2 MRX34 as cancer therapeutic
				7.1.4.4.3 OsteomiR and ThrombomiR as diagnostic markers
				7.1.4.4.4 miRView Meso and miRView mets as diagnostic markers
				7.1.4.4.5 Patisiran (or ONPATTRO) for the treatment of hereditary TTR-mediated amyloidosis
				7.1.4.4.6 Givosiran (or GIVLAARI) for the treatment of acute hepatic porphyria
				7.1.4.4.7 QPI1007 for the treatment of nonarteritic anterior ischemic optic neuropathy
		7.1.5 Long noncoding RNAs
			7.1.5.1 Biogenesis of lncRNAs
			7.1.5.2 Working mechanisms of lncRNAs
			7.1.5.3 Expression profile of lncRNAs in disease pathology
			7.1.5.4 lncRNAs—from bench to clinic
				7.1.5.4.1 Inodiftagene vixteplasmid therapy (BC-819) for bladder cancer
				7.1.5.4.2 OPK88001 (CUR-1916) for Dravet syndrome
				7.1.5.4.3 PCA3 as a diagnostic marker for prostate cancer
	7.2 Patent landscape of noncoding RNA
	7.3 Bottlenecks in the use of noncoding RNAs as biomarkers/therapeutics
	7.4 Conclusions and future perspectives
	References
8 Peptide-based hydrogels for biomedical applications
	8.1 Introduction
	8.2 Peptide-based hydrogelators
		8.2.1 β-Sheet forming peptides
			8.2.1.1 Peptides end-capped with aromatic moieties
			8.2.1.2 Amyloid peptides
			8.2.1.3 Designed peptides without aromatic end-caps
			8.2.1.4 β-Turn-containing peptides
			8.2.1.5 Peptide amphiphiles and amphiphilic peptides
				8.2.1.5.1 Peptide amphiphiles
				8.2.1.5.2 Amphiphilic peptides
				8.2.1.5.3 PEGylated peptides
		8.2.2 α-Helical peptides
	8.3 Biomedical applications
		8.3.1 Therapeutic delivery
			8.3.1.1 Small molecules
			8.3.1.2 Vaccine adjuvant and macromolecule delivery
			8.3.1.3 Therapeutic secretions from encapsulated cells
		8.3.2 Scaffold for regenerative medicine
		8.3.3 Wound dressing
		8.3.4 Antimicrobial agents
	8.4 Conclusion, limitations, and future directions
	References
9 Bispecific antibodies: A promising entrant in cancer immunotherapy
	9.1 Introduction
	9.2 Evolution of bispecific antibodies
		9.2.1 Different formats of bispecific antibodies
		9.2.2 Mechanism of action
			9.2.2.1 Bispecific T-cell Engager
			9.2.2.2 Immune payloads
			9.2.2.3 Immune checkpoint blockade inhibitors
	9.3 Production of bispecific antibodies
		9.3.1 Hybrid hybridoma (quadroma technology)
		9.3.2 Knob-into-hole approach
		9.3.3 CrossMab approach
		9.3.4 Chemical conjugation
			9.3.4.1 Case study: blinatumomab/MT103
			9.3.4.2 Molecular design
			9.3.4.3 Manufacturing
			9.3.4.4 Characterization
			9.3.4.5 Purification of blinatumomab
	9.4 Biomarkers in immunotherapy at a glance
		9.4.1 Biomarkers for breast cancer
		9.4.2 Biomarkers for prostate cancer
		9.4.3 Biomarkers for checkpoint blockade immunotherapy
	9.5 Engineering of therapeutic protein
		9.5.1 Binding affinity enhancement
		9.5.2 Immunogenicity minimization
		9.5.3 Stability enhancement and half-life extension
	9.6 Market analysis: past, present and future
	9.7 Future challenges and opportunities
	9.8 Conclusion
	References
10 Emerging therapeutic modalities against malaria
	10.1 Introduction
	10.2 Heme-detoxification drugs
	10.3 Drugs targeting DNA or protein synthesis
	10.4 Drugs targeting membrane transporters
	10.5 Natural products
	10.6 Protein-based malaria vaccines
	10.7 Nucleic acid vaccines for the new era
		10.7.1 DNA-based vaccines
		10.7.2 RNA-based vaccines
	10.8 Biological therapeutics
	10.9 Conclusion
	References
11 Translational bioinformatics: An introduction
	11.1 Introduction
	11.2 The era of omics and big data: data mining and biomedical data integration
		11.2.1 Data acquisition and warehousing
		11.2.2 Data integration
		11.2.3 Data mining
	11.3 TBI in biomarker discovery
	11.4 Computer-aided drug discovery
	11.5 Artificial intelligence-based approach in TBI
		11.5.1 Complex disease analysis using ML
		11.5.2 Illustrious examples of ML in translational research
	11.6 The implication of TBI in precision medicine
		11.6.1 Data-driven precision medicine initiatives
		11.6.2 Future prospects of transitional bioinformatics in personalized medicine
	11.7 Conclusion
	References
12 Pharmacodynamic biomarker for Hepatocellular carcinoma C: Model-based evaluation for pharmacokinetic–pharmacodynamic res...
	12.1 Hepatocellular carcinoma
		12.1.1 Possible risk factors of hepatocellular carcinoma
		12.1.2 Stages of hepatocellular carcinoma
			12.1.2.1 NAFLD
			12.1.2.2 Nonalcoholic steatohepatitis/fibrosis
			12.1.2.3 Cirrhosis
		12.1.3 Challenges in therapeutic and medicinal drug treatment for hepatocellular carcinoma
	12.2 Pharmacokinetic and pharmacodynamic profiles (PK–PD)
		12.2.1 Pharmacokinetic profile (PK)
		12.2.2 Pharmacodynamics (PD)
	12.3 Pharmacokinetic and pharmacodynamic models
		12.3.1 Compartmental models
		12.3.2 Direct pharmacokinetic and pharmacodynamic models
		12.3.3 Indirect pharmacokinetic and pharmacodynamic models
	12.4 Advantages of pharmacokinetic and pharmacodynamic modeling
	12.5 Development of pharmacodynamic (PD) biomarker in hepatocellular carcinoma
		12.5.1 Proteomic approach for identification of pharmacodynamic biomarkers
		12.5.2 Therapeutic outcome using PD biomarker
	12.6 Pharmacokinetic and pharmacodynamic drug responses
	12.7 Conclusions
	References
13 System biology and synthetic biology
	13.1 Introduction
	13.2 System biology
		13.2.1 Central principles of scientific approaches to biology systems
		13.2.2 Fields in therapeutic applications system biology
			13.2.2.1 Systems medicine
			13.2.2.2 Systems pharmacology
	13.3 Synthetic biology
		13.3.1 Role of synthetic biology in understanding disease mechanisms
		13.3.2 Synthetic biology in drug discovery, development, and delivery
		13.3.3 Role of synthetic biology in personalized medicine
		13.3.4 Regulation and ethical considerations of synthetic biology
	13.4 Conclusion
	References
14 Translational research in drug discovery: Tiny steps before the giant leap
	14.1 Introduction
	14.2 Tools involved in translation drug discovery
	14.3 Recent successful advances in translation drug discovery
		14.3.1 Cancer
		14.3.2 Diabetes
		14.3.3 Acquired immunodeficiency syndrome
		14.3.4 Autoimmune disorders
		14.3.5 Neurological disorder
		14.3.6 Cardiovascular disease (CVD)
	14.4 Opportunities in translation drug discovery
	14.5 Challenges in translation drug discovery
	14.6 Approaches to boost translational drug discovery
	14.7 Conclusion
	14.8 Future perspective
	References
15 FLAGSHIP: A novel drug discovery platform originating from the “dark matter of the genome”
	15.1 Introduction
	15.2 Designing novel therapeutic peptides from dark matter of the genome
		15.2.1 Antimicrobial peptides
		15.2.2 Antimalarial peptides
		15.2.3 Anti-Alzheimer peptides
		15.2.4 Drawbacks of peptides therapeutics
		15.2.5 Future applications
	15.3 Pseudogenes: a potential biotherapeutic target
		15.3.1 Pseudogene-directed gene regulation
	References
16 Role of shared research facilities/core facilities in translational research
	16.1 Introduction: socioeconomic impact of translational research
		16.1.1 Challenges faced in translational research
	16.2 Core facility: shared research–shared cost
		16.2.1 Core facilities of prime significance in translational research
	16.3 Research and development supporting mechanism: environmental scan (the United States and Canada)
		16.3.1 Supporting translational research through core facilities in the United States—from past to present
		16.3.2 Canada’s ecosystem of translational research and funding mechanism
		16.3.3 Highlights around the world
			16.3.3.1 Funding mechanism for research and innovation
			16.3.3.2 Awareness of networking and engagement
		16.3.4 Glimpses of global research and development expenditure
	16.4 Efficiencies and lean practices in research management
		16.4.1 Core facilities business model
		16.4.2 Governance model for core facility
		16.4.3 Core facilities and research outcome
	16.5 Final notes: learnings for future
		16.5.1 Integration of core facilities within the institutional strategic plan
		16.5.2 Comprehensive availability of infrastructure inventory
		16.5.3 Impact measurement
	References
17 A new TOPSIS-based approach to evaluate the economic indicators in the healthcare system and the impact of biotechnology
	17.1 Introduction
	17.2 Technique for order of preference by similarity to ideal solution approach
		17.2.1 Metric space
		17.2.2 New technique for order of preference by similarity to ideal solution approach
	17.3 Methodology
		17.3.1 Selection of criteria
		17.3.2 Selection of indicators
		17.3.3 Application of new technique for order of preference by similarity to ideal solution approach
		17.3.4 Analysis of sensitivity
	17.4 Result and discussion
		17.4.1 Result from technique for order of preference by similarity to ideal solution 1
		17.4.2 Result from technique for order of preference by similarity to ideal solution
		17.4.3 Result from sensitivity analysis
	17.5 Conclusion
	References
Glossary
Index
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