Translational biotechnology : a journey from laboratory to clinics / edited by Yasha Hasija.

Format:

Book

Contributor:

Hasija, Yasha, editor.

Language:

English

Subjects (All):

Biotechnology.

Biomedical engineering.

Genre:

Electronic books.

Physical Description:

1 online resource

Place of Publication:

London, United Kingdom ; San Diego, CA, United States : Academic Press, an imprint of Elsevier, [2021]

System Details:

text file

Contents:

1 Introduction to translational biotechnology

1 Transiational biotechnology: A transition from basic biology to evidence-based research p. 3 / Debleena Guin and Sarita Thakran and Pooja Singh and S. Ramachandran and Yasha Hasija and Ritushree Kukreti

1.1.1 Background and emergence of the field p. 4

1.2 The phases of translational research p. 5

1.3 Challenges to solutions p. 6

1.4.1 Drug development p. 12

1.4.2 Nanomedicine p. 16

1.4.3 Gene therapy p. 17

1.4.4 Precision medicine and biomarker development p. 19

1.4.5 Microbial engineering for bio-therapeutics p. 19

1.4.6 Application of big data and transiational bioinformatics p. 19

1.5 Conclusion and future direcrions p. 21

Conflict of interest p. 22

2 Biotherapeutics

2 Biotechnology-based therapeutics p. 27 / Ravichandran Vijaya Abinaya and Pragasam Viswanathan

2.2 Human gene therapy p. 29

2.2.1 Somatic cell gene therapy p. 30

2.2.2 Germline gene therapy p. 30

2.2.3 Gene transfer system p. 30

2.2.4 Gene-editing technology p. 33

2.2.5 Ethical issue p. 34

2.3 Stem cell therapy p. 34

2.3.1 Sources of stem cells p. 35

2.3.2 Benefits of stem cell therapy in various disorder p. 36

2.3.3 Challenges and problems p. 37

2.4 Nanomedicine p. 37

2.4.1 Nano therapeutic applications p. 37

2.4.2 Tissue engineering p. 39

2.4.3 Nanoimaging p. 40

2.5 Drug designing and delivery p. 40

2.5.1 Rational drug design p. 41

2.5.2 Computer-aided drug design p. 41

2.5.3 Drug delivery p. 44

2.6 Recombinant therapeutic proteins and vaccines p. 44

2.6.1 Recombinant protein p. 44

2.6.2 Expression system p. 44

2.6.3 Recombinant protein as a treatment p. 46

2.6.4 Recombinant vaccine p. 47

2.7 Conclusion and future applications p. 48

Conflicts of interest p. 48

Author's contribution p. 48

3 Advanced biotechnology-based therapeutics p. 53 / Srividhya Ravichandran and Gaurav Verma

3.2 Technologies that lead to the discovery of therapy p. 55

3.2.1 Genome editing technologies p. 55

3.2.2 Role of nanomedicine in drug discovery approaches p. 56

3.2.3 Antibody-drug conjugates p. 58

3.3 Molecular diagnostics p. 60

3.3.1 Translational bioinformatics p. 62

3.3.2 Organoids-tools for disease models p. 63

3.4 Cell-based therapy p. 65

3.5 Nanotechnology and its uses in biomedicine p. 67

3.6 Genome-scale metabolic modeling p. 68

3.7 Critical processes in the flow from basic science to practical application in the clinic via clinical trials and translational studies p. 69

3.8 Major pitfalls in translational research p. 70

3.9 Advancement in devices, biologies, and vaccines as an introduction to biotechnology products that are being used in therapy p. 72

3 Pathway and target discovery

4 Human in vitro disease models to aid pathway and target discovery for neurological disorders p. 81 / Bhavana Muralidharan

4.2 Generation of human disease models using iPSCs/patient fibroblasts p. 83

4.2.1 Directed differentiation into neural cells p. 84

4.2.2 Direct differentiation into neurons/glia p. 86

4.2.3 Direct lineage reprogramming/transdifferentiation into neurons p. 88

4.3 Modeling neurodevelopmental disorders p. 88

3.1 Rett syndrome p. 88

4.3.2 Fragile X syndrome p. 89

4.3.3 Autism spectrum disorders p. 89

4.3.4 Schizophrenia p. 90

4.4 Modeling neurodegenerative diseases p. 91

4.4.1 Amyotrophic lateral sclerosis p. 91

4.4.2 Alzheimer's disease p. 92

4.4.3 Parkinson's disease p. 93

4.5 Cerebral organoids and the future of human in vitro disease modeling p. 93

4.6 From bench to bedside-identification of pathways and drug targets for designing therapies p. 95

4.7 Future perspectives p. 97

Keyword definitions p. 97

5 Importance of targeted therapies in acute myeloid leukemia p. 107 / Ajit Kumar Rai and Neeraj Kumar Satija

5.1.1 Conventional therapy for acute myeloid leukemia p. 108

5.1.2 Significance of target discovery p. 108

5.2 Approaches in target discovery p. 109

5.2.1 Systems approach p. 110

5.2.2 Molecular approach p. 111

5.3 Acute myeloid leukemia-targeted therapies in clinics p. 117

5.3.1 BCL-2 inhibitors p. 117

5.3.2 Isocitrate dehydrogenase inhibitors p. 117

5.3.3 PML-RARα targeted therapy p. 118

5.3.4 Targeting FLT3-mutated acute myeloid leukemia: from bench to bedside (a case study) p. 119

5.4 Hurdles and emerging targeted therapies p. 120

4 Novel therapeutic modalities

6 Biological therapeutic modalities p. 137 / Munish Chhabra

6.1 Introduction to biological therapeutic modalities p. 137

6.2 History of classical modalities p. 139

6.3 New modalities p. 140

6.3.1 Small molecules p. 140

6.3.2 Nucleic acid therapeutics p. 142

6.3.3 Therapeutic proteins p. 143

6.3.4 Antibodies p. 145

6.3.5 Cell-based immunotherapies p. 148

6.3.6 Stem cells p. 150

6.3.7 Phage therapies p. 151

6.3.8 Microbiome-based therapeutics p. 153

6.4 Future of biological therapeutics p. 154

6.5 Case study-bio-therapeutic modalities in COVID-19 treatment p. 155

7 The journey of noncoding RNA from bench to clinic p. 165 / Ravindresh Chhabra

7.1.1 Noncoding RNAs and their classification p. 165

7.1.2 In silico ncRNA prediction tools p. 166

7.1.3 Screening and characterization of ncRNAs p. 167

7.1.4 Small noncoding RNAs (miRNAs and siRNAs) p. 167

7.1.5 Long noncoding RNAs p. 181

7.2 Patent landscape of noncoding RNA p. 187

7.3 Bottlenecks in the use of noncoding RNAs as biomarkers/therapeutics p. 189

7.4 Conclusions and future perspectives p. 191

8 Peptide-based bydrogels for biomedical applications p. 203 / Debika Datta and Nitin Chaudhary

8.2 Peptide-based hydrogelators p. 204

8.2.1 ß-Sheet forming peptides p. 204

8.2.2 α-Helical peptides p. 214

8.3 Biomedical applications p. 215

8.3.1 Therapeutic delivery p. 216

8.3.2 Scaffold for regenerative medicine p. 218

8.3.3 Wound dressing p. 219

8.3.4 Antimicrobial agents p. 220

8.4 Conclusion, limitations, and future directions p. 221

9 Bispecific antibodies: A promising entrant in cancer immunotherapy p. 233 / Samvedna Saini and Yatender Kumar

9.2 Evolution of bispecific antibodies p. 234

9.2.1 Different formats of bispecific antibodies p. 236

9.2.2 Mechanism of action p. 238

9.3 Production of bispecific antibodies p. 243

9.3.1 Hybrid hybridoma (quadroma technology) p. 243

9.3.2 Knob-into-hole approach p. 243

9.3.3 CrossMab approach p. 244

9.3.4 Chemical conjugation p. 244

9.4 Biomarkers in immunotherapy at a glance p. 246

9.4.1 Biomarkers for breast cancer p. 246

9.4.2 Biomarkers for prostate cancer p. 247

9.4.3 Biomarkers for checkpoint blockade immunotherapy p. 248

9.5 Engineering of therapeutic protein p. 248

9.5.1 Binding affinity enhancement p. 249

9.5.2 Immunogenicity minimization p. 249

9.5.3 Stability enhancement and half-life extension p. 250

9.6 Market analysis: past, present and future p. 250

9.7 Future challenges and opportunities p. 254

10 Emerging therapeutic modalities against malaria p. 267 / Suresh Kumar Chalapareddy and Andaleeb Sajid and Mritunjay Saxena and Kriti Arora and Rajan Guha and Gunjan Arora

10.2 Heme-detoxification drugs p. 268

10.3 Drugs targeting DNA or protein synthesis p. 270

10.4 Drugs targeting membrane transporters p. 271

10.5 Natural products p. 272

10.6 Protein-based malaria vaccines p. 273

10.7 Nucleic acid vaccines for the new era p. 273

10.7.1 DNA-based vaccines p. 274

10.7.2 RNA-based vaccines p. 277

10.8 Biological therapeutics p. 277

5 Healthcare bioinformatics

11 Translational bioinformatics: An introduction p. 289 / Richa Nayak and Yasha Hasija

11.2 The era of omics and big data: data mining and biomedical data integration p. 292

11.2.1 Data acquisition and warehousing p. 292

11.2.2 Data integration p. 293

11.2.3 Data mining p. 294

11.3 TBI in biomarker discovery p. 297

11.4 Computer-aided drug discovery p. 299

11.5 Artificial intelligence-based approach in TBI p. 300

11.5.1 Complex disease analysis using ML p. 301

11.5.2 Illustrious examples of ML in transiational research p. 302

11.6 The implication of TBI in precision medicine p. 304

11.6.1 Data-driven precision medicine initiatives p. 305

11.6.2 Future prospects of transitional bioinformatics in personalized medicine p. 305

12 Pharmacodynamic biomarker for Hepatocellular carcinoma C: Model-based evaluation for pharmacokinetic-pharmacodynamic responses of drug p. 311 / Nitu Dogra and Savita Mishra and Ruchi Jakbmola Mani and Vidhu Aeri and Deepshikha Pande Katare

12.1 Hepatocellular carcinoma p. 312

12.1.1 Possible risk factors of hepatocellular carcinoma p. 312

12.1.2 Stages of hepatocellular carcinoma p. 314

12.1.3 Challenges in therapeutic and medicinal drug treatment for hepatocellular carcinoma p. 316

12.2 Pharmacokinetic and pharmacodynamic profiles (PK-PD) p. 316

12.2.1 Pharmacokinetic profile (PK) p. 316

12.2.2 Pharmacodynamics (PD) p. 316

12.3 Pharmacokinetic and pharmacodynamic models p. 317

12.3.1 Compartmental models p. 317

12.3.2 Direct pharmacokinetic and pharmacodynamic models p. 318

12.3.3 Indirect pharmacokinetic and pharmacodynamic models p. 319

12.4 Advantages of pharmacokinetic and pharmacodynamic modeling p. 319

12.5 Development of pharmacodynamic (PD) biomarker in hepatocellular carcinoma p. 320

12.5.1 Proteomic approach for identification of pharmacodynamic biomarkers p. 321

12.5.2 Therapeutic outcome using PD biomarker p. 322

12.6 Pharmacokinetic and pharmacodynamic drug responses p. 323

6 Biological systems engineering

13 System biology and synthetic biology p. 329 / Richa Nayak and Rajkumar Chakraborty and Yasha Hasija

13.2 System biology p. 331

13.2.1 Central principles of scientific approaches to biology systems p. 332

13.2.2 Fields in therapeutic applications system biology p. 333

13.3 Synthetic biology p. 336

13.3.1 Role of synthetic biology in understanding disease mechanisms p. 337

13.3.2 Synthetic biology in drug discovery, development, and delivery p. 339

13.3.3 Role of synthetic biology in personalized medicine p. 340

13.3.4 Regulation and ethical considerations of synthetic biology p. 340

7 Drug discovery and personalized medicine

14 Transiational research in drug discovery: Tiny steps before the giant leap p. 347 / Sindhuri Upadrasta and Vikas Yadav*

14.2 Tools involved in translation drug discovery p. 349

14.3 Recent successful advances in translation drug discovery p. 351

14.3.1 Cancer p. 352

14.3.2 Diabetes p. 355

14.3.3 Acquired immunodeficiency syndrome p. 355

14.3.4 Autoimmune disorders p. 356

14.3.5 Neurological disorder p. 357

14.3.6 Cardiovascular disease (CVD) p. 357

14.4 Opportunities in translation drug discovery p. 358

14.5 Challenges in translation drug discovery p. 359

14.6 Approaches to boost transiational drug discovery p. 360

14.8 Future perspective p. 364

15 FLAGSHIP: A novel drug discovery platform originating from the "dark matter of the genome" p. 371 / Neeraj Verma and Siddharth Manvati and Pawan Dhar

15.2 Designing novel therapeutic peptides from dark matter of the genome p. 373

15.2.1 Antimicrobial peptides p. 373

15.2.2 Antimalarial peptides p. 374

15.2.3 Anti-Alzheimer peptides p. 374

15.2.4 Drawbacks of peptides therapeutics p. 375

15.2.5 Future applications p. 375

15.3 Pseudogenes: a potential biotherapeutic target p. 376

15.3.1 Pseudogene-directed gene regulation p. 377

8 Socio-economic impact of transiational biotechnology

16 Role of shared research facilities/core facilities in transiational research p. 383 / Vidhu Sharma

16.1 Introduction: socioeconomic impact of translational research p. 384

16.1.1 Challenges faced in translational research p. 385

16.2 Core facility: shared research-shared cost p. 386

16.2.1 Core facilities of prime significance in translational research p. 388

16.3 Research and development supporting mechanism: environmental scan (the United States and Canada) p. 389

16.3.1 Supporting translational research through core facilities in the United States-from past to present p. 390

16.3.2 Canada's ecosystem of translational research and funding mechanism p. 392

16.3.3 Highlights around the world p. 394

16.3.4 Glimpses of global research and development expenditure p. 396

16.4 Efficiencies and lean practices in research management p. 399

16.4.1 Core facilities business model p. 399

16.4.2 Governance model for core facility p. 402

16.4.3 Core facilities and research outcome p. 402

16.5 Final notes: learnings for future p. 403

16.5.1 Integration of core facilities within the institutional strategic plan p. 403

16.5.2 Comprehensive availability of infrastructure inventory p. 403

16.5.3 Impact measurement p. 404

17 A new TOPSIS-based approach to evaluate the economic indicators in the healthcare system and the impact of biotechnology p. 407 / Priyanka Majumder and Apu Kumar Saha

17.2 Technique for order of preference by similarity to ideal solution approach p. 410

17.2.1 Metric space p. 410

17.2.2 New technique for order of preference by similarity to ideal solution approach p. 411

17.3.1 Selection of criteria p. 413

17.3.2 Selection of indicators p. 414

17.3.3 Application of new technique for order of preference by similarity to ideal solution approach p. 414

17.3.4 Analysis of sensitivity p. 416

17.4 Result and discussion p. 416

17.4.1 Result from technique for order of preference by similarity to ideal solution 1 p. 416

17.4.2 Result from technique for order of preference by similarity to ideal solution p. 417

17.4.3 Result from sensitivity analysis p. 418.

Notes:

Includes bibliographical references and index.

Online resource; title from PDF title page (ScienceDirect, viewed May 13, 2021).

ISBN:

9780128219737

0128219734

OCLC:

1232032238

Access Restriction:

Restricted for use by site license.