Translational Biotechnology: A Journey from Laboratory to Clinics

427 Pages·2021·18.757 MB·English

by  Yasha Hasija (editor)

TRANSLATIONAL BIOTECHNOLOGY
TRANSLATIONAL
BIOTECHNOLOGY
A Journey from Laboratory to Clinics
Edited by
Y H
ASHA ASIJA
DepartmentofBiotechnology,DelhiTechnologicalUniversity,Delhi,India
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Contents
List of contributors xi 2
Preface xiii
Biotherapeutics
2 Biotechnology-based therapeutics 27
1
RavichandranVijayaAbinayaandPragasamViswanathan
Introduction to translational
2.1 Introduction 28
biotechnology
2.2 Humangenetherapy 29
2.2.1 Somaticcellgenetherapy 30
1 Translational biotechnology:
2.2.2 Germlinegenetherapy 30
A transition from basic
2.2.3 Genetransfersystem 30
biology to evidence-based 2.2.4 Gene-editingtechnology 33
research 3 2.2.5 Ethicalissue 34
2.3 Stemcelltherapy 34
DebleenaGuin,SaritaThakran,PoojaSingh,S.Ramachandran,
2.3.1 Sourcesofstemcells 35
YashaHasijaandRitushreeKukreti
2.3.2 Benefitsofstemcelltherapyinvarious
1.1 Introduction 4 disorder 36
1.1.1 Backgroundandemergenceofthe 2.3.3 Challengesandproblems 37
field 4 2.4 Nanomedicine 37
1.2 Thephasesoftranslationalresearch 5 2.4.1 Nanotherapeuticapplications 37
1.3 Challengestosolutions 6 2.4.2 Tissueengineering 39
1.4 Applications 9 2.4.3 Nanoimaging 40
2.5 Drugdesigninganddelivery 40
1.4.1 Drugdevelopment 12
2.5.1 Rationaldrugdesign 41
1.4.2 Nanomedicine 16
2.5.2 Computer-aideddrugdesign 41
1.4.3 Genetherapy 17
2.5.3 Drugdelivery 44
1.4.4 Precisionmedicineandbiomarker
2.6 Recombinanttherapeuticproteinsand
development 19
vaccines 44
1.4.5 Microbialengineeringfor
2.6.1 Recombinantprotein 44
bio-therapeutics 19
2.6.2 Expressionsystem 44
1.4.6 Applicationofbigdataand 2.6.3 Recombinantproteinasatreatment 46
translationalbioinformatics 19
2.6.4 Recombinantvaccine 47
1.5 Conclusionandfuturedirections 21 2.7 Conclusionandfutureapplications 48
1.6 Highlights 21 Acknowledgments 48
Acknowledgment 22 Conflictsofinterest 48
Conflictofinterest 22 Author’scontribution 48
References 22 References 49
v
vi Contents
3 Advanced biotechnology-based 4.3.3 Autismspectrumdisorders 89
therapeutics 53 4.3.4 Schizophrenia 90
4.4 Modelingneurodegenerativediseases 91
SrividhyaRavichandranandGauravVerma
4.4.1 Amyotrophiclateralsclerosis 91
3.1 Introduction 54 4.4.2 Alzheimer’sdisease 92
3.2 Technologiesthatleadtothediscoveryof 4.4.3 Parkinson’sdisease 93
therapy 55 4.5 Cerebralorganoidsandthefutureofhuman
3.2.1 Genomeeditingtechnologies 55 invitrodiseasemodeling 93
3.2.2 Roleofnanomedicineindrug 4.6 Frombenchtobedside—identificationof
discoveryapproaches 56 pathwaysanddrugtargetsfordesigning
3.2.3 Antibody(cid:1)drugconjugates 58 therapies 95
3.3 Moleculardiagnostics 60 4.7 Futureperspectives 97
3.3.1 Translationalbioinformatics 62 Keyworddefinitions 97
3.3.2 Organoids—toolsfordisease Acknowledgments 98
models 63 References 98
3.4 Cell-basedtherapy 65
5 Importance of targeted therapies in
3.5 Nanotechnologyanditsusesin
biomedicine 67 acute myeloid leukemia 107
3.6 Genome-scalemetabolicmodeling 68
AjitKumarRaiandNeerajKumarSatija
3.7 Criticalprocessesintheflowfrombasic
sciencetopracticalapplicationintheclinic 5.1 Introduction 107
viaclinicaltrialsandtranslationalstudies 69 5.1.1 Conventionaltherapyforacutemyeloid
3.8 Majorpitfallsintranslationalresearch 70 leukemia 108
3.9 Advancementindevices,biologics,andvaccines 5.1.2 Significanceoftargetdiscovery 108
asanintroductiontobiotechnologyproducts 5.2 Approachesintargetdiscovery 109
thatarebeingusedintherapy 72 5.2.1 Systemsapproach 110
3.10 Conclusionandsummary 74 5.2.2 Molecularapproach 111
References 74 5.3 Acutemyeloidleukemia(cid:1)targetedtherapiesin
clinics 117
3
5.3.1 BCL-2inhibitors 117
5.3.2 Isocitratedehydrogenaseinhibitors 117
Pathway and target discovery
5.3.3 PML-RARαtargetedtherapy 118
5.3.4 TargetingFLT3-mutatedacutemyeloid
4 Human in vitro disease models to aid
leukemia:frombenchtobedside(acase
pathway and target discovery for study) 119
neurological disorders 81 5.4 Hurdlesandemergingtargetedtherapies 120
5.5 Conclusion 125
BhavanaMuralidharan
Acknowledgments 125
4.1 Introduction 82 References 126
4.2 Generationofhumandiseasemodelsusing
4
iPSCs/patientfibroblasts 83
4.2.1 Directeddifferentiationintoneural
Novel therapeutic modalities
cells 84
4.2.2 Directdifferentiationintoneurons/
6 Biological therapeutic modalities 137
glia 86
4.2.3 Directlineagereprogramming/
MunishChhabra
transdifferentiationintoneurons 88
4.3 Modelingneurodevelopmentaldisorders 88 6.1 Introductiontobiologicaltherapeutic
3.1 Rettsyndrome 88 modalities 137
4.3.2 FragileXsyndrome 89 6.2 Historyofclassicalmodalities 139
Contents vii
6.3 Newmodalities 140 8.4 Conclusion,limitations,andfuture
6.3.1 Smallmolecules 140 directions 221
6.3.2 Nucleicacidtherapeutics 142 References 223
6.3.3 Therapeuticproteins 143
6.3.4 Antibodies 145 9 Bispecific antibodies: A promising
6.3.5 Cell-basedimmunotherapies 148 entrant in cancer immunotherapy 233
6.3.6 Stemcells 150
SamvednaSainiandYatenderKumar
6.3.7 Phagetherapies 151
6.3.8 Microbiome-basedtherapeutics 153 9.1 Introduction 234
6.4 Futureofbiologicaltherapeutics 154
9.2 Evolutionofbispecificantibodies 234
6.5 Casestudy—bio-therapeuticmodalitiesin
9.2.1 Differentformatsofbispecific
COVID-19treatment 155 antibodies 236
6.6 Conclusion 156
9.2.2 Mechanismofaction 238
References 160
9.3 Productionofbispecificantibodies 243
9.3.1 Hybridhybridoma(quadroma
7 The journey of noncoding RNA from technology) 243
bench to clinic 165 9.3.2 Knob-into-holeapproach 243
9.3.3 CrossMabapproach 244
RavindreshChhabra 9.3.4 Chemicalconjugation 244
9.4 Biomarkersinimmunotherapyataglance 246
7.1 Introduction 165
9.4.1 Biomarkersforbreastcancer 246
7.1.1 NoncodingRNAsandtheir
9.4.2 Biomarkersforprostatecancer 247
classification 165
9.4.3 Biomarkersforcheckpointblockade
7.1.2 InsiliconcRNApredictiontools 166
immunotherapy 248
7.1.3 Screeningandcharacterizationof
9.5 Engineeringoftherapeuticprotein 248
ncRNAs 167
9.5.1 Bindingaffinityenhancement 249
7.1.4 SmallnoncodingRNAs
9.5.2 Immunogenicityminimization 249
(miRNAsandsiRNAs) 167
9.5.3 Stabilityenhancementand
7.1.5 LongnoncodingRNAs 181
half-lifeextension 250
7.2 PatentlandscapeofnoncodingRNA 187
9.6 Marketanalysis:past,presentandfuture 250
7.3 Bottlenecksintheuseofnoncoding
9.7 Futurechallengesandopportunities 254
RNAsasbiomarkers/therapeutics 189
9.8 Conclusion 255
7.4 Conclusionsandfutureperspectives 191
References 255
References 192
8 Peptide-based hydrogels for biomedical 10 Emerging therapeutic modalities against
applications 203 malaria 267
DebikaDattaandNitinChaudhary SureshKumarChalapareddy,AndaleebSajid,MritunjaySaxena,
KritiArora,RajanGuhaandGunjanArora
8.1 Introduction 203
8.2 Peptide-basedhydrogelators 204 10.1 Introduction 267
8.2.1 β-Sheetformingpeptides 204 10.2 Heme-detoxificationdrugs 268
8.2.2 α-Helicalpeptides 214 10.3 DrugstargetingDNAorprotein
8.3 Biomedicalapplications 215 synthesis 270
8.3.1 Therapeuticdelivery 216 10.4 Drugstargetingmembranetransporters 271
8.3.2 Scaffoldforregenerativemedicine 218 10.5 Naturalproducts 272
8.3.3 Wounddressing 219 10.6 Protein-basedmalariavaccines 273
8.3.4 Antimicrobialagents 220 10.7 Nucleicacidvaccinesforthenewera 273
10.7.1 DNA-basedvaccines 274
viii Contents
10.7.2 RNA-basedvaccines 277 12.1.2 Stagesofhepatocellular
10.8 Biologicaltherapeutics 277 carcinoma 314
10.9 Conclusion 278 12.1.3 Challengesintherapeuticand
References 279 medicinaldrugtreatmentfor
hepatocellularcarcinoma 316
5 12.2 Pharmacokineticandpharmacodynamic
profiles(PK(cid:1)PD) 316
Healthcare bioinformatics 12.2.1 Pharmacokineticprofile(PK) 316
12.2.2 Pharmacodynamics(PD) 316
11 Translational bioinformatics: 12.3 Pharmacokineticandpharmacodynamic
An introduction 289 models 317
12.3.1 Compartmentalmodels 317
RichaNayakandYashaHasija
12.3.2 Directpharmacokineticand
11.1 Introduction 289 pharmacodynamicmodels 318
11.2 Theeraofomicsandbigdata:datamining 12.3.3 Indirectpharmacokineticand
andbiomedicaldataintegration 292 pharmacodynamicmodels 319
11.2.1 Dataacquisitionand 12.4 Advantagesofpharmacokineticand
warehousing 292 pharmacodynamicmodeling 319
11.2.2 Dataintegration 293 12.5 Developmentofpharmacodynamic(PD)
11.2.3 Datamining 294 biomarkerinhepatocellularcarcinoma 320
11.3 TBIinbiomarkerdiscovery 297 12.5.1 Proteomicapproachforidentification
11.4 Computer-aideddrugdiscovery 299 ofpharmacodynamicbiomarkers 321
11.5 Artificialintelligence-basedapproachin 12.5.2 TherapeuticoutcomeusingPD
TBI 300 biomarker 322
11.5.1 Complexdiseaseanalysisusing 12.6 Pharmacokineticandpharmacodynamicdrug
ML 301 responses 323
11.5.2 IllustriousexamplesofMLin 12.7 Conclusions 323
translationalresearch 302 References 324
11.6 TheimplicationofTBIinprecision
medicine 304 6
11.6.1 Data-drivenprecisionmedicine
initiatives 305 Biological systems engineering
11.6.2 Futureprospectsoftransitional
bioinformaticsinpersonalized 13 System biology and synthetic
medicine 305 biology 329
11.7 Conclusion 306
RichaNayak,RajkumarChakrabortyandYashaHasija
References 307
13.1 Introduction 329
12 Pharmacodynamic biomarker for
13.2 Systembiology 331
Hepatocellular carcinoma C:
13.2.1 Centralprinciplesofscientific
Model-based evaluation for approachestobiologysystems 332
pharmacokinetic(cid:1)pharmacodynamic 13.2.2 Fieldsintherapeuticapplications
responses of drug 311 systembiology 333
13.3 Syntheticbiology 336
NituDogra,SavitaMishra,RuchiJakhmolaMani,VidhuAeriand
13.3.1 Roleofsyntheticbiologyin
DeepshikhaPandeKatare
understandingdisease
12.1 Hepatocellularcarcinoma 312 mechanisms 337
12.1.1 Possibleriskfactorsofhepatocellular 13.3.2 Syntheticbiologyindrugdiscovery,
carcinoma 312 development,anddelivery 339
Contents ix
13.3.3 Roleofsyntheticbiologyin 15.2.2 Antimalarialpeptides 374
personalizedmedicine 340 15.2.3 Anti-Alzheimerpeptides 374
13.3.4 Regulationandethicalconsiderations 15.2.4 Drawbacksofpeptides
ofsyntheticbiology 340 therapeutics 375
13.4 Conclusion 341 15.2.5 Futureapplications 375
References 342 15.3 Pseudogenes:apotentialbiotherapeutic
target 376
7 15.3.1 Pseudogene-directedgene
regulation 377
Drug discovery and personalized References 377
medicine
8
14 Translational research in
drug discovery: Tiny steps before
Socio-economic impact of
the giant leap 347
translational biotechnology
SindhuriUpadrastaandVikasYadav*
16 Role of shared research facilities/core
14.1 Introduction 348
facilities in translational research 383
14.2 Toolsinvolvedintranslationdrug
discovery 349 VidhuSharma
14.3 Recentsuccessfuladvancesintranslationdrug
discovery 351 16.1 Introduction:socioeconomicimpactof
14.3.1 Cancer 352 translationalresearch 384
14.3.2 Diabetes 355 16.1.1 Challengesfacedintranslational
14.3.3 Acquiredimmunodeficiency research 385
syndrome 355 16.2 Corefacility:sharedresearch(cid:1)shared
14.3.4 Autoimmunedisorders 356 cost 386
14.3.5 Neurologicaldisorder 357 16.2.1 Corefacilitiesofprimesignificancein
14.3.6 Cardiovasculardisease(CVD) 357 translationalresearch 388
14.4 Opportunitiesintranslationdrug 16.3 Researchanddevelopmentsupporting
discovery 358 mechanism:environmentalscan(theUnited
14.5 Challengesintranslationdrugdiscovery 359 StatesandCanada) 389
14.6 Approachestoboosttranslationaldrug 16.3.1 Supportingtranslationalresearch
discovery 360 throughcorefacilitiesintheUnited
14.7 Conclusion 364 States—frompasttopresent 390
14.8 Futureperspective 364 16.3.2 Canada’secosystemoftranslational
References 365 researchandfunding
mechanism 392
16.3.3 Highlightsaroundtheworld 394
15 FLAGSHIP: A novel drug discovery
16.3.4 Glimpsesofglobalresearchand
platform originating from the developmentexpenditure 396
“dark matter of the genome” 371 16.4 Efficienciesandleanpracticesinresearch
management 399
NeerajVerma,SiddharthManvatiandPawanDhar
16.4.1 Corefacilitiesbusinessmodel 399
15.1 Introduction 371 16.4.2 Governancemodelforcore
15.2 Designingnoveltherapeuticpeptidesfrom facility 402
darkmatterofthegenome 373 16.4.3 Corefacilitiesandresearch
15.2.1 Antimicrobialpeptides 373 outcome 402
x Contents
16.5 Finalnotes:learningsforfuture 403 17.3 Methodology 412
16.5.1 Integrationofcorefacilitieswithin 17.3.1 Selectionofcriteria 413
theinstitutionalstrategicplan 403 17.3.2 Selectionofindicators 414
16.5.2 Comprehensiveavailabilityof 17.3.3 Applicationofnewtechniquefor
infrastructureinventory 403 orderofpreferencebysimilarityto
16.5.3 Impactmeasurement 404 idealsolutionapproach 414
Acknowledgments 404 17.3.4 Analysisofsensitivity 416
References 404 17.4 Resultanddiscussion 416
17.4.1 Resultfromtechniquefororderof
17 A new TOPSIS-based approach to preferencebysimilaritytoideal
evaluate the economic indicators in the solution1 416
healthcare system and the impact of 17.4.2 Resultfromtechniquefororderof
preferencebysimilaritytoideal
biotechnology 407
solution 417
PriyankaMajumderandApuKumarSaha 17.4.3 Resultfromsensitivityanalysis 418
17.5 Conclusion 418
17.1 Introduction 408
References 419
17.2 Techniquefororderofpreferencebysimilarity
toidealsolutionapproach 410
Glossary 421
17.2.1 Metricspace 410
17.2.2 Newtechniquefororderofpreference Index 425
bysimilaritytoidealsolution
approach 411