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River Publishers Series in Research and Business Chronicles: Biotechnology and Medicine
Copyright © 2015. River Publishers. All rights reserved.
Post-genomic Approaches in Cancer and Nano Medicine
Editors Kishore R. Sakharkar Meena K. Sakharkar Ramesh Chandra
Post-Genomic Approaches in Cancer and Nano Medicine, River Publishers, 2015. ProQuest Ebook Central,
Copyright © 2015. River Publishers. All rights reserved.
Post-genomic Approaches in Cancer and Nano Medicine
RIVER PUBLISHERS SERIES IN RESEARCH AND BUSINESS CHRONICLES: BIOTECHNOLOGY AND MEDICINE Volume 4 Series Editors ALAIN VERTES Sloan Fellow, NxR Biotechnologies, Basel, Switzerland
PAOLO DI NARDO University of Rome Tor Vergata, Italy
PRANELA RAMESHWAR Rutgers University, USA
Copyright © 2015. River Publishers. All rights reserved.
Combining a deep and focused exploration of areas of basic and applied science with their fundamental business issues, the series highlights societal benefits, technical and business hurdles, and economic potentials of emerging and new technologies. In combination, the volumes relevant to a particular focus topic cluster analyses of key aspects of each of the elements of the corresponding value chain. Aiming primarily at providing detailed snapshots of critical issues in biotechnology and medicine that are reaching a tipping point in financial investment or industrial deployment, the scope of the series encompasses various specialty areas including pharmaceutical sciences and healthcare, industrial biotechnology, and biomaterials. Areas of primary interest comprise immunology, virology, microbiology, molecular biology, stem cells, hematopoiesis, oncology, regenerative medicine, biologics, polymer science, formulation and drug delivery, renewable chemicals, manufacturing, and biorefineries. Each volume presents comprehensive review and opinion articles covering all fundamental aspect of the focus topic. The editors/authors of each volume are experts in their respective fields and publications are peer-reviewed.
For a list of other books in this series, visit www.riverpublishers.com http://riverpublishers.com/series.php?msg=Research and Business Chronicles: Biotechnology and Medicine
Post-genomic Approaches in Cancer and Nano Medicine Kishore R Sakharkar OmicsVista, Singapore
Meena K Sakharkar Department of Pharmacy and Nutrition, University of Saskatchewan, SK, Canada
Ramesh Chandra
Copyright © 2015. River Publishers. All rights reserved.
Department of Chemistry, Delhi University, India B. R. Ambedkar Center for Biomedical Research, University of Delhi, India
Published, sold and distributed by: River Publishers Niels Jernes Vej 10 9220 Aalborg Ø Denmark
ISBN: 978-87-93102-86-6 (Hardback) 978-87-93102-87-3 (Ebook)
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©2015 River Publishers All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, mechanical, photocopying, recording or otherwise, without prior written permission of the publishers.
Contents
Series Note
xv
Preface
xvii
Acknowledgements
xix
List of Figures
xxi
List of Tables
xxxi
1 Alternative Splicing and Cancer 1 J.E. Kroll, A.F. Fonseca and S.J.de Souza 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Overall Features of mRNA Splicing . . . . . . . . . 1 1.1.2 Alternative Splicing . . . . . . . . . . . . . . . . . 2 1.1.2.1 Types of alternative splicing . . . . . . . . 3 1.1.2.2 Identification of alternative splicing variants 4 1.1.3 Alternative Splicing and Cancer . . . . . . . . . . . 6 1.1.3.1 Mutations affecting splicing signals and regulatory elements . . . . . . . . . . . . 6 1.1.3.2 Splicing factors affected in cancer . . . . . 7 1.1.3.3 Protein families altered by alternative splicing in cancer . . . . . . . . . . . . . . . . 8 1.1.3.4 Splicing variants as cancer biomarkers . . 9 1.2 Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . 11
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Non-coding RNAs as Molecular Tools Renu Wadhwa, Yoshio Kato, Ran Gao and Sunil C. Kaul 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Ribozyme as Molecular Tool . . . . . . . . . . . . . . . . . 2.2.1 Muscle Differentiation as a Model . . . . . . . . . .
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vi Contents
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2.4
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2.2.2 Cancer Mechanism as a Model . . . . . . . . 2.2.3 Cancer Targets as a Model . . . . . . . . . . siRNA as a Molecular Tool . . . . . . . . . . . . . . 2.3.1 Anticancer siRNAs and Cancer Targets . . . 2.3.2 Cancer Drug Target Screening . . . . . . . . miRNA as a Molecular Tool . . . . . . . . . . . . . 2.4.1 Detection of miRNAs . . . . . . . . . . . . 2.4.1.1 RT-PCR and Related Technologies 2.4.1.2 Hybridization-Based Technologies 2.4.1.3 Imaging of miRNAs in Cells . . . 2.4.2 miRNA in Cancers . . . . . . . . . . . . . .
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PPAR Responsive Regulatory Modules in Breast Cancer Meena K Sakharkar, Babita Shashni, Karun Sharma, Ramesh Chandra and Kishore R Sakharkar 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 PPAR Gamma, Breast Cancer and Energy Metabolism . . . 3.3 Glycolysis, Cell pH and NHE1 . . . . . . . . . . . . . . . . 3.4 ROS and Breast Cancer . . . . . . . . . . . . . . . . . . . . 3.5 Natural and Synthetic Ligands of PPARγ . . . . . . . . . . 3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . .
4 Animal Model of Cancer and Infection Karun Sharma, Babita Shashni, Meena K Sakharkar, Kishore R Sakharkar and Ramesh Chandra 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Mouse Models for Cancer . . . . . . . . . . . . . . . . . . 4.3 In-vivo Methods . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Tumor Model by use of Chemical Agents . . . . . . 4.3.1.1 DMBA- induced mouse skin papillomas . 4.3.1.2 N-methyl, N-nitrosurea (MNU) induced Rat mammary gland carcinogenesis . . . . 4.3.1.3 DMBA induced rat mammary gland carcinogenesis . . . . . . . . . . . . . . . . 4.3.1.4 MNU-induced tracheal squamous cell carcinoma in hamster . . . . . . . . . . . . . 4.3.1.5 Azoxymethane(AOM) induced aberrant crypt Foci in Rat . . . . . . . . . . . . . .
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4.3.1.6
1,2 Dimethylhydralazine(DMH) induced colorectal adenocarcinomain rat and mouse . . . . . . . . . . . . . . . . . . . 4.3.2 Models Involving Cell Lines or Tumor Pieces (Xenograft) . . . . . . . . . . . . . . . . . . . . . . 4.3.2.1 Cell lines . . . . . . . . . . . . . . . . . . 4.3.2.2 Xenograft . . . . . . . . . . . . . . . . . 4.3.3 Transgenic Mice . . . . . . . . . . . . . . . . . . . 4.3.3.1 Retroviral vector method . . . . . . . . . 4.3.3.2 DNA microinjection method . . . . . . . 4.3.3.3 Nude mouse . . . . . . . . . . . . . . . . 4.4 Anti-cancer Drugs . . . . . . . . . . . . . . . . . . . . . . . 4.5 Mouse Model of Infection . . . . . . . . . . . . . . . . . . 4.5.1 Air Pouch Model . . . . . . . . . . . . . . . . . . . 4.5.2 Dermatophytosis . . . . . . . . . . . . . . . . . . . 4.5.3 Endocarditis Model . . . . . . . . . . . . . . . . . . 4.5.4 Lung Infection Model . . . . . . . . . . . . . . . . 4.5.5 Thigh Infection Model . . . . . . . . . . . . . . . . 4.5.6 Urinary Tract Infection Model . . . . . . . . . . . .
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90 90 90 91 91 92 92 94 94 96 96 97 98 99 99 99
Potential Application of Natural Compounds for the Prevention and Treatment of Hepatocellular Carcinoma 101 Shikha Satendra Singh, Sakshi Sikka, Gautam Sethi and Alan Prem Kumar 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 101 5.2 Risk Factors Involved in HCC . . . . . . . . . . . . . . . . 103 5.2.1 Viral infections . . . . . . . . . . . . . . . . . . . . 103 5.2.1.1 Hepatitis B virus (HBV) . . . . . . . . . . 103 5.2.1.2 Hepatitis C virus (HCV) . . . . . . . . . . 104 5.2.2 Toxins . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.2.2.1 Chemical carcinogens . . . . . . . . . . . 104 5.2.2.2 Other carcinogens . . . . . . . . . . . . . 104 5.2.3 Diet and Metabolic factors . . . . . . . . . . . . . . 105 5.2.4 Genetic Factors . . . . . . . . . . . . . . . . . . . . 105 5.2.5 Cirrhosis . . . . . . . . . . . . . . . . . . . . . . . 106 5.3 Types of Liver Cancers . . . . . . . . . . . . . . . . . . . . 106 5.3.1 Epithelial tumors (Malignant) . . . . . . . . . . . . 106 5.3.1.1 Hepatocholangiocarcinoma (HCC-CC) . . 106 5.3.1.2 Hepatoblastoma . . . . . . . . . . . . . . 106
viii Contents 5.3.2
5.4
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5.5
Non-Epithelial Tumors (Benign) . . . . . . . . . . . 5.3.2.1 Hepatic angiomyolipoma (AML) . . . . . 5.3.2.2 Hemangioma . . . . . . . . . . . . . . . . 5.3.3 Non-Epithelial Tumors (Malignant) . . . . . . . . . 5.3.3.1 Hepatic Angiosarcoma . . . . . . . . . . . 5.3.3.2 Hepatic epithelioidhemangioendothelioma (EH) . . . . . . . . . . . . . . . . . . . . 5.3.3.3 Embryonal sarcoma . . . . . . . . . . . . 5.3.3.4 Hepatic Rhabdomyosarcoma . . . . . . . 5.3.4 Miscellaneous tumors . . . . . . . . . . . . . . . . 5.3.4.1 Solitary fibrous tumor . . . . . . . . . . . 5.3.4.2 Hepatic teratomas . . . . . . . . . . . . . 5.3.4.3 York Sac tumor . . . . . . . . . . . . . . 5.3.4.4 Carcinosarcoma . . . . . . . . . . . . . . 5.3.4.5 Rhabdiod tumor . . . . . . . . . . . . . . 5.3.4.6 Hepatic mesenchymalhamartoma (HMH) . . . . . . . . . . . . . . . . . . . 5.3.5 Secondary tumors . . . . . . . . . . . . . . . . . . . Dysregulated Signaling Cascades in HCC . . . . . . . . . . 5.4.1 STAT3 signaling . . . . . . . . . . . . . . . . . . . 5.4.2 Wnt/β-catenin pathway . . . . . . . . . . . . . . . 5.4.3 NFκB pathway . . . . . . . . . . . . . . . . . . . . 5.4.4 PI3K/AKT/mTOR pathway . . . . . . . . . . . . . 5.4.5 MAPK pathway . . . . . . . . . . . . . . . . . . . 5.4.6 VEGF pathway . . . . . . . . . . . . . . . . . . . . 5.4.7 Other miscellaneous pathways . . . . . . . . . . . . Reported Anti-Cancer Effects of Natural Compounds Against Hepatocellular Carcinoma . . . . . . . . . . . . . . . . . . 5.5.1 Curcumin . . . . . . . . . . . . . . . . . . . . . . . 5.5.2 Selenium . . . . . . . . . . . . . . . . . . . . . . . 5.5.3 Epigallocatechin-3-gallate (EGCG) . . . . . . . . . 5.5.4 Resveratrol . . . . . . . . . . . . . . . . . . . . . . 5.5.5 Ursolic Acid . . . . . . . . . . . . . . . . . . . . . 5.5.6 Pterostilbene . . . . . . . . . . . . . . . . . . . . . 5.5.7 Celastrol . . . . . . . . . . . . . . . . . . . . . . . 5.5.8 Honokiol . . . . . . . . . . . . . . . . . . . . . . . 5.5.9 γ- Tocotrienol . . . . . . . . . . . . . . . . . . . . 5.5.10 Butein . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.11 β- Escin . . . . . . . . . . . . . . . . . . . . . . . .
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5.6
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5.5.12 Diosgenin . . . . . . 5.5.13 Phyllanthusniruri . . 5.5.14 Oleanolic Acid . . . 5.5.15 Mushrooms . . . . . 5.5.16 JuglansMandshurica 5.5.17 Quercetin . . . . . . 5.5.18 Ginger . . . . . . . Conclusions . . . . . . . . .
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Nanomaterials: A Ray of Hope in Infectious Disease Treatment 167 Rashmi M. Bhande and C.N.Khobragade 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 167 6.2 Systemic Applications of Nanoparticles . . . . . . . . . . . 170 6.2.1 Antimicrobial Nanotechnology Based Drug Delivery 171 6.2.1.1 NPs for efficient antimicrobial drug delivery 171 6.2.1.2 Liposome for antimicrobial drug delivery . 171 6.2.1.3 Solid lipid (SL) NPs . . . . . . . . . . . . 172 6.2.1.4 Polymeric NPs . . . . . . . . . . . . . . . 173 6.2.1.5 Dendrimers . . . . . . . . . . . . . . . . 174 6.2.1.6 Drug-infused nanoparticles . . . . . . . . 175 6.2.1.7 Chitosan Nanoparticle . . . . . . . . . . . 175 6.2.1.8 Silver Nanoparticle . . . . . . . . . . . . 176 6.2.1.9 Copper Nanoparticle . . . . . . . . . . . . 176 6.2.1.10 Titanium Nanoparticle . . . . . . . . . . . 177 6.2.1.11 Magnesium Nanoparticles . . . . . . . . . 177 6.2.1.12 Zinc Nanoparticle . . . . . . . . . . . . . 178 6.2.1.13 Nitric oxide-releasing Nanoparticles . . . 178 6.2.1.14 Immunomodulatory effects of nanotechnologybased drug delivery systems . . . . . . . . 179 6.2.1.15 Nanotechnology-based vaccines and immunostimulatory adjuvant . . . . . . . 179 6.2.1.16 Synthetic polymers . . . . . . . . . . . . 180 6.2.1.17 Nanoemulsions . . . . . . . . . . . . . . 180 6.2.1.18 Immune-stimulating complexes . . . . . . 180 6.2.1.19 Cytidine-phosphate-guanosine (CpG) motifs . . . . . . . . . . . . . . . . . . . 181 6.2.1.20 Fullerenes (C60) and fullerene-derivatives 181 6.2.1.21 Carbon nanotubes (CNTs) . . . . . . . . . 181
x Contents 6.2.1.22 Surfactant-based nanoemulsions . . . . . Nanocarriers . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Types of nanocarriers . . . . . . . . . . . . . . . . . 6.3.1.1 Liposome . . . . . . . . . . . . . . . . . 6.3.1.2 Polymeric micelles . . . . . . . . . . . . 6.3.1.3 Polymer blended nanoparticles . . . . . . 6.3.1.4 Fluorescent Nanoparticles . . . . . . . . . 6.4 Synergism of Antibiotics with Zinc Oxide Nanoparticles: A Study of Urinary Tract Infections . . . . . . . . . . . . . . 6.4.1 Structural and Morphological Evaluation of Synthesized ZnO NPS . . . . . . . . . . . . . . . . . . . . 6.4.2 Surface Analysis of Synthesized ZnO NPS . . . . . 6.4.3 Optical Analysis . . . . . . . . . . . . . . . . . . . 6.4.4 TIME–KILL ASSAY . . . . . . . . . . . . . . . . . 6.5 Appications of Nanoparticles . . . . . . . . . . . . . . . . . 6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . .
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Nanomedicine for the Treatment of Oxidative Stress Injuries Toru Yoshitomi, Long Binh Vong and Yukio Nagasaki 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Preparation and Characterization of Redox Polymers and Nanoparticles . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Design and Preparation of Redox Polymers and Redox Nanoparticles . . . . . . . . . . . . . . . . . 7.2.2 Safety of RNPs . . . . . . . . . . . . . . . . . . . . 7.2.3 pH-Sensitive Disintegration of RNPN . . . . . . . . 7.3 Treatment of Ischemia-Reperfusion Injuries with pHSensitive Redox Nanoparticles . . . . . . . . . . . . . . . . 7.3.1 Ischemia-Reperfusion Injury . . . . . . . . . . . . . 7.3.2 Biodistribution and Morphological Change of RNPN . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.3 Therapeutic Effect of RNPs on Renal IschemiaReperfusion Injury in Mice . . . . . . . . . . . . . . 7.4 Oral Nanotherapy with pH-Insensitive Redox Nanoparticle for the Treatment of Inflammatory Bowel Disease . . . . . . 7.4.1 Inflammatory Bowel Disease (IBD) . . . . . . . . . 7.4.2 Specific Accumulation of Orally Administered RNPO without Uptake in the Blood Stream . . . . . . . . .
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Therapeutic Effect of RNPO in a Mouse Model of Colitis . . . . . . . . . . . . . . . . . . . . . . . . . 213 Treatment of Other Oxidative Stress Injuries . . . . . . . . . 213 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 214 7.4.3
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7.5 7.6 8
Rational Design of Multifunctional Nanoparticles for Targeted Cancer Imaging and Therapy 219 Arun K. Iyer 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 219 8.1.1 Mechanism of Tumor Selective Delivery: Passive and Active Targeting . . . . . . . . . . . . . . . . . . . 221 8.2 Rational Design of Nanoparticles . . . . . . . . . . . . . . . 224 8.2.1 Shape of Nanoparticles . . . . . . . . . . . . . . . . 226 8.2.2 Size and Surface Charge of Nanoparticles . . . . . . 227 8.2.3 Surface Functionalization of Nanoparticles . . . . . 228 8.3 Types of Nanoparticle Systems for Cancer Therapy . . . . . 229 8.3.1 Polymeric Nanoparticles . . . . . . . . . . . . . . . 229 8.3.2 Polymeric Micelles and Dendrimers . . . . . . . . . 231 8.3.3 Lipid-Based Nanoparticles . . . . . . . . . . . . . . 233 8.4 Multifunctional Theranostic Nanosystems . . . . . . . . . . 235 8.5 Illustrative Examples of Multifunctional Nanosystems . . . 238 8.5.1 Iron Oxide Nanoparticles-Based Theranostic Systems . . . . . . . . . . . . . . . . . . . . . . . . 238 8.5.2 Quantum-Dots-Based Nano-Theranostic Agents . . . 239 8.5.3 Gold Nanoparticles-Based Theranostic Agents . . . 241 8.5.4 Silica Nanoparticles and Carbon Nanotubes-Based Theranostic Agents . . . . . . . . . . . . . . . . . . 243 8.6 Conclusions and Future Directions . . . . . . . . . . . . . . 244
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Nanomedicine for the Treatment of Breast Cancer Surendra Nimesh, Nidhi Gupta and Ramesh Chandra 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Etiology of Breast Cancer . . . . . . . . . . . . . . . . . . . 9.2.1 Histology and Diagnosis of Breast cancer . . . . . . 9.2.2 Luminal A subgroup . . . . . . . . . . . . . . . . . 9.2.3 Luminal B Subgroup . . . . . . . . . . . . . . . . . 9.2.4 Basal-like Carcinomas, a Subgroup of Triple-negative Breast Cancers . . . . . . . . . . . . . . . . . . . . 9.2.5 Normal Breast . . . . . . . . . . . . . . . . . . . .
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9.2.6 Claudin-low . . . . . . . . . . . . . . . . Nanomedicine . . . . . . . . . . . . . . . . . . . . Targeted Nanomedicine . . . . . . . . . . . . . . . 9.4.1 Passive Targeting . . . . . . . . . . . . . . 9.4.2 Active Targeting . . . . . . . . . . . . . . Nanomedicince for the Treatment of Breast Cancer 9.5.1 Liposomes . . . . . . . . . . . . . . . . . 9.5.2 Gold Nanoparticles . . . . . . . . . . . . 9.5.3 Carbon Nanotubes . . . . . . . . . . . . . 9.5.4 Human Serum Albumin Nanoparticles . . 9.5.5 Other Nanomaterials . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . .
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10 Nanoparticle-Based Drug Delivery Systems: Associated Toxicological Concerns and Solutions 287 James Lyons, Aniruddha Bhati, Jaimic Trivedi and Arati Sharma 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 287 10.1.1 Nanotechnology and Nanoparticles (NP) . . . . . . 288 10.1.2 Nanotechnology and Cancer . . . . . . . . . . . . . 289 10.1.2.1 Nanoparticles in diagnostics . . . . . . . . 289 10.1.2.2 MRI (Magnetic Resonance Imaging) . . . 290 10.1.2.4 Cantilevers . . . . . . . . . . . . . . . . . 290 10.1.2.5 Bio-barcode . . . . . . . . . . . . . . . . 291 10.2 Nanoparticles as Onco-therapeutics . . . . . . . . . . . . . 291 10.2.1 Drug Delivery Systems (DDSs) . . . . . . . . . . . 291 10.2.1.1 Polymeric biodegradable nanoparticles . . 292 10.2.1.2 Metallic nanoparticles . . . . . . . . . . . 292 10.2.1.3 Ceramic nanoparticles . . . . . . . . . . . 292 10.2.1.4 Polymeric micelles . . . . . . . . . . . . 293 10.2.1.5 Dendrimers . . . . . . . . . . . . . . . . 293 10.2.1.6 Liposomes . . . . . . . . . . . . . . . . . 294 10.2.2 Nucleic acid carriers . . . . . . . . . . . . . . . . . 295 10.2.3 Magnetofection . . . . . . . . . . . . . . . . . . . . 295 10.3 Nanotoxicology . . . . . . . . . . . . . . . . . . . . . . . . 295 10.3.1 Important physiochemical properties, which causes toxicity . . . . . . . . . . . . . . . . . . . . . . . . 296 10.3.1.1 Particle size . . . . . . . . . . . . . . . . 296 10.3.1.2 Particle composition and charge . . . . . . 296
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10.3.1.3 Particle surface area . . . . . . . . . . . . 10.3.2 Nanodrug delivery systems and toxicity . . . . . . . 10.3.2.1 Liposome toxicity . . . . . . . . . . . . . 10.3.2.2 Dendrimer-based drug delivery system toxicity . . . . . . . . . . . . . . . . . . . . 10.3.2.3 Metallic nanoparticle toxicity . . . . . . . 10.3.2.4 Quantum dot toxicity . . . . . . . . . . . Assessment of Nanotoxicology . . . . . . . . . . . . . . . . 10.4.1 In vitro cell culture assays . . . . . . . . . . . . . . 10.4.1.1 Biocompatibility assays . . . . . . . . . . 10.4.1.2 Hemolytic and platelet aggregation tests . 10.4.1.3 Reactive Oxygen Species (ROS) and oxidative stress detection assays . . . . . 10.4.1.4 Genotoxicity assays . . . . . . . . . . . . 10.4.2 In vivo animal assays . . . . . . . . . . . . . . . . . 10.4.2.1 Dose-range determination . . . . . . . . . 10.4.2.2 Pharmacokinetics . . . . . . . . . . . . . 10.4.2.3 Immunotoxicity . . . . . . . . . . . . . . Toxicity Modulation to Optimally Exploit Clinical Potential of Nanoparticles as a Therapeutics . . . . . . . . . . . . . . 10.5.1 Size . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5.2 Shape . . . . . . . . . . . . . . . . . . . . . . . . . 10.5.3 Charge . . . . . . . . . . . . . . . . . . . . . . . . 10.5.4 Masking to escape immune rejection . . . . . . . . . 10.5.5 Leaching of the constituents entrapped in nanoparticles . . . . . . . . . . . . . . . . . . . . . 10.5.6 Adding targeting moieties to nanoparticles . . . . . . Future Implication . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . .
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11 Biodegradable Carrier Systems for Drug and Vaccine Delivery Anil Mahapatro and Dinesh Singh 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Nanoparticle Fabrication . . . . . . . . . . . . . . . . . . . 11.2.1 Dispersion of preformed polymers . . . . . . . . . . 11.2.1.1 Polymerization Methods . . . . . . . . . . 11.2.1.2 Ionic gelation method for hydrophilic polymers . . . . . . . . . . . . . . . . . . . .
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10.4
10.5
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10.6 10.7
297 297 298 298 298 299 299 299 299 299 299 299 300 300 300 301 302 302 302 303 303 304
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xiv Contents
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11.2.1.3 Biodegradable polymer matrix for nanoparticle fabrication . . . . . . . . . . . . . . 11.2.1.4 Nanoparticle functionalization . . . . . . 11.3 Specific Applications of Biodegradable Nps . . . . . . . . . 11.3.1 Tumor Targeting . . . . . . . . . . . . . . . . . . . 11.3.1.1 Nanoparticles for Oral delivery . . . . . . 11.3.1.2 Nanoparticles for vaccine adjuvants and gene delivery . . . . . . . . . . . . . . . 11.3.1.3 Nanoparticles for drug delivery into the brain . . . . . . . . . . . . . . . . . . . . 11.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . .
324 325 328 328 329 330 331 332
About the Editors
341
About the Contributors
343
Index
351
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Series Note
The deciphering in 2003 of the nucleotide sequence of the human genome, which followed the determination of the chromosomal sequences of an array of model microorganisms including Escherichia coli, Bacillus subtilis, and Saccharomyces cerevisiae, is a landmark in biology that has paved the way for a revolution in research and development in biotechnology and pharmaceutical sciences. Moreover, several novel discovery tools have since emerged, including dramatically enhanced computers, sequencing instruments with dramatically higher throughout and decreased costs, softwares conferring the ability to generate and manage very large amounts of data, and systems biology tools which have enabled in silico experiments and the creation of virtual patients or virtual microbes to both accelerate and increase the scope of pharmaceutical and biotechnological research. Whereas more than 10 years have already elapsed since this major scientific milestone, the translation into novel products, perhaps best exemplified by the current focus of pharmaceutical companies on personalised medicine, has only reached mainstream at the beginning of the present decade. The impact of post-genomic approaches in cancer and nano-medicine development is the focal point of the present monograph. Starting with a review of underlying bases of cancer and the biology of coding and non-coding RNAs, principles of discovery of novel drugs including advances in animal models for oncology are laid out here. Revisiting the potential of natural compounds for the treatment and prevention of carcinomas, the discussion subsequently explores one of the next innovation S-curves in cancer therapeutics using nanomaterials as a case study. The ultimate purpose of the journey is to accelerate the development of disease-modifying pharmaceuticals, and answer unmet medical needs to enable cancer patients worldwide achieve remission and, ideally, cure. Alain Vertès, Basel, Switzerland Pranela Rameshwar Rutgers, USA Paolo di Nardo, Roma, Italy
xv
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Preface
Cancer is a complex disease involving genomic alterations across several molecular mechanisms. Systematic and comprehensive elucidation of the molecular landscape of a wide range of cancers complemented by genome-wide approaches to interrogating the function of cancer genes and the vulnerabilities of tumors will pave the way for understanding the basic molecular mechanisms of cancer and applying this knowledge to transform the practice of cancer medicine. Alternative splicing has critical roles in normal cell function and development and can promote growth and survival in cancer. Aberrant splicing can lead to loss-of-function in tumor suppressors or activation of oncogenes and cancer pathways. Cancerspecific changes in splicing profiles can occur through mutations that are affecting splice sites and splicing control elements, and also by alteration in the expression of proteins that control splicing decisions. Chapter 1 presents a comprehensive review on alternative splicing and how it contributes to tumorigenesis by producing splice isoforms that can stimulate cell proliferation and cell migration or induce resistance to apoptosis and anticancer agents. Chapter 2 discusses the use of non-coding RNAs as molecular tools to understand the molecular mechanism of cell proliferation control during carcinogenesis, differentiation and drug-induced cytotoxicity. Malignant cells exhibit metabolic changes, when compared to their normal counterparts. Chapter 3 delineates the identification and validation of novel targets of nuclear hormone receptor (PPAR-γ) in glycolytic pathway and their role in breast cancer pathophysiology. Animal experiments have contributed significantly to our understanding of mechanisms of disease and mouse has been the model of choice. Chapter 4 describes various mouse/rat models for cancer and infectious diseases. Chapter 5 describes the use of natural compounds for hepatocellular carcinoma. The advent of nanotechnology promises revolutionizing many fields including oncology, by proposing advanced systems for cancer treatment.
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xviii Preface
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Targeted drug delivery systems are among the most successful examples of nanotechnology. In the past few years, there has been significant momentum in the field of nanomedicine with the development of novel nanoparticles for the diagnosis and treatment of cancer. Their small size, large surface area-tovolume ratio, and surface characteristics enable them to have viable carrier for site specific delivery of vaccines, genes, drugs and other biomolecules in the body. They also have compatibility with different administration routes, which makes them highly attractive in many aspects of oncology and infectious diseases. Chapter 6 through Chapter 11 discuss the use of nanoparticles in cancer therapeutics. In putting together this book, we have tried to bring to table the contributions of various experts towards some key aspects in drug discovery with focus on cancer and naomedicine. As editors of this book, we are grateful to all the contributors who have made this book possible.
Acknowledgements
On behalf of all the authors, we would like to thank all our mentors, colleagues and friends who instilled in us the culture of science. Without support from them, we could not have written this book. The unconditional love and support from our families is gratefully acknowledged. Finally, we would like to take this opportunity to acknowledge the services of the team of River Publishers and everyone who collaborated in producing this book. Kishore R. Sakharkar Meena K. Sakharkar
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Ramesh Chandra
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List of Figures Figure 1.1 Figure 1.2 Figure 1.3
Figure 1.4
Figure 1.5 Figure 2.1 Figure 2.2
Figure 2.3
Figure 2.4
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Figure 2.5
Figure 2.6
Most important splicing signals . . . . . . . . . . Molecular diversity increases as information flows from genes to proteins . . . . . . . . . . . . . . . Types of alternative splicing events: (A) exon skipping, (B) 3’ alternative border, (C) 5’ alternative border, (D) intron retention, (E) dual-specific splice site, (F) mutually exclusive exons and (G) exon skipping of multiple adjacent exons . . . . . . . . RNA-Seq mapping, as implemented by TopHat. (A) Mapping of splice-sites boundaries. (B) Amount of aligned reads (read depth) . . . . . . . . . . . . . . CD44 isoforms associated to cancer . . . . . . . . Schematic representation of action of ribozymes and miRNA . . . . . . . . . . . . . . . . . . . . . . . Schmatic representation of use of randomized ribozyme library for identification of genes involved in muscle differentiation: adopted from Wadhwa et al. [26] . . . . . . . . . . . . . . . . . . . . . . Demonstration of ARF-Per19p interaction in mouse (p19ARF), but not in human (p14ARF) cells. Pex19p specific ribozymes increased the activity of p19ARF only as shown in G. Adopted from Wadhwa et al. [39] . . . . . . . . . . . . . . . . . . . . . . Use of randomized for identification of genes involved in killing od cancer cells by Ashwagandha leaf extract (i-Extract)and its pure phytochemical, Withanone. Adopted from Widodo et al. [46] . . . Use of mortalin staining as a reporter for induction of senescence in cancer cells and hence the identification of anticancer siRNAs. Adopted from Gao et al. [65] . . . . . . . . . . . . . . . . . . . . . . A representative flowchart of miRNA detection. Three major approaches to detect miRNAs;
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xxii List of Figures
Figure 2.7
Figure 2.8
Figure 2.9
Figure 2.10 Figure 3.1
Figure 3.2
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Figure 3.3
Figure 3.4
hybridization, RT-PCR and cellular imaging are shown . . . . . . . . . . . . . . . . . . . . . . . . Flowchart describing the modification of miRNAs for amplification is shown. In order to amplify miRNA pools from the total RNA, miRNAs can be tagged with adaptors based on their chemical characteristics as shown . . . . . . . . . . . . . . . Schematic representation of molecular spotter probe. The probe is designed to be quenched in the presence of precursor RNA. It emits fluorescence only upon hybridization to the mature RNA . . . . Schematic representation of the dual-color sensor vector system. Reporter system enables the realtime, quantitative detection of miRNAin single cells using dual color fluorescent proteins . . . . . . . . Demonstration of targeting of p53 and p21WAF1 by miR-296. Adopted from Yoon et al [88] . . . . . . PPARγ activation mechanism. Upon ligand activation PPARγ heterodimerizes with Retinoid X Receptor (RXR) in nucleus and binds to PPRE and/or PACM motifs in the promoter region and modulates the expression of genes downstream. The consensus PPRE site consists of a direct repeat of the sequence AGGTCA separated by a single/double nucleotide, which is designated as DR-1 site/DR-2 site and PACM site consist of 15 bp consensus sequence, TTCATTTGGACATTG. The PACM motifs are reported to be more common than PPREs . . . . . . . . . . . . . . . . . . . . . . . . Molecular targets of PPARγ and pathways associated [Adapted from 27] . . . . . . . . . . . . . . . Genomic structure of the human PPAR gamma gene (5’ end) and PPARγ mRNA splicing forms and protein variants. There are seven isoforms of PPARγ with common exons 1–6 . . . . . . . . . . . . . . Transcriptional regulation of PPARγ gene targets. A PPAR protein binds PPRE/ PACM motifs in combination with retinoid X receptors (RXRs) upon
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Figure 3.5 Figure 3.6
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Figure 3.7
Figure 3.8
ligand activation. The two paired up proteins then regulate transcription of various genes e.g. PPARγ upon activation is known to down regulate glycolytic genes - PGK1 and PKM2, pH regulator -NHE1, anti-oxidant enzyme - MnSOD in breast cancer cells . . . . . . . . . . . . . . . . . . . . . PPARγ activation inhibits many malignancies . . . Metabolic targets of PPARγ. Many glycolytic enzymes are over expressed in cancers. Glycolytic enzyme pyruvate kinase-muscle 2 (PKM2) is a key regulator of tumor metabolism which promotes tumor growth and Warburg effect by switching between its dimeric form the active one, which has higher affinity for substrate Phosphoenol pyruvate (PEP) to tetrameric form the inactive form, with lower affinity for substrate PEP and vice-versa. This switching behavior of PKM2 keeps a balance of activation of many pathways including, glycerol, serine/glycine, ether/ester phospholipid pyrimidine biosynthesis (in green) and oxidative metabolism for energy production, thereby promoting tumor growth and tumor cell proliferation . . . . . . . . . Regulation of glycolytic genes - PGK1 and PKM2 by PPARγ in human breast cancer cell lines – MDAMB-231 and MCF-7. The human breast cancer cells were exposed to 10 μM of PPARγ inhibitor GW9662 for 4 h followed by 5 μM and 10 μM of 15d-PGJ2 for 48 h at 37 ◦ C. PPARγ activation by 15d-PGJ2 down regulated the expression of glycolytic enzyme, PGK1 and PKM2 in breast cancer cell lines - MDA-MB-231 (7A and 7C) and MCF-7 (7B and 7D). Inhibiting the activation of PPARγ by the PPARγ inhibitor GW9662, did not affect the expression of PGK1 and PKM2, suggesting the transcriptional regulation of these glycolytic genes by PPARγ [Adapted from 27] . . . . . . . . . . . Apotosis and PPARγ. Apoptosis was initiated in human breast cancer cells lines MDA-MB-231 and MCF-7 upon 15d-PGJ2 activation of PPARγ.
64 65
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xxiv List of Figures
Figure 3.9
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Figure 3.10
Figure 3.11
The breast cancer cells were exposed to 5 μM and 10 μM of 15d-PGJ2 for 48 h at 37 ◦ C. Caspase dependent apoptosis was confirmed in MDA-MB-231 and MCF-7 by expression studies for active caspase 8 (8A and 8B) and chromatin condensation as assessed by nuclear specific dye Hoechst (8C and 8D) [Adapted from 27] . . . . . . . . . . . . . . . . . . . . . . . Disruption of mitochondrial potential in human breast cancer cells by PPARγ ligand. The human breast cancer cells were exposed to 5 μM and 10 μM of 15d-PGJ2 for 48 h at 37 ◦ C. PPARγ ligand, 15d-PGJ2 induced loss of mitochondrial potential in human breast cancer cell lines -MDAMB-231 and MCF-7 as assessed by potential dependent dye, JC-1. In healthy/non-apoptotic cells, JC-1 exists as a monomer in the cytosol (green) and accumulates as J-aggregates in the active mitochondria, which appear red. In apoptotic cells, due to loss mitochondrial potential relatively lower dye J-aggregates accumulates in the mitochondria than cytoplasm where it is remains as a monomer. Control breast cancer cell lines had higher Red/Green flouresence ratio than 15d-PGJ2 treated test cells, suggesting the loss of mitochondrial potential by PPARγ ligand, 15d-PGJ2 [Adapted from 27] . . . . . . . . . . . . . . . . . PPARγ activation by 15d-PGJ2 represses NHE1 mRNA and protein levels in human breast cancer cell lines - MDA-MB-231 and MCF-7. The breast cancer cells were exposed to 1 μM, 3 μM and 5 μM of 15d-PGJ2 for 24 h at 37 ◦ C. A significant down regulation of NHE1 expression was observed. A similar pattern was followed at transcriptional level, suggesting PPARγ regulation of NHE1 in breast cancer cell lines [Adapted from 44] . . . . . . . . . PPARγ novel ligand, Hydroxy hydroquinone (HHQ) induces intracellular Reactive Oxygen Species (ROS) formation in human breast cancer
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List of Figures xxv
Figure 3.11
Figure 3.12
Figure 4.1 Figure 4.2 Figure 4.3 Figure 4.4 Figure 4.5 Figure 4.6 Figure 5.1
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Figure 5.2
cell lines - MDA-MB-231 and MCF-7. The breast cancer cells were treated with 12.5 μM and 25 μM of HHQ for 48 h at 37 ◦ C. Intracellular ROS formation was found to be significantly increased in HHQ treated cells as compared to control cells in a dose dependent manner was reported. The effective enhancement of ROS production by HHQ correlates to its cytotoxicity nature [Adapted from 52] . . . . . . . . . . . . . . . . . 73 PPARγ activation by 15d-PGJ2 represses MnSOD mRNA and protein levels in human breast cancer cell lines - MDA-MB-231 and MDA-MB-468. The breast cancer cells were exposed to 3 μM, 5 μM and 10 μM of 15d-PGJ2 for 24 h at 37 ◦ C. A significant repression of MnSOD level was observed in dosedependent manner [Adapted from 44] . . . . . . . 74 Retroviral vector method . . . . . . . . . . . . . . 92 DNA microinjection method . . . . . . . . . . . . 93 Anti cancer drug targets . . . . . . . . . . . . . . . 95 The Cell cycle . . . . . . . . . . . . . . . . . . . . 96 Air pouch model . . . . . . . . . . . . . . . . . . 97 Urinary tract infection . . . . . . . . . . . . . . . 100 Deregulation of JAK/STAT3 pathway in Hepatocellular carcinoma: Upregulation of IL-6, Mutations in gp130, Methylation of SOCS has been reported as plausible mechanisms for deregulation of JAK/STAT3 pathway. IL6: Interleukin-6; JAK:Janus Kinase-2; STAT3:Signal Transducer and Activation of transcription 3; SOCS: Suppressor of cytokine signaling . . . . . . . . . . . . . . . . . . 112 Deregulation of β-catenin in hepatocellular carcinoma: Upregulation of FZD7, mutations in AXINS, production of stable beta-catenin and increase in cell-cell adhesion have been postulated as plausible mechanisms for deregulation of beta-catenin pathway in HCC. FZD7: Frizzled-7 protein; GSK3B: Glycogen Synthase kinase 3 beta; TCF: Transcription factor . . . . . . . . . . . . . . . . . . . . . . 114
xxvi List of Figures Figure 5.3
Figure 5.4
Figure 6.1 Figure 6.2
Figure 6.3
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Figure 6.4 Figure 6.5 Figure 6.6 Figure 6.7 Figure 6.8 Figure 7.1
Figure 7.2
Deregulation of NFkB in hepatocellular carcinoma: Elevated levels of IL-6 and TNFα have been postulated in constitutive activation of NFkB, thereby, deregulation in hepatocellular carcinoma due to upregulation of pro-survival signals. NFkB:Nuclear factor kappa-light chain-enhancer of activated B cells; TNF: Tumor necrosis factor; IL-6: Interleukin6; IKK: IκB kinase . . . . . . . . . . . . . . . . . Deregulation of PI3K/Akt/mTOR pathway in hepatocellular carcinoma: Elevated levels of Akt phosphorylation, overexpression of phoshomTOR, somatic mutations of PTEN have been postulated to play a plausible role in deregulation of PI3K/Akt/mTOR pathway in hepatocellular carcinoma. PTEN : Phosphatase and tensin homolog; PI3K : Phophoinositide-3-kinase; IGF : Insulinlike growth factor; EGF : Epidermal growth factor; PDGF : Platelet derived growth factor; PDK1 : Phosphoinositide-dependent kinase1; PIP2: Phosphophatidylinositol 4, 5-biphosphate; PIP3:Phosphophatidylinositol 3, 4, 5-triphosphate; Akt: Protein kinase B . . . . . . . . . . . . . . . . The XRD spectra of ZnO nanoparticles . . . . . . (a) The SEM image of synthesized ZnO NPs. (b) TEM images of ZnO NPs. (c) HR-TEM of ZnO NPs (d) SAED pattern . . . . . . . . . . . . . . . . . . XPS spectra of ZnO nanoparticles. (a, b, c represents the scan over wide range and magnified band structure at Zn and O level) . . . . . . . . . . . . . UV-Visible spectrum of ZnO NPs . . . . . . . . . Time –Kill curve of E.coli . . . . . . . . . . . . . Time –Kill curve of K.pneumoniae . . . . . . . . . Time –Kill curve of S.paucimobilis . . . . . . . . . Time –Kill curve of P.aeruginosa . . . . . . . . . Chemical structures of redox polymers possessing nitroxide radicals, PEG-b-PMNT and PEG-bPMOT, and a redox nanoparticle (RNP) . . . . . . In vitro characterization of RNPN and RNPO (a) Effect of pH on the light scattering intensities of
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List of Figures xxvii
Figure 7.3
Figure 7.4
Figure 7.5
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Figure 7.6
RNPN (closed circle) and RNPO (open circle). The normalized scattering intensity (%) is expressed as the value relative to that at pH 8.2 (b,c) Xband ESR spectra of (b) RNPN and (c) RNPO from pH 5.6 to 8.2. Reprinted with permission c from [15] and [19] and modification. American Chemical Society (2009) and Elsevier B.V. (2011), respectively. . . . . . . . . . . . . . . . . . . . . . Environmental-signal-enhanced polymer drug therapy using RNPN for the treatment of oxidative stress injuries . . . . . . . . . . . . . . . . . . . . . . . Time profile of drug concentration in (a) blood and (b) injured kidney. (white circle, RNPO ; black circle, RNPN ; white square, TEMPOL) (c) Therapeutic effect of RNP on renal IR. BUN and Cr levels in the plasma of mice are measured at 24 h after reperfusion following 50 min of ischemia. Drugs are administered at 5 min after reperfusion. Sham veh, sham-operated and vehicle-treated groups; IR veh, vehicle . . . . . . . . . . . . . . . . . . . . . treated group; IR RNPN , RNPN -treated group; IR (RNPO , RNPO -treated group; IR TEMPOL, TEMPOL-treated group. Values are expressed as mean ± SE. *P < 0.0001 as compared to IR veh. **P < 0.005 as compared to IR veh. ***P < 0.05 as compared to IR veh. n = 7, ANOVA) Reprinted with permission from [19] and modification. c Elsevier B.V. (2011). . . . . . . . . . . . . . . . Oral nanotherapy with RNPO reduces the inflammation in UC patients. RNPO is stable, withstands the harsh conditions of the GIT, and reaches the colon to scavenge ROS, especially at sites of inflammation. . . . . . . . . . . . . . . . . . . . . (a) Specific accumulation of RNPO in the colon. Accumulation of LMW TEMPOL, RNPO , and polystyrene latex particles in the colon. The data are expressed as mean ± SE, n = 3. (b,c) Therapeutic effect of RNPO on DSS-induced colitis in mice. (a)(b) Changes in DAI. DAI is the summation
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xxviii List of Figures
Figure 8.1
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Figure 8.2
of the stool consistency index (0–3), c the fecal bleeding index (0–3), and the weight loss index (0–4). The data are expressed as mean ± SE, *P