Table Of ContentDeep Learning
for Targeted Treatments
Scrivener Publishing
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Beverly, MA 01915-6106
Publishers at Scrivener
Martin Scrivener ([email protected])
Phillip Carmical ([email protected])
Deep Learning
for Targeted Treatments
Transformation in Healthcare
Edited by
Rishabha Malviya
Gheorghita Ghinea
Rajesh Kumar Dhanaraj
Balamurugan Balusamy
and
Sonali Sundram
This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA
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Library of Congress Cataloging-in-Publication Data
ISBN 9781119857327
Cover image: Pixabay.Com
Cover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Contents
Preface xvii
Acknowledgement xix
1 Deep Learning and Site-Specific Drug Delivery: The Future
and Intelligent Decision Support for Pharmaceutical
Manufacturing Science 1
Dhanalekshmi Unnikrishnan Meenakshi,
Selvasudha Nandakumar, Arul Prakash Francis,
Pushpa Sweety, Shivkanya Fuloria, Neeraj Kumar Fuloria,
Vetriselvan Subramaniyan and Shah Alam Khan
1.1 Introduction 2
1.2 Drug Discovery, Screening and Repurposing 5
1.3 DL and Pharmaceutical Formulation Strategy 11
1.3.1 DL in Dose and Formulation Prediction 11
1.3.2 DL in Dissolution and Release Studies 15
1.3.3 DL in the Manufacturing Process 16
1.4 Deep Learning Models for Nanoparticle-Based
Drug Delivery 19
1.4.1 Nanoparticles With High Drug Delivery
Capacities Using Perturbation Theory 20
1.4.2 Artificial Intelligence and Drug Delivery Algorithms 21
1.4.3 Nanoinformatics 22
1.5 Model Prediction for Site-Specific Drug Delivery 23
1.5.1 Prediction of Mode and a Site-Specific Action 23
1.5.2 Precision Medicine 26
1.6 Future Scope and Challenges 27
1.7 Conclusion 29
References 30
v
vi Contents
2 Role of Deep Learning, Blockchain and Internet of Things
in Patient Care 39
Akanksha Sharma, Rishabha Malviya and Sonali Sundram
2.1 Introduction 40
2.2 IoT and WBAN in Healthcare Systems 42
2.2.1 IoT in Healthcare 42
2.2.2 WBAN 44
2.2.2.1 Key Features of Medical Networks
in the Wireless Body Area 44
2.2.2.2 Data Transmission & Storage Health 45
2.2.2.3 Privacy and Security Concerns in Big Data 45
2.3 Blockchain Technology in Healthcare 46
2.3.1 Importance of Blockchain 46
2.3.2 Role of Blockchain in Healthcare 47
2.3.3 Benefits of Blockchain in Healthcare Applications 48
2.3.4 Elements of Blockchain 49
2.3.5 Situation Awareness and Healthcare Decision
Support with Combined Machine Learning
and Semantic Modeling 51
2.3.6 Mobile Health and Remote Monitoring 53
2.3.7 Different Mobile Health Application
with Description of Usage in Area of Application 54
2.3.8 Patient-Centered Blockchain Mode 55
2.3.9 Electronic Medical Record 57
2.3.9.1 The Most Significant Barriers
to Adoption Are 60
2.3.9.2 Concern Regarding Negative Unintended
Consequences of Technology 60
2.4 Deep Learning in Healthcare 62
2.4.1 Deep Learning Models 63
2.4.1.1 Recurrent Neural Networks (RNN) 63
2.4.1.2 Convolutional Neural Networks (CNN) 64
2.4.1.3 Deep Belief Network (DBN) 65
2.4.1.4 Contrasts Between Models 66
2.4.1.5 Use of Deep Learning in Healthcare 66
2.5 Conclusion 70
2.6 Acknowledgments 70
References 70
Contents vii
3 Deep Learning on Site-Specific Drug Delivery System 77
Prem Shankar Mishra, Rakhi Mishra and Rupa Mazumder
3.1 Introduction 78
3.2 Deep Learning 81
3.2.1 Types of Algorithms Used in Deep Learning 81
3.2.1.1 Convolutional Neural Networks (CNNs) 82
3.2.1.2 Long Short-Term Memory Networks
(LSTMs) 83
3.2.1.3 Recurrent Neural Networks 83
3.2.1.4 Generative Adversarial Networks (GANs) 84
3.2.1.5 Radial Basis Function Networks 84
3.2.1.6 Multilayer Perceptron 85
3.2.1.7 Self-Organizing Maps 85
3.2.1.8 Deep Belief Networks 85
3.3 Machine Learning and Deep Learning Comparison 86
3.4 Applications of Deep Learning in Drug Delivery System 87
3.5 Conclusion 90
References 90
4 Deep Learning Advancements in Target Delivery 101
Sudhanshu Mishra, Palak Gupta, Smriti Ojha,
Vijay Sharma, Vicky Anthony and Disha Sharma
4.1 Introduction: Deep Learning and Targeted Drug Delivery 102
4.2 Different Models/Approaches of Deep Learning
and Targeting Drug 104
4.3 QSAR Model 105
4.3.1 Model of Deep Long-Term Short-Term Memory 105
4.3.2 RNN Model 107
4.3.3 CNN Model 108
4.4 Deep Learning Process Applications in Pharmaceutical 109
4.5 Techniques for Predicting Pharmacotherapy 109
4.6 Approach to Diagnosis 110
4.7 Application 113
4.7.1 Deep Learning in Drug Discovery 114
4.7.2 Medical Imaging and Deep Learning Process 115
4.7.3 Deep Learning in Diagnostic and Screening 116
4.7.4 Clinical Trials Using Deep Learning Models 116
4.7.5 Learning for Personalized Medicine 117
viii Contents
4.8 Conclusion 121
Acknowledgment 122
References 122
5 Deep Learning and Precision Medicine: Lessons to Learn
for the Preeminent Treatment for Malignant Tumors 127
Selvasudha Nandakumar, Shah Alam Khan,
Poovi Ganesan, Pushpa Sweety, Arul Prakash Francis,
Mahendran Sekar, Rukkumani Rajagopalan
and Dhanalekshmi Unnikrishnan Meenakshi
5.1 Introduction 128
5.2 Role of DL in Gene Identification, Unique Genomic
Analysis, and Precise Cancer Diagnosis 132
5.2.1 Gene Identification and Genome Data 133
5.2.2 Image Diagnosis 135
5.2.3 Radiomics, Radiogenomics, and Digital Biopsy 137
5.2.4 Medical Image Analysis in Mammography 138
5.2.5 Magnetic Resonance Imaging 139
5.2.6 CT Imaging 140
5.3 DL in Next-Generation Sequencing, Biomarkers,
and Clinical Validation 141
5.3.1 Next-Generation Sequencing 141
5.3.2 Biomarkers and Clinical Validation 142
5.4 DL and Translational Oncology 144
5.4.1 Prediction 144
5.4.2 Segmentation 146
5.4.3 Knowledge Graphs and Cancer Drug Repurposing 147
5.4.4 Automated Treatment Planning 149
5.4.5 Clinical Benefits 150
5.5 DL in Clinical Trials—A Necessary Paradigm Shift 152
5.6 Challenges and Limitations 155
5.7 Conclusion 157
References 157
6 Personalized Therapy Using Deep Learning Advances 171
Nishant Gaur, Rashmi Dharwadkar and Jinsu Thomas
6.1 Introduction 172
6.2 Deep Learning 174
6.2.1 Convolutional Neural Networks 175
