Foreword
Preface
Acknowledgments
Contents
About the Editor
1: History and Present Scenario of Computers in Pharmaceutical Research and Development
1.1 Introduction
1.2 The Birth of Computational Chemistry: The 1960s
1.3 Evolving Collaboration of Computational Chemists with Other Chemists: The 1970s
1.4 Multifaceted Acceptance of Computational Chemistry and the Birth of CADD: The 1980s
1.5 CADD Regaining Momentum and Entry of ML: The 1990s
1.6 CADD and Big Data: The 2000s
1.7 AI Integration with CADD: The 2010s to Present
1.8 Computer-Aided Pharmaceutics and Drug Delivery
1.9 Conclusion and Future Prospects
References
2: Historical Developments on Computer Applications in Pharmaceutics
2.1 Introduction
2.2 Computer-Aided Formulation Development
2.2.1 Design of Experiment (DOE)
2.2.2 The Agricultural Origins (1918-1940s): The Emergence of Factorial Designs and ANOVA
2.2.3 The First Industrial Era (1951-1970s): Response Surface Methodology
2.2.4 The Second Industrial Era (Late 1970s to 1990): Quality Revolution
2.2.5 The Modern Era (1990s to 2000): Continuous Quality Improvement (CQI)
2.2.6 The Present Era (2000-2020): The Era of QbD
2.3 Artificial Intelligence
2.3.1 Expert- and Knowledge-Based Systems
2.3.1.1 Tablet Formulations
2.3.1.2 Capsule Formulations
2.3.1.3 Other Formulations
2.3.2 Neural Computing
2.3.2.1 Tablet Formulations
2.3.2.2 Topical Formulations
2.3.2.3 Other Formulations
2.3.3 Computer Simulations
2.4 Three-Dimensional (3D) Printing
2.4.1 Emergence of Main Techniques of 3D Printing: The 1980s
2.4.2 Evolution of 3D Printer Manufacturer and Modeling Tool: The 1990s
2.4.3 Revolutionary Developments in 3D Printing: The 2000s
2.4.4 Entry of 3D Printing in Drug Development: The 2010s
2.4.5 Advancement of 3D Printing: 2020s
2.5 Computer-Aided Pharmacokinetics
2.6 A Brief History on Robotic Developments
2.6.1 1950-1959: Birth of First Industrial Robot and Numerical Control System
2.6.2 1960-1969: Evolution of Industrial Robots with More Degrees of Freedom and Robotic Grippers
2.6.3 1970-1979: Development of Robots for Special Purposes
2.6.4 1980-1989: Growth of Modern Industrial Robots
2.6.5 1990-1999: The Decade of Fastest Industrial Robots and Actuators
2.6.6 2000-2009: The Birth of Collaborative Robots and Humanoids and Evolution of Bioinspired Robots
2.6.7 2010-Present: The Rise of Collaborative Robots, Humanoids, and Soft Robots
2.7 Conclusion
References
3: Computer-Aided Formulation Development
3.1 Introduction
3.2 The Concept of Optimization and Optimization Parameters
3.3 Statistical Experimental Designs
3.3.1 Screening Designs
3.3.1.1 Taguchi Designs
3.3.1.2 Plackett-Burman Designs
3.3.2 Full Factorial and Fractional Factorial Designs
3.3.3 Response Surface Designs
3.3.3.1 Three-Level Factorial Design
3.3.3.2 Central Composite Designs
3.3.3.3 The Box-Behnken Design
3.3.3.4 The Doehlert Design
3.3.3.5 The D-Optimal Design
3.3.4 Mixture Designs
3.4 Computer-Aided Pharmaceutical Formulation
3.4.1 Development of Pharmaceutical Emulsions
3.4.2 Development of Pharmaceutical Microemulsions
3.5 Conclusions
References
4: Quality by Design in Pharmaceutical Development
4.1 Introduction
Box 4.1 Advantages of QbD Implementation
4.2 International Council for Harmonization (ICH) of Technical Requirements of Pharmaceuticals for Human Use Guidelines
4.2.1 ICH Q8 (R2)
4.2.2 ICH Q9
4.2.3 ICH Q10
4.2.4 ICH Q12
4.3 Design of Experiment (DoE)
4.3.1 Design of Experiment Plan
4.3.2 Methodologies in Optimization
4.3.3 Simultaneous Optimization
4.3.4 Sequential Optimization
4.4 QbD in Formulation Development
4.4.1 Quality Target Product Profile
4.4.2 Critical Quality Attributes
4.4.3 Critical Process Parameters
4.4.4 Critical Manufacturing Parameters
4.4.5 Unclassified Process Parameters
4.4.6 Control Strategy
4.4.7 Design Space Verification
4.4.8 Real-Time Release Testing
4.4.9 Normal Operating Range
4.4.10 Proven Acceptable Range
4.4.11 Product/Process Design and Understanding
4.4.12 Post-approval Lifecycle Management
Box 4.2 Elements of PALM
4.5 RegulatorΒ΄s Views on QbD
4.5.1 FDA ONDQAΒ΄s CMC (Office of New Drug Quality Assessment; Chemistry, Manufacturing and Control) Pilot Program
4.5.2 EMAΒ΄s Perspective
4.5.3 Pharmaceuticals and Medical Devices Agency (PMDA) Perspective
4.5.4 Health Canada (HC) Perspective
4.6 Reflections of QbD on the Industry
4.7 QbD Software
Box 4.3 Summary of fusion QbD software
Box 4.4 Summary of JMP QbD Software
Box 4.5 Summary of Lean QbD Software
4.8 Scientific Examples of QbD (Case Studies)
4.8.1 5-Fluorouracil (5-FU)-Loaded Thermosensitive Hydrogel
4.8.2 Naproxen Enteric-Coated Pellets
4.9 Conclusion
References
5: Teaching Principles of DoE as an Element of QbD for Pharmacy Students
5.1 Introduction
5.2 The Fishbone Diagram as a Means of Delivering the Ideas of DoE
5.3 Types of Experimental Designs
5.4 D-Optimal Designs
5.5 Step-by-Step Protocol to Handle a Drug Delivery Formulation Case Using Design Expert
5.5.1 Design
5.5.2 Analysis
5.5.3 Diagnostics
5.5.4 Optimization
5.5.5 Model Graphs
5.5.6 Prediction
5.6 Conclusions and Future Prospects
References
6: Computer-Assisted Manufacturing of Medicines
6.1 Introduction
6.2 Computer-Assisted Dosage Form Manufacturing
6.3 History of 3D Printing
6.4 Basic Components of 3D Printing
6.5 Three-Dimensional Printing Processing Steps
6.6 3D Printing Software
6.6.1 MakerBot Print Software
6.7 Various 3D Techniques Used to Fabricate DDS
6.7.1 Stereolithographic 3D Printing (SLA)
6.7.2 Fused Filament Fabrication/Fused Deposition Modelling (FDM) 3D Printing
6.7.3 Binder Deposition
6.7.4 Selective Laser Sintering (SLS) 3D Printing
6.7.5 Digital Light Processing (DLP)
6.7.6 Inkjet Printing
6.7.7 Pressure-Assisted Microsyringe (PAM)
6.7.8 Embedded 3D Printing
6.7.9 Stencil Printing
6.8 Advantages, Limitations, and Challenges of 3DP
6.9 Applications of 3D Printing Technology
6.9.1 In Medicine and Dentistry
6.9.2 3D Printed Drugs
6.9.3 New Geometries and Designs
6.10 Regulatory Concerns
6.11 Future of 3D Printing Technology in the Pharmaceutical Industry
6.12 Risks and Challenges in 3D Printing
6.12.1 Product Liability Risk
6.12.2 Cyber Risk
6.12.3 Safety and Efficacy of 3D Printers
6.13 Summary
6.14 Credible Online Resources for Further Reading
References
7: Computer-Aided Biopharmaceutical Characterization: Gastrointestinal Absorption Simulation and In Silico Computational Model...
7.1 Introduction
7.2 Biopharmaceutical Characterization: Theoretical Background
7.3 Gastrointestinal Absorption Modeling and Simulation
7.4 Mechanistic Approaches for Predicting Oral Drug Absorption
7.4.1 Compartmental Absorption and Transit (CAT) Model
7.4.2 Grass Model
7.4.3 GI Transit Absorption (GITA) Model
7.4.4 Advanced Dissolution, Absorption, and Metabolism (ADAM) Model
7.4.5 Advanced Compartmental Absorption and Transit (ACAT) Model
7.5 Exemplary Demonstration of ACAT Model Integrated GastroPlus Software
7.5.1 Model Construction
7.5.2 Model Exploration
7.5.2.1 Parameter Sensitivity Analysis
7.5.2.2 Virtual Trial
7.5.2.3 Prediction of Food Effects (Fed vs. Fasting State)
7.5.2.4 In Vitro Dissolution and In Vitro-In Vivo Correlation
7.5.2.5 Biowaiver Consideration
7.6 Conclusion
7.7 Credible Online Resources for Further Reading
References
8: Computer Simulation and Modeling in Pharmacokinetics and Pharmacodynamics
8.1 Introduction
8.2 Computational Modeling and Simulation
8.2.1 Methods of Biomolecular Simulation
8.3 Multiscale Modeling and Simulation Studies
8.3.1 Pharmacokinetic and Pharmacodynamic Modeling
8.3.2 Physiologically Based Pharmacokinetic (PBPK) Model
8.3.3 Pharmacodynamic Simulation
8.3.3.1 Quantitative Structure-Pharmacokinetic Parameters (QSPkRs)
8.4 Structural Components of the Physiologically Based Pharmacokinetic (PBPK) Model
8.4.1 PBPK Modeling Simulation Software
8.5 Physiome Project
8.5.1 Cardiovascular Modeling
8.5.2 Breast Cancer Modeling (HAMAM)
8.6 Isolated Tissues and Organs
8.7 Cells
8.7.1 CellModeller
8.7.2 The Cellular Potts Model
8.7.3 Chaste
8.7.4 Agent-Based Modeling (ABM)
8.7.5 Steered Molecular Dynamics (SMD) Simulation
8.8 Proteins and Genes
8.9 Future Scope and Direction
8.10 Credible Online Resources for Further Reading
References
9: Physiologically Based Pharmacokinetic (PBPK) Modelling
9.1 Introduction
9.2 History of PBPK Modelling
9.3 Regulatory Considerations
9.4 Applications of PBPK Modelling
9.4.1 Early Stage Drug Development
9.4.2 Cross-Species Extrapolation and First-In-Human (FIH) Dose Predictions
9.4.3 Formulation Development and Optimization
9.4.4 Prediction of Drug-Drug Interactions
9.4.5 Prediction of the Effect of Age
9.4.6 Prediction of Genetic Effects
9.4.7 Prediction of the Effects of Disease
9.4.8 Assessment of the Food Effect
9.4.9 Other Applications
9.5 Components of a PBPK Model
9.5.1 PBPK Model Basic Structure
9.5.2 System-Related Input Parameters
9.5.3 Drug-Specific Input Parameters
9.5.4 Other Miscellaneous Properties
9.6 Approaches for PBPK Model Development
9.6.1 Bottom-Up Approach
9.6.2 Top-Down Approach
9.6.3 Middle-Out Approach
9.7 Best Practices for PBPK Model Building
9.7.1 Compilation of Available Data and Information
9.7.2 Establishment of the Intravenous Disposition Model
9.7.3 Establishment of Oral Absorption Model
9.7.4 Overall Model Evaluation
9.7.4.1 Concurrence of Modelling Outcome with the Experimental Data
9.7.4.2 Comparison of Typical PK Parameters like Cmax, Tmax, AUC and T Between Simulated Results Obtained by the Model and the...
9.7.5 Sensitivity Analysis or Best/Worst Case Scenarios
9.8 Software Employed for the Modelling
9.8.1 Custom Physiologically Based Pharmacokinetics (PBPK) Software
9.8.2 General Purpose High-Level Scientific Computing Software
9.8.3 Bio-Mathematical Modelling Software
9.9 Understanding Limitations of PBPK Modelling
9.9.1 Inaccurate and Inconsistent Core Information
9.9.2 Absence of Key Data
9.9.3 Non-availability of Selective Substrates and Inhibitors
9.9.4 Absence of Knowledge of Basic Mechanisms in Certain Instances
9.9.5 Limited Utility in the Prediction of Disposition of Therapeutic Proteins
9.10 Conclusion
9.11 Credible Online Resources for Further Reading
References
10: Computers in Clinical Development
10.1 Introduction
10.2 Clinical Trial Management System
10.2.1 Registries
10.2.2 Electronic Health Records (EHR)
10.2.3 Informed Consent
10.2.4 Clinical Data Management Systems (CDMS)
10.2.5 Electronic Case Report Forms (e-CRF)
10.2.6 Pharmacovigilance/Drug Safety Systems
10.2.7 Electronic Data Capture (EDC)
10.3 E-Technologies Used in Clinical Trials for Data Management
10.3.1 Softwares Used in Clinical Data Management
10.3.2 Digital Wearable Medical Health Devices
10.3.3 Health Apps
10.4 Big Data in Clinical Trial
10.5 Artificial Intelligence (AI) in Clinical Trial
10.6 Data Collection and Data Management
10.7 Advantages of E-Technologies in Clinical Trials
10.8 Challenges and Limitations of E-Technologies in Clinical Trials
10.9 Future Directions
References
11: Artificial Intelligence and Its Applications in Drug Discovery, Formulation Development, and Healthcare
11.1 Introduction
11.2 A Brief History of Artificial Intelligence
11.3 Artificial Intelligence
11.3.1 Machine Learning
11.3.1.1 Supervised ML
11.3.1.2 Unsupervised ML
11.3.1.3 Semi-supervised ML
11.3.1.4 Reinforcement ML
11.3.2 Artificial Neural Network and Deep Learning
11.3.2.1 Feed Forward Neural Networks and Multiple Layered Perceptron
11.3.2.2 Recurrent Neural Network
11.3.2.3 Convolutional Neural Network
11.3.2.4 Auto-Encoder
11.3.2.5 Generative Adversarial Networks
11.4 AI in Drug Discovery and Development
11.4.1 Drug Screening
11.4.2 Predicting Toxicity
11.4.3 Drug Repurposing
11.4.4 Polypharmacology and Drug-Drug Interactions
11.4.5 Clinical Trials
11.4.6 Pharmacokinetics
11.5 AI in Formulation Development and Pharmaceutical Manufacturing
11.5.1 ANN Modeling
Box 11.1 Advantages and Disadvantages of ANN
11.5.1.1 Collection and Labeling of Data
11.5.1.2 Cleaning of the Data
11.5.1.3 Selection of Algorithm
11.5.1.4 Data Grouping
11.5.1.5 Training
11.5.1.6 Postprocessing and Finalization of Model
11.5.1.7 Prediction with Developed Model
11.5.2 Preformulation Studies
11.5.3 Expert Systems for Preformulation and Formulation Designing
11.5.4 ANN for Formulation Development and Optimization
11.5.5 Production Technology
11.6 Healthcare Applications
11.6.1 Personalized Treatment
11.6.2 Medical Diagnosis
11.6.3 Epidemic Surveillance and Forecasting
11.7 Some Proprietary AI Technologies and Their Collaborations
11.7.1 Drug Discovery and Development
11.7.2 Formulation Development and Manufacturing
11.7.3 Healthcare
11.8 Some Challenges
11.9 Conclusion
References
12: Robotic Automation of Pharmaceutical and Life Science Industries
12.1 Introduction
12.2 Types of Robots
12.2.1 Parallel (Delta) Robots
12.2.2 SCARA Robots
12.2.3 Articulated Robots
12.2.4 Cartesian/Linear/Gantry Robots
12.2.5 Cylindrical Robots
12.2.6 Collaborative Robots
12.2.7 Autonomous Mobile Robots (AMRs) and Autonomous Guided Vehicles (AGVs)
12.2.8 Exoskeletons/Human-Robot-Hybrids
12.2.9 Humanoid Robots
12.3 Robots in Pharmaceutical Industries
Box 12.1 Advantages and disadvantages of robots
Box 12.2 Applications of robots in pharmaceutical manufacturing, packaging, and warehousing and laboratories
12.4 Automation of Pharmaceutical Manufacturing
12.4.1 3D Printing of Dosage Forms
12.4.2 Personalized Medicines
12.5 Automation of Parenteral Manufacturing and Aseptic Processes
12.5.1 Pharmaceutical Clean Room Robots
12.5.2 Aseptic Isolators/Restricted Access Barrier Systems
12.5.3 Robotic Fill/Finish Systems
12.5.4 Radiopharmaceuticals
12.5.5 Mobile Autonomous Robots for Cleanroom Monitoring
12.6 Laboratory Automation
12.6.1 YuMi Robot
12.6.2 The Robotic Cloud Lab
12.7 Automation of Pharmaceutical Packaging Operations
12.7.1 Robotic Picking of Container Products
12.7.1.1 FANUC Delta Robots for Packaging Operations
12.7.1.2 ABBΒ΄s IRB 360 (FlexPicker)
12.7.1.3 IRB 390 FlexPacker
12.7.2 Robotic Palletizing
12.7.2.1 SERPA Pelletizers Equipped with FANUC Robots
12.7.2.2 AstraZeneca Case Study
12.7.2.3 URIO: A Collaborative Robotic Pelletizer
12.8 Warehouse Automation
12.8.1 MiR200
12.8.2 Mini
12.8.3 Omron LD/HD Mobile Robots
12.8.4 Yujin YRL3 Series
12.8.5 Squid
12.9 Conclusion
12.10 Credible Online Resources for Further Reading
References
13: Soft Robots for the Delivery of Drugs
13.1 Introduction
13.2 Robotics in Drug Delivery
13.2.1 Origami Robots
13.2.2 Sperm Robots
13.2.3 Soft Multi-legged Robots
13.2.4 Robotic Capsules
13.2.5 Nanorobots
13.3 Robotic Innovations Under Clinical Trial
13.3.1 RaniPill
13.4 Challenges and Future Prospects
13.5 Conclusion
References
14: Online Literature Searching for Research Projects in Pharmaceutical Sciences
14.1 Introduction
14.2 Literature
14.2.1 Primary Literature
14.2.2 Secondary Literature
14.2.3 Tertiary Literature
14.3 Significant Literature Sources
14.3.1 Databases
14.3.2 Online Digital Library Platforms
14.3.3 Online Journal Databases/Platforms/Search Engines
14.3.4 Patent Databases
14.3.5 Online Clinical Trial Registries
14.3.6 Drug Regulatory Agencies and Other International Organizations
14.3.7 Institutional Repositories
14.3.8 Miscellaneous Categories
14.4 Literature Search Methods
14.4.1 Bibliographic Electronic Databases
14.4.2 Searching Supplementary Literature
14.4.2.1 Searching Clinical Trial Registers
14.4.2.2 Searching Patents and Patent Applications
14.4.2.3 Searching Institutional Repositories for Thesis/Dissertations and Other Contents
14.4.2.4 Searching Internet Sources/Web Searching
14.4.3 Scanning Reference Lists/Bibliography of Key Papers
14.4.4 Citation Searching of Relevant Studies
14.4.5 Contacting Study Authors/Experts/Manufacturers
14.4.6 Hand Searching of Pertinent Journals
14.5 Electronic Searching Guidance for Bibliographic Databases
14.5.1 Step 1: A Well-Focused Research Problem
14.5.2 Step 2: Identify Search Terms or Search Words or Keywords
14.5.3 Step 3: Find Synonyms for Keywords
14.5.4 Step 4: Searching Literature with Keywords
14.5.4.1 Connect Keywords with Boolean Operators
14.5.4.2 Building Search String
14.5.5 Some Search Tips
14.5.5.1 Truncation Symbols
14.5.5.2 Use of Wildcards
14.5.5.3 Exact Match or Phrase Searching
14.5.5.4 Proximity Operators
14.5.6 Step 5: Refining Search Results
14.5.7 Step 6: Adapt and Track Search Results
14.5.8 Step 7: Reference Managing
14.6 Conclusion
References
15: Patent Searching
15.1 Introduction
15.2 What Is Patent?
15.3 Why Search Patents?
15.4 Patent Classification System
15.4.1 International Patent Classification
15.4.2 United States Patent Classification (USPC)
15.4.3 European Classification
15.4.4 Cooperative Patent Classification
15.5 Patent Search Type
15.5.1 State-of-the-Art Patent Search (Evidence of Use Search)
15.5.2 Novelty (Patentability)
15.5.3 Freedom to Operate (Infringement, Right to Use, Clearance)
15.5.4 Opposition Search
15.5.5 Validity
15.5.6 Due Diligence (Patent Portfolio Analysis)
15.6 Strategies for Query Construction
15.6.1 Boolean Operators
15.6.2 Proximity Operators
15.6.3 Wildcards
15.6.4 Parentheses and Nesting
15.6.5 Phrases
15.7 Information Sources
15.7.1 Google Patents
15.7.2 Espacenet
15.7.3 PatentScope
15.7.4 USPTO
15.7.5 Indian Patent Advanced Search System (InPASS)
15.7.6 Japan Platform for Patent Information (J-PlatPat)
15.7.7 Derwent World Patent Index (DWPI)
15.7.8 Delphion
15.7.9 Questel
15.7.10 WIPS Global
15.7.11 Miscellaneous Sources
15.8 Conclusion
References
16: Computer Aided Drug Design
16.1 Introduction
16.2 Structure-Based Drug Design
16.2.1 Molecular Modelling
16.2.1.1 Strategies for Molecular Modelling
Direct Drug Designing
Indirect Drug Designing
16.2.1.2 Molecular Modelling Methods
Molecular Mechanics (MM)
Quantum Mechanics (QM)
Quantum Mechanical Methods
16.2.1.3 Energy Minimization Methods
16.2.2 High-Throughput Screening, Virtual Screening, Docking
16.2.2.1 Virtual Screening
16.2.2.2 Docking
16.2.2.3 Types of Docking
16.2.3 De Novo Drug Design
16.2.4 Homology Modelling
16.3 Ligand-Based Drug Design
16.3.1 QSAR and Historical Development of QSAR
16.3.2 Two-Dimensional and Three-Dimensional QSAR
16.3.2.1 Two-Dimensional QSAR
Statistical Methods
16.3.2.2 Three-Dimensional QSAR
Computational Molecular Field Analysis (CoMFA)
Comparative Molecular Indices Analysis (CoMSIA)
16.4 List of Software
16.5 Applications of CADD
16.6 Drugs Developed Using CADD
16.6.1 Captopril (Capoten, Bristol Myers-Squibb)
16.6.2 Dorzolamide (Trusopt, Merck)
16.6.3 Saquinavir (Invirase, Hoffmann-La Roche)
16.6.4 Aliskiren (Tekturna, Novartis)
16.6.5 TMI-005 (Apratastat, Wyeth Research)
16.6.6 Rupintrivir (AG7088, Agouron)
16.7 Conclusion
16.8 Credible Online Resources for Further Reading
References
17: Quantitative Structure-Property Relationship (QSPR) Modeling Applications in Formulation Development
17.1 Introduction
17.2 Development of QSPR Models
17.3 Various Tools for Performing QSPR/QSAR Studies
17.4 Applications of QSPR Modeling in Formulation Development
17.4.1 Aqueous Solubility
17.4.2 Hydrophobicity and Partition Coefficients
17.5 Conclusion
References
18: Modelling Approaches for Studies of Drug-Polymer Interactions in Drug Delivery Systems
18.1 Introduction
18.2 Polymers as Drug Carriers
18.2.1 Linear Polymers
18.2.2 Hyperbranched Polymers
18.3 Molecular Modelling of Drugs and Polymers
18.3.1 Structure Generation
18.3.2 Small Molecules
Box 18.1 List of Structure Drawing Software and Molecular Editors
18.3.3 Linear Polymers
18.3.4 Hyperbranched Polymers and Dendrimers
18.3.5 Molecular Dynamics Simulation of Polymers
Box 18.2 List of Molecular Docking and Molecular Dynamics Simulation Software
18.4 In Silico Evaluation of Intermolecular Interactions
18.4.1 Molecular Docking
18.4.2 Molecular Dynamics Simulations
18.5 Conclusions and Future Prospects
18.6 Credible Online Resources for Further Reading
References
19: Computers in Pharmaceutical Analysis
19.1 Introduction
19.2 Automation and Computer-Aided Analysis
19.3 Computer-Assisted Analysis of Drug Delivery Systems
19.4 Chromatographic Data Systems
19.4.1 Data Integrity
19.4.2 Workflow Automation
19.5 Computer-/Software-Assisted Analytical Method Development
19.6 Analytical QbD
19.6.1 Applications of QbD Analytical Process
19.6.1.1 In Validation and Development of Ultrahigh Performance Liquid Chromatography (UHPLC)
19.6.1.2 In Hydrophilic Interaction Liquid Chromatography (HILIC) Development
19.6.1.3 In Chromatography Column Screening
19.6.1.4 In the Development of HPLC Methods for Drug Products/Substances
19.6.1.5 Response Surface Methodology (RSM)
19.6.1.6 RSMΒ΄s Fundamentals and Theoretical Aspects
19.6.1.7 Application of RSM
19.7 Software for Dissolution/Drug Release Analysis
19.8 Nanoparticle Tracking Analysis (NTA)
19.9 Conclusions
19.10 Credible Online Resources for Further Reading
References
20: Telemedicine
20.1 Introduction
20.2 Telemedicine Definition
20.3 Telemedicine History
20.4 Telemedicine Services
20.4.1 Teleconsultation
20.4.1.1 Store and Forward
20.4.1.2 Real-Time Video Consultations
20.4.2 Remote Patient Monitoring (RPM)
20.4.3 Telesurgery
20.4.4 Telepharmacy
20.4.4.1 Modern ICTs and Software in Telepharmacy
20.4.4.2 Artificial Intelligence and Telepharmacy
20.5 Telemedicine in Emergencies
20.6 The Future of Telemedicine
20.7 Conclusion
20.8 Credible Online Resources for Further Reading
References
21: Bioinformatics in Drug Design and Delivery
21.1 Introduction
21.2 Software Used in Bioinformatics
21.3 Structural Bioinformatics
21.3.1 Protein Structure Basis
21.3.2 Protein Structure Visualization, Comparison, and Classification
21.3.3 Protein Secondary and Tertiary Structure Prediction
21.3.4 RNA Structure Prediction
21.4 Genomics and Proteomics
21.4.1 Genome Mapping, Assembly, and Comparison
21.4.2 Functional Genomics
21.4.3 Proteomics
21.4.4 Pharmacogenomics
21.4.5 Ribosome Profiling and Its Applications
21.5 Transcriptomics Analysis
21.5.1 Aim and Scope
21.5.2 Hybridization-Based Approaches
21.5.3 Sequence-Based Approaches
21.5.4 Microarray Chips and Application
21.5.5 Next-Generation Sequencing (NGS)
21.6 Bioinformatics in Treatment
21.6.1 Pathogen Identification and Strain Typing
21.6.2 Antimicrobial Resistance
21.7 Molecular Phylogenetics
21.7.1 Phylogenetics Basics
21.7.2 Multiple Sequence Alignment
21.7.3 Phylogenetic Tree Construction
21.7.4 Bootstrapping and Jackknifing
21.8 Personalized Medication and Cost Reduction
21.8.1 Virtual-High-Throughput Screening (v-HTS)
21.8.2 Dosage Regimen to Reduce Treatment Time
21.8.3 Adverse Drug Reaction
21.8.4 Drug Target Validation
21.9 Conclusion
References
22: Statistical Modeling Techniques
22.1 Introduction
22.2 Univariate Statistical Analysis
22.3 Multivariate Analysis (MVA)
22.4 Principal Component Analysis
22.5 Support Vector Machines
22.5.1 Applications
22.6 Probabilistic Modeling
22.7 Partial Least Squares (PLS)
22.8 Some Statistical Softwares
22.9 Conclusions and Future Prospects
References
23: Molecular Modeling of Nanoparticles
23.1 Introduction
23.2 Nanoparticles
23.3 Molecular Modeling of Nanoparticles
23.3.1 MD (Full Atomistic Models)
23.3.1.1 Basic Steps in MD Simulation Studies
23.3.2 Enhanced Sampling Method
23.3.2.1 Replica-Exchange MD (REMD)
23.3.2.2 Metadynamics
23.3.3 Coarse-Grained Models
23.3.3.1 Dissipative Particle Dynamics
23.3.4 Comparison of Models for Nanomaterial-Biology Interactions
23.3.5 Solvent Model
23.4 The Software Used in Molecular Modeling
23.5 Applications of Molecular Modeling for Nanoparticles
23.5.1 Docking Analysis of Nanoparticles
23.6 Conclusion
23.7 Credible Online Resources for Further Reading
References
24: Pharmaceutics Informatics: Bio/Chemoinformatics in Drug Delivery
24.1 Introduction
24.2 Bio/Chemoinformatics Tools in Detecting the Drug Loading in Lipidic and Polymeric Nanoparticulate Matrices
24.3 Bio/Chemoinformatics Tools in Selecting the Optimum Oil for Drugs Solubilization in Microemulsion Systems
24.4 Bio/Chemoinformatics in Selecting the Optimum Protein Carrier for Polyphenolic Drugs
24.5 Bio/Chemoinformatics in Comparing the Biopharmaceutical Behaviour of Curcuminoids in AlzheimerΒ΄s Disease
24.6 Bio/Chemoinformatics Tools in Selecting the Optimum Natural Bio-macromolecular Carrier of Doxorubicin
24.7 Bio/Chemoinformatics for Re-purposing and Re-formulation of an Old Molecule in Order to Combat COVID-19
24.8 Conclusion
24.9 Credible Online Resources for Further Reading
References
25: Computer-Aided Development and Testing of Human Extra-Thoracic Airway Models for Inhalation Drug Delivery
25.1 Introduction
25.1.1 Significance of Airway Models in Pharmaceutical Development
25.1.2 Previous Works
25.2 Methods
25.2.1 Computer-Aided Development of Airway Models
25.2.1.1 Mouth-Throat Model
25.2.1.2 Nasal Model
25.2.1.3 Lung Model
25.2.2 Experimental Setup
25.2.3 Computational Fluid-Particle Governing Equations
25.2.3.1 Magnetophoretic Force
25.2.3.2 Acoustophoretic Force
25.2.4 Numerical Methods
25.2.4.1 ANSYS Fluent and ICEM CFD
25.2.4.2 COMSOL Multiphysics
25.2.5 Statistical Analysis
25.3 Applications
25.3.1 Model Validation
25.3.2 Mouth-Throat Model Development and Testing
25.3.3 Pulmonary Drug Delivery
25.3.3.1 Probability Analysis
25.3.3.2 Deposition Visualization: CFD Versus Experiments
25.3.3.3 Deposition Visualization: Normal and Diseased Breathing Conditions
25.3.3.4 Statistical Shape Modeling for Lung Morphing
25.3.4 Nasal Drug Delivery
25.3.4.1 Deposition Visualization for Nasal Sprays and Nebulizers
25.3.4.2 Normal Versus Bidirectional Nasal Drug Delivery
25.3.4.3 Nasal Passage Dilation Effects
25.3.4.4 Targeted Olfactory Delivery Using Magnetic Guidance
25.3.4.5 Pulsating Aerosol Delivery to Maxillary Sinuses
25.3.5 Computational Fluid-Particle Dynamics for COVID-19: Effect of Mask-Wearing
25.4 Conclusion
25.5 Credible Online Resources for Further Reading
References