Drug Delivery Systems using Quantum Computing

Name: Drug Delivery Systems using Quantum Computing
Brand: Wiley-Scrivener
Price: 173.99 EUR
Availability: OnlineOnly

Rishabha Malviya Sonali Sundram Dhanalekshmi Unnikrishnan Meenakshi(Herausgeber*in)

Wiley-Scrivener (Verlag)

1. Auflage

Erschienen am 13. Juni 2024

480 Seiten

E-Book

PDF mit Adobe-DRM

Systemvoraussetzungen

978-1-394-15932-1 (ISBN)

173,99 €inkl. 7% MwSt.

Systemvoraussetzungen

für PDF mit Adobe-DRM

E-Book Einzellizenz

Als Download verfügbar

Beschreibung

The first book of its kind to show the potential of quantum computing in drug delivery.

Drug delivery systems (DDS) are defined as methods by which drugs are delivered to desired tissues, organs, cells, and subcellular organs for drug release and absorption through a variety of drug carriers. By controlling the precise level and/or location of a given drug in the body, side effects are reduced, doses are lowered, and new therapies are possible. Nevertheless, there are still significant obstacles to delivering certain medications to particular cells. Drug delivery methods change pharmacokinetic, pharmacodynamic, and drug release patterns to enhance product efficacy and safety, as well as patient convenience and compliance. Computational approaches in drug development enable quick screening of a vast chemical library and identification of possible binders by using modeling, simulation, and visualization tools. Quantum computing (QC) is a fundamentally new computing paradigm based on quantum mechanics rules that enables certain computations to be conducted significantly more rapidly and effectively than regular computing, and hence this has huge promise for the pharmaceutical sector.

Significant advances in computational simulation are making it easier to comprehend the process of drug delivery. This book explores an important biophysical component of DDSs, and how computer modeling may help with the logical design of DDSs with enhanced and optimized characteristics. The book concentrates on computational research for various important types of nanocarriers, including dendrimers and dendrons, polymers, peptides, nucleic acids, lipids, carbon-based DDSs, and gold nanoparticles.

Audience

Researchers and industry scientists working in clinical research and disease management; pharmacists, formulation and pharmaceutical scientists working in R&D; computer science engineers applying deep learning and quantum computing in healthcare.

Rishabha Malviya, PhD, is an associate professor in the Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University. He has authored more than 150 research/review papers for national/international journals of repute. He has been granted more than 10 patents from different countries while a further 40 patents were either published or are under evaluation. His areas of interest include formulation optimization, nanoformulation, targeted drug delivery, localized drug delivery, and characterization of natural polymers as pharmaceutical excipients.

Sonali Sundram, PhD, MPharm, completed her doctorate in pharmacy and is currently working at Galgotias University, Greater Noida. Her areas of interest are neurodegeneration, clinical research, and artificial intelligence. She has edited 6 books, been granted 8 patents, and has organized more than 15 national and international seminars/conferences/workshops.

Dhanalekshmi Unnikrishnan Meenakshi, PhD, holds a doctorate in Pharmacology from the Council of Scientific and Industrial Research (CSIR)- CLRI, India. She is a faculty member in the College of Pharmacy, National University of Science and Technology, Muscat, Sultanate of Oman. She has published extensively in nanomedicine, drug delivery, and formulation technology in peer-reviewed reputed journals and books and works on an array of projects relating to cancer and gene therapy, nanotechnology, and pharmacology.

Weitere Details

Weitere Ausgaben

Inhalt

Cover
Series Page
Title Page
Copyright Page
Dedication Page
Contents
Foreword
Preface
Acknowledgments
Chapter 1 Quantum Computational Concepts and Approaches in Drug Discovery, Development and Delivery
1.1 Introduction
1.2 Algorithms and QC in Pharma
1.2.1 Algorithms
1.2.2 Supervised Learning
1.2.3 Unsupervised Learning
1.2.4 Multi-Task Neural Networks
1.2.5 Graph Convolution
1.3 Potential of QC in Drug Discovery
1.3.1 Target Recognition and Validation
1.3.2 Production and Validation of Hits
1.3.3 Lead Optimization
1.3.4 Clinical Trials
1.3.5 Linking and Generating Data
1.4 QC and Drug Delivery
1.5 QC in Drug Delivery Modalities
1.5.1 Computational Approach Towards Nano Particulate Drug Delivery
1.5.2 Computational Approach for Bone Drug Delivery
1.5.3 Computational Approach for Polymeric Drug Delivery
1.5.4 Computational Approach for Microsphere Drug Delivery
1.5.5 Computational Approach for Dendrimer-Based Drug Delivery
1.5.6 Computational Approach for Carbon Nanotube-Based Drug Delivery
1.6 QC Applications in Pharma Industry
1.7 Challenges or Prospects
1.8 Conclusion
References
Chapter 2 Quantum-Enabled Drug Discovery Process
2.1 Introduction
2.2 Usual Challenges in Drug Designing and Discovery
2.2.1 Commercial Challenge
2.2.2 Modification or Transitional Challenges
2.2.3 Unexplored Areas Under Classical Computational Techniques
2.3 Medicinal Chemistry Through Quantum Mechanics
2.3.1 Underappreciation of Chemical Interactions in Protein-Ligand Complexes
2.3.2 Non-Classical Hydrogen Bonding
2.3.3 p-p Stacking
2.3.4 Hydrophobic Bond Interactions
2.3.5 Coordination from Water
2.3.6 Explicit Water
2.3.7 Implicit Water
2.4 Interaction Analysis
2.4.1 Fragment Interaction Energy
2.4.2 Binding Free Energy
2.4.3 Fragment Molecular Orbital (FMO) Process and Analysis
2.5 Geometric Optimization
2.5.1 Analyzing Gradients
2.6 GAMESS: A Computational Technique for Biochemical Simulations
2.6.1 Introduction to Biochemical Simulations
2.6.2 Parametrizing Quantum Mechanics Method for Simulations
2.6.3 Quantum Mechanics and Molecular Mechanics Associated with GAMESS
2.6.4 Introduction to QuanPol
2.6.5 QuanPol: Covalent Boundary Treatment
2.6.6 GO and Harmonic Vibration Frequency
2.6.7 Molecular Dynamics Simulation
2.6.8 Free Energy Perturbation (Deviation of Energy) Simulation
2.6.8.1 QuanPol Process Umbrella Sampling
2.6.8.2 QuanPol Process Thermodynamic Integration (TI)
2.6.9 Setting Up Valuation and Calculation
2.6.10 Molecular Modeling and Visualization Software
2.7 Conclusion
References
Chapter 3 Quantum Computing and Its Promise in Drug Discovery
3.1 Introduction
3.1.1 Need for Quantum Computing
3.2 Quantum Computing's Types, Applications, Generality, and Power
3.2.1 Quantum Annealer
3.2.2 Analog Quantum
3.2.3 Universal Quantum
3.3 Drug Discovery and Quantum Computing
3.3.1 A Brief History of Drug Discovery
3.3.2 Modern Drug Discovery
3.4 Role of Quantum Computing in Drug Discovery
3.5 Quantum Computing Methodology in Drug Discovery
3.5.1 Target Identification and Validation
3.5.2 Hit Generation and Validation
3.5.3 Lead Optimization
3.5.4 Data Linkage and Generation
3.5.5 Clinical Trials
3.5.6 Molecular Formations
3.6 Examples of Companies Using Quantum Theory to Accelerate Drug Discovery
3.6.1 Aqemia
3.6.2 Hafnium Labs
3.6.3 Kuano
3.6.4 Menten AI
3.6.5 Pharmacelera
3.6.6 PharmCADD
3.6.7 Polaris Quantum Biotech
3.6.8 ProteinQure
3.6.9 Riverlane
3.6.10 Roivant Discovery
3.6.11 XtalPi
3.6.12 Zapata Computing
3.7 Advantages of Quantum Computing
3.8 Applications of Quantum Computing in Drug Discovery and Development
3.8.1 A Future View of QC and Drug Discovery
3.8.2 Current Developments in QC and Drug Discovery
3.9 Conclusion
References
Chapter 4 Exploring Nano-Based Therapeutics by Quantum Computational Modeling
4.1 Introduction to Nano-Based Therapeutics
4.2 Introduction to Quantum Computational Modeling with Respect to Nano-Based Therapeutics
4.2.1 Particle-Based Models
4.2.2 Continuum-Based Models
4.3 Exploration of Nano-Based Therapeutics
4.4 Design and Development of Nano-Based Therapeutics
4.4.1 Prediction of Solubility
4.4.2 Prediction of Permeability
4.4.3 Selection of Components and Optimization of Formulation of Nano-Based Therapeutics
4.4.3.1 Prediction of Therapeutic Loading
4.4.4 Prediction of Therapeutic Release or Leakage
4.4.5 Prediction of the Pharmacokinetic Profile of Nano-Based Therapeutics
4.4.5.1 Prediction of Absorption
4.4.5.2 Prediction of Distribution (Protein Corona Formation)
4.4.5.3 Prediction of Metabolism
4.4.5.4 Prediction of Excretion
4.4.6 Understanding Protein Corona Formation
4.4.7 Understanding the Interaction of Nanosized Carrying Objects with Bio-Membranes
4.4.8 Prediction of the Pharmacodynamic Profile of Nano-Based Therapeutics
4.4.9 Prediction of Adverse Drug Reactions and Nanotoxicity
4.4.10 Design of Target-Oriented Nano-Based Therapeutics
4.5 Conclusion
References
Chapter 5 Application of Quantum Computational Simulation in Drug Delivery Strategies with Carbon Nanotubes
5.1 Introduction
5.2 Properties of CNTs
5.2.1 Electrical Properties of CNTs
5.2.2 Elastro-Mechanical Properties of CNTs
5.2.3 Thermal Properties of CNTs
5.2.4 Optical Properties of CNTs
5.3 Functionalization of CNTs
5.3.1 Covalent Functionalization
5.3.2 Noncovalent Functionalization of CNTs
5.3.3 Encapsulation Inside CNTs
5.3.4 "Defect" Functionalization
5.4 Significance of CNT in Drug Delivery
5.5 Overview of CNT-Based Drug Delivery
5.6 Pharmacokinetics of CNTs
5.6.1 Absorption
5.6.2 Distribution
5.6.3 Metabolism and Excretion
5.7 Biosafety of Carbon Nanotube
5.7.1 Mechanism of CNT Toxicity
5.7.2 Scenario to Bypass Carbon Nanotube Toxicity
5.8 Quantum Computational
5.8.1 Structure-Based Drug Design Methods (SBDD)
5.8.2 Ligand-Based Drug Design Methods (LBDD)
5.9 Various Simulation Approaches in Drug-CNTs Interaction
5.9.1 QM Approaches
5.9.1.1 Ab Initio Approach
5.9.1.2 Semiempirical Approach
5.9.1.3 Hartree-Fock (HF) Approach
5.9.2 Molecular Dynamics (MD) Approaches
5.9.3 Monte Carlo (MC) Simulation Approaches
5.9.4 Hybrid Approaches
5.9.4.1 MD and QM Approaches
5.9.4.2 MM and QM Approaches
5.10 Applications of Quantum Computational Methods
5.10.1 Applications of Quantum Computational Methods in DDS
5.10.2 Applications of Quantum Computational Methods in Nanobiosensors
5.11 Conclusion
References
Chapter 6 Quantum Computation Approach for Nanotechnology-Based Targeted Drug Delivery Systems
6.1 Introduction
6.2 The Types of Quantum Computers
6.2.1 Scalable Quantum Computers
6.2.2 Noisy Intermediate-Scale Quantum Devices
6.2.3 Analog Quantum Devices
6.3 Role of QC in Computer-Aided Drug Design
6.4 Development of Molecular Formulations
6.4.1 QC-Based Development of Nanocarriers
6.4.2 QC in Biosensor
6.4.3 QC-Based Targeted Drug Delivery
6.4.4 Target Identification
6.4.5 Target Validation
6.4.6 Identification of Hit and Its Validation
6.4.7 Optimization of Lead
6.5 Data Generation, Interpretation, and Co-Relation
6.6 Role of QC in Clinical Trials
6.7 Future Prospects
6.8 Conclusion
References
Chapter 7 Role of Quantum Computing Simulations in Targeted Drug Delivery of Liposomes
7.1 Introduction
7.2 Liposomes
7.3 Liposome Classification
7.3.1 Based on Preparation Methods
7.3.2 Based on Compositional and Structural Characteristics
7.4 Methods of Liposome Preparation
7.5 Drug Loading Method
7.5.1 Passive Loading Technique
7.5.1.1 Sonication
7.5.1.2 French Pressure Cell
7.5.1.3 Freeze-Thawed Liposomes
7.5.1.4 Solvent Evaporation with Ether Injection
7.5.1.5 Alcohol Infusion
7.5.1.6 Method of Reverse Phase Evaporation
7.5.1.7 Removal of Non-Encapsulated Material with Detergent
7.5.2 Active Loading Technique
7.5.2.1 Pro-Liposome
7.5.2.2 Lyophilization
7.6 Newer Approaches to Liposomes
7.6.1 Stealth Liposomes (Improving Circulation Time)
7.6.2 Improving Elasticity (Transferosomes)
7.6.3 Ethosomes (Improving Skin Penetration)
7.6.4 Pharmacosomes (Improvement in Medication Delivery for Poorly Soluble Medicines)
7.6.5 Nebulized Liposomes and Stimuli-Responsive Liposomes
7.7 Quantum Computing
7.8 Computational Modeling for Drug Delivery Process
7.9 Correlation Between Quantum Computing Simulation and Targeted Delivery of Liposomes
7.10 Computational Simulation of Lipid Membranes
7.11 Properties Measured by Simulation
7.11.1 Mechanical and Structural Properties
7.11.2 Dynamic Properties
7.11.3 Molecule Permeation
7.12 Liposomal Drug Delivery System
7.13 Experimental Techniques and Role of Computational Simulation in Liposomal Drug Delivery System
7.13.1 Lipids Membrane
7.13.2 Size, Surface Charge, and Zeta Potential
7.13.3 Morphology and Lamellarity
7.14 Computational Simulation Study of Liposomes
7.15 Conclusion
References
Chapter 8 Quantum Computational Methods and Computer-Aided Drug Design in Transdermal Drug Delivery of Nanoparticles
8.1 Introduction
8.2 Skin Lipid Membranes for TDD
8.3 Computer-Aided Drug Designing for Formulation
8.4 Advantages of CADD
8.5 System and Model
8.5.1 Force Field
8.5.2 Software Supporting MD and Visualization
8.6 Computational Components
8.7 Procedure
8.7.1 Framework for In Silico NP Design
8.7.2 Integumentary Model
8.7.3 NP Model
8.7.4 Free Permeability Energy
8.8 Testing, Co-Delivery of Actives, and Active Screening
8.9 Future Challenges
8.10 Conclusion
References
Chapter 9 Computational Approaches for Drug Delivery of Nanoparticles
9.1 Drug Delivery System
9.1.1 Conventional vs. Controlled Drug Delivery Systems
9.1.2 History of DDS Development
9.1.3 Drug Delivery Routes
9.2 Nanoparticles
9.2.1 Need for Developing NPs
9.2.2 Advantages
9.2.3 Classification of NPs
9.3 Problems in Conventional Manufacturing of NPs
9.4 Computational Drug Delivery
9.4.1 Computational Approach for Nanoparticulate Drug Delivery
9.4.2 Computational Approach for Bone Drug Delivery
9.4.3 Computational Approach for Nasal Drug Delivery
9.4.4 Computational Approach for Polymeric Drug Delivery
9.4.5 Computational Approach for Microsphere Drug Delivery
9.4.6 Computational Approach for Liposomal Drug Delivery
9.4.7 Computational Approach for Tumor Cord Drug Delivery
9.4.8 Computational Approach for Gastroretentive Drug Delivery
9.4.9 Computational Approach for Bioerodible Device-Based Drug Delivery
9.5 Future Prospects
9.6 Conclusion
9.7 Acknowledgement
9.8 Funding Information
References
Chapter 10 Utilization of Computational Methods for Rational Development of Nanoemulsions, Polymeric Micelles, and Dendrimers Drug Delivery Systems
10.1 Introduction
10.2 Recognizing Optimal Drug-Excipient Pairs
10.3 Emerging of Nano-Formulation Using Theoretic Technique
10.3.1 Theoretical Approach
10.3.1.1 Solubility Parameter
10.3.1.2 Assessment of Total SP
10.3.1.3 Flory-Huggins (FH) Theory
10.4 Influence of Rigidity, Conformation, and Compatibility on Drug Loading
10.5 MD Simulations and Docking
10.5.1 Molecular Dynamics (MD)
10.6 Solubility
10.6.1 Free Energy Perturbation Calculations
10.6.2 Widom Insertion Method
10.7 Screening
10.7.1 Carriers-Oriented Screening
10.7.2 Drugs-Oriented Screening
10.7.3 Interactions Imagining
10.8 Artificial Intelligence and Machine Learning
10.9 Partition Coefficient
10.9.1 Prediction of Partition Coefficient Using Density Functional Theory (DFT)
10.9.2 Prediction of Partition Coefficient Using MD Simulations
10.10 Machine Learning
10.11 Conclusion
References
Chapter 11 Molecular Simulation: A Promising Tool for In Silico Design of Drug Delivery Formulations
11.1 Introduction
11.1.1 Simulation of Molecular Dynamic
11.1.2 A Molecular Simulations Using a General System
11.2 Software Available for Performing MDs
11.3 Applications of Molecular Simulation in Drug Delivery System
11.3.1 Lipid Bilayer Drug Diffusion and Penetration
11.3.2 Solubility of Drugs
11.3.3 Carrier-Drug Miscibility
11.3.4 Drug Crystallization
11.3.5 Drug Loading and Release
11.4 Molecular Dynamics Simulations for Nanomedical Applications
11.5 Molecular Dynamics Insight Examples
11.5.1 Bloodstream Behavior and Coronal Protective Polymer
11.5.2 Controlled Release and Drug Loading
11.6 Conclusion
References
Chapter 12 Controlling the Drug Release Rate and Targeted Drug Delivery to the Desired Site by Molecular Simulation
Abbreviations
12.1 Introduction
12.1.1 MC and MD Processes
12.1.2 Correlation of Continual Methods with Molecular Simulation
12.1.3 Simulation Scheme
12.1.4 Comparison Between MC and MD Processes
12.1.5 Size and Boundary Conditions of the System
12.1.6 Coarse-Graining
12.1.7 Free Energy Calculation
12.1.8 Force Fields
12.1.9 Simulation Software
12.2 MD Simulations: Existing Limits
12.3 MD Simulation and Drug Discovery
12.4 Applications in Drug Discovery
12.4.1 Drug Diffusion and Lipid Bilayer Permeation
12.4.2 Drug Solubility
12.4.3 Carrier-Drug Miscibility
12.4.4 Drug Crystallization
12.4.5 Drug Loading and Release
12.5 Molecular Simulation of Drug Delivery Strategies with Dendrimer
12.6 Molecular Simulation of Drug Delivery Strategies with Polymer
12.7 Liposome-Based Molecular Simulation of Drug Delivery Strategies
12.8 Molecular Simulation of Drug Delivery Strategies with Carbon Nanotube
12.9 Conclusion
Acknowledgment
Funding
References
Chapter 13 Molecular Docking: An Essential Step in Computer-Aided Drug Design
13.1 Introduction
13.1.1 Medicinal Chemistry
13.1.2 Computer-Aided Drug Design (CADD) and Molecular Docking
13.1.3 History and Progression of CADD
13.1.4 Classification of CADD
13.1.4.1 Structure-Based Drug Design (SBDD)
13.1.4.2 Ligand-Based Drug Design (LBDD)
13.1.5 Application of CADD in Drug Discovery and Development
13.1.6 Stages of Drug Discovery
13.1.6.1 Identification of Target
13.1.6.2 Target Validation
13.1.6.3 Identification of Lead
13.1.6.4 Lead Optimization
13.1.6.5 Product Characterization
13.1.6.6 Preclinical Testing
13.1.6.7 The Investigational New Drug (IND) Process
13.1.6.8 Clinical Research
13.2 Conclusion
References
Chapter 14 Challenges and Emerging Problems in CADD
14.1 Introduction
14.1.1 The Antiquity of CADD
14.2 Preamble of CADD
14.2.1 Structure-Based Drug Design
14.2.2 Protein Structure Preparation
14.2.3 Binding Site Identification
14.2.4 Molecular Docking
14.2.5 Scoring Functions
14.3 Ligand-Based Drug Design
14.4 Challenges
14.4.1 Challenges in CAD (Computer-Aided Design)
14.4.2 Geometry
14.4.2.1 Controlling Shape
14.4.2.2 Interoperability
14.4.2.3 Design Exploration
14.4.3 Interactive Techniques
14.4.3.1 Reverse Engineering
14.4.3.2 Everything in Its Place
14.4.3.3 What We Do
14.4.4 Scale
14.4.4.1 Understanding Vast Quantities of Data
14.4.4.2 Suitable Layout for Users
14.4.4.3 Retrieving Data Years Later
14.4.4.4 Constricted by the Light Speed
14.5 Challenges in CADD (Computer-Aided Drug Design)
14.5.1 Metal-Binding Docking
14.5.2 Protein-Protein Docking
14.5.3 Evaluation of Protein-Ligand Flexibility and Interaction
14.5.4 Protein-Ligand Docking
14.5.5 Docking Peptides or Peptide-Like Ligands
14.5.6 Water Solvation and Docking
14.5.7 Covalent Docking
14.5.7.1 Bigger QM Regions
14.5.7.2 Utilization of Polarizable Force Fields and Polarized Embedding
14.5.7.3 Improvements on the QM Level
14.5.8 Pose vs. Scoring
14.5.9 Chemical Space
14.5.10 Biological Space
14.6 Future Prospects of CADD
14.6.1 Molecular Dynamics Simulations
14.6.2 Virtual Screening
14.6.3 Density Functional Theory (DFT)
14.6.4 Drug Design-Related Web Servers
14.6.5 Big Data
14.6.6 Quantum Mechanics (QM)
14.7 Recent Patents in CADD
14.8 Conclusion
References
Index
EULA

Systemvoraussetzungen

Als PDF speichern Als Link merken