Exploring Computational Pharmaceutics

Name: Exploring Computational Pharmaceutics | AI and Modeling in Pharma 4.0
Brand: Wiley
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AI and Modeling in Pharma 4.0

Defang Ouyang(Herausgeber*in)

Wiley (Verlag)

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Erschienen am 18. Juni 2024

624 Seiten

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Beschreibung

Provides an extensive and up-to-date overview of the theory and application of computational pharmaceutics in the drug development process

Exploring Computational Pharmaceutics - AI and Modeling in Pharma 4.0 introduces a variety of current and emerging computational techniques for pharmaceutical research. Bringing together experts from academia, industry, and regulatory agencies, this edited volume also explores the current state, key challenges, and future outlook of computational pharmaceutics while encouraging development across all sectors of the field. Throughout the text, the authors discuss a wide range of essential topics, from molecular modeling and process simulation to intelligent manufacturing and quantitative pharmacology.

Building upon Exploring Computational Pharmaceutics - AI and Modeling in Pharma 4.0, this new edition provides a multi-scale perspective that reveals the physical, chemical, mathematical, and data-driven details of pre-formulation, formulation, process, and clinical studies, in addition to in vivo prediction in the human body and precision medicine in clinical settings. Detailed chapters address both conventional dosage forms and the application of computational technologies in advanced pharmaceutical research, such as dendrimer-based delivery systems, liposome and lipid membrane research, and inorganic nanoparticles.

A major contribution to the development and promotion of computational pharmaceutics, this important resource:

Discusses the development track, achievements, and prospects of computational pharmaceutics
Presents multidisciplinary research to help physicists, chemists, mathematicians, and computer scientists locate problems in the field of drug delivery
Covers a wide range of technologies, including complex formulations for water-insoluble drugs, protein/peptide formulations, nanomedicine, and gene delivery systems
Focuses on the application of cutting-edge computational technologies and intelligent manufacturing of emerging pharmaceutical technologies
Includes a systematic overview of computational pharmaceutics and Pharma 4.0 to assist non-specialist readers

Covering introductory, advanced, and specialist topics, Exploring Computational Pharmaceutics - AI and Modeling in Pharma 4.0 is an invaluable resource for computational chemists, computational analysts, pharmaceutical chemists, process engineers, process managers, and pharmacologists, as well as computer scientists, medicinal chemists, clinical pharmacists, material scientists, and nanotechnology specialists working in the field.

Edited by

Dr. Defang Ouyang is Associate Professor, Institute of Chinese Medical Sciences and Faculty of Health Sciences, University of Macau. With more than twenty years of experience in academia and industry, Dr. Ouyang has pioneered the integration of multi-scale modeling, Artificial Intelligence, and Big Data techniques in the field of drug delivery. He developed six approved patents and published two books, several book chapters, and over 100 refereed journal papers.

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Inhalt

Cover
Title Page
Copyright
Contents
List of Contributors
Preface
Chapter 1 Introduction to Computational Pharmaceutics
1.1 Current Pharmaceutical Research
1.2 What is Computational Pharmaceutics?
1.3 About This Book
References
Chapter 2 Opportunities and Challenges of Artificial Intelligence (AI) in Drug Delivery
2.1 Introduction
2.2 Machine Learning Algorithms
2.2.1 Linear Models
2.2.2 Artificial Neural Networks
2.2.3 Deep Learning
2.2.4 Genetic Algorithm
2.2.5 Fuzzy Neural Network
2.2.6 Support Vector Machine
2.2.7 Decision Tree
2.2.8 Ensemble Learning
2.3 Applications of Machine Learning in Pharmaceutics
2.3.1 Immediate-Release Tablets
2.3.2 Hard Gelatin Capsules
2.3.3 Oral Sustained-Release Preparations
2.3.4 Emulsion, Microemulsion, and Nanoemulsion Drug Delivery Systems
2.3.5 Hydrogel Transdermal Drug Delivery Systems
2.3.6 Nanoparticle Drug Delivery Systems
2.3.7 Solid Dispersions
2.3.8 Cyclodextrin Complexations
2.4 Applications of Large-Scale Models in Drug Discovery and Development
2.4.1 Language Model Pre-Training for Downstream Drug Discovery and Development Tasks
2.4.2 Multi-Task Learning and Multi-Property Prediction Pre-Training for Downstream Drug Discovery and Development Tasks
2.4.3 Drug-Target Interaction Prediction
2.4.4 Antimicrobial Peptide Prediction
2.4.5 Pharmacokinetic Property Prediction
2.4.6 Generative Models for New Molecule Design
2.4.7 Clinical Medical Image Processing
2.4.8 Medical Document Retrieval and Recommendation
2.4.9 Clinical Rational Drug Use and Drug-Drug Interaction Prediction
2.4.10 Automated Experimental Platform and Machine Learning Interpretability
2.5 AI in the Clinic and Precision Medicine
2.6 Opportunities and Challenges
2.7 Summary
References
Chapter 3 Computational Resources in Pharmaceutics
3.1 Concept and Overview of Computational Pharmaceutics
3.2 Databases
3.2.1 Database Technology Promotes Efficient Communication in Pharmaceuticals
3.2.2 Databases for Drug Formulation
3.3 Computational Platforms/Web Servers
3.3.1 Artificial Intelligence Promotes Rapid Screening and Evaluation of Drug Formulations
3.3.2 Artificial Intelligence Models in Pharmaceuticals
3.3.3 Computational Platforms/Web Servers
3.4 Implementation Methods for Databases and Computing Platforms
3.5 Summary and Outlook
References
Chapter 4 Computational Modeling of Dry Powder Inhalation
4.1 Introduction
4.2 Discrete Element Methods (DEMs)
4.3 Modeling of Agglomeration
4.3.1 Formation of Drug-Only Agglomerates
4.3.2 Formation of Carrier-Based Agglomerates
4.4 Modeling of De-agglomeration
4.4.1 De-agglomeration Due to Aerodynamic Forces
4.4.2 De-agglomeration Due to Mechanical Impact
4.5 Modeling of Particle Dispersion in DPIs
4.5.1 DEM-CFD Modeling at the Device Scale
4.5.2 Multiscale Modeling of Aerosolization
4.5.3 CFD-DPM Modeling of Aerosolization
4.6 Summary and Perspectives
References
Chapter 5 Molecular Modeling in Drug Delivery: Polymer Protective Coatings as Case Study
5.1 Introduction
5.1.1 Prodrugs and Nanomedicine: Easing the Balancing Act of Drug Delivery
5.1.2 Polymers in Drug Delivery
5.1.3 Mechanistic Understanding: Paradigm of Biophysics in Pharmaceutics
5.1.4 Molecular Dynamics Simulation: Tool to Obtain Mechanistic Understanding
5.2 Molecular Dynamics Simulation of Polymers in Drug Delivery
5.2.1 Lipid-Based Systems
5.2.2 Polymer-Based Systems
5.2.3 Protein-Based Systems
5.2.4 Inorganic Nanoparticles
5.3 Conclusion
References
Chapter 6 3D Structural Investigation of Solid Dosage Forms
6.1 Structure of Solid Dosage Forms and Methods of Investigation - An Overview
6.2 Synchrotron Radiation X-ray Computed Microtomography
6.3 3D Structure Reconstruction Based on SR-&rmmu
CT
6.3.1 Preparation of Samples
6.3.2 Image Acquisition and 3D Reconstruction
6.3.3 Model Construction and Analysis
6.4 3D Visualization and Quantitative Characterization
6.4.1 Microstructure of Particles and Granular Systems
6.4.2 Static Structure and Material Distribution of Solid Dosage Form
6.4.3 Dynamic Structure of Hydrophilic Matrix Tablets
6.4.4 Dynamic Structure of Osmotic Pump Tablets
6.5 Future Prospects
References
Chapter 7 Dissolution Mechanism of Pharmaceuticals and Formulations by Nonequilibrium Thermodynamic Modeling
7.1 Introduction
7.2 Theoretical Basis and Models
7.2.1 Phase Equilibrium and Chemical Potential
7.2.2 Chemical Potential Gradient Model
7.2.3 Statistical Rate Theory
7.2.4 Perturbed Chain Statistical Associating Fluid Theory (PC-SAFT)
7.2.5 Activity Coefficient Calculation by PC-SAFT
7.2.6 Calculation of the Dissolution Profiles
7.3 Experimental Methods
7.3.1 Measurement of API Solubility
7.3.2 Measurement of Calorimetric Properties
7.3.3 Preparation of API/Polymer Formulations
7.3.4 Characterizations of DSC, XRD, and SEM
7.3.5 In vitro Intrinsic Dissolution Measurement
7.3.6 UV-Vis Spectrophotometric Analysis
7.4 Mechanism Analysis and Model Predictions
7.4.1 Dissolution Kinetics and Mechanism of Crystalline APIs
7.4.2 Dissolution Kinetics of API/Polymer Formulations
7.5 Conclusions and Outlook
References
Chapter 8 Physiologically Based Pharmacokinetics
8.1 Definition and History of Physiologically Based Pharmacokinetics
8.2 Principles and Structures of the Physiologically Based Pharmacokinetics Model
8.2.1 Intrinsic Clearance, Extraction Ratio, and Well-Stirred Model
8.2.2 Allometric Scaling Method
8.2.3 In vitro-In vivo Extrapolation Method
8.2.4 Basic Model Structure and Important Parameters of Physiologically Based Pharmacokinetics
8.2.5 Physiologically Based Pharmacokinetics Modeling and Simulation Basic Steps
8.3 Physiologically Based Pharmacokinetics Model in Complex Situations
8.3.1 Permeability-Limited Model
8.3.2 Physiologically Based Pharmacokinetics Model of the Distribution Process
8.3.3 Physiologically Based Pharmacokinetics Model of the Absorption Process
8.4 Challenges and Perspectives of the Physiologically Based Pharmacokinetics Model
8.4.1 Physiologically Based Pharmacokinetics Modeling Software
8.4.2 Acquirement for System Parameters of the Physiologically Based Pharmacokinetics Model
8.4.3 Perspectives on Future Research on Physiologically Based Pharmacokinetic Models
References
Chapter 9 Molecular Modeling in Drug Delivery
9.1 Introduction
9.2 Basic Principles of Molecular Dynamic Simulation and Molecular Modeling Methods
9.2.1 Basic Principles of Molecular Dynamic Simulation
9.2.2 Molecular Modeling
9.2.3 Molecular Dynamic Simulations
9.3 Molecular Dynamic Simulation of Drug Delivery Strategies with Nanoparticles
9.3.1 Carbon-Based Nanomaterials
9.3.2 Silicon-Based Nanomaterials
9.3.3 Metal-Based Nanomaterials
9.3.4 Other Nanoparticles
9.3.5 Other Applications of Molecular Dynamic in DDS
9.4 Summary
References
Chapter 10 Integration of Dendrimer-Based Delivery Technologies with Computational Pharmaceutics and Their Potential in the Era of Nanomedicine
10.1 Introduction
10.2 Dendrimers as Drug/Gene Delivery Systems and Their Pharmaceutical Applications
10.2.1 Multifunctional Carrier Systems
10.2.2 Solubility Enhancers
10.2.3 Permeation Enhancers
10.2.4 Drug Delivery Agents
10.2.5 Therapeutic Agents
10.2.6 Gene Delivery Agents
10.2.7 Role and Application of Dendrimers in the COVID-19 Pandemic
10.3 Computational Aspects of Dendrimer-Based Drug Delivery and Challenges
10.4 Conclusions
References
Chapter 11 Artificial Intelligence and Computational Modeling in Orally Inhaled Drugs
11.1 Overview
11.2 Chronic Respiratory Diseases and Inhaled Therapy
11.2.1 Chronic Respiratory Diseases
11.2.2 Inhaled Therapy
11.2.3 Inhalers
11.3 Introduction of Computational Methods
11.3.1 Computational Fluid Dynamics Modeling
11.3.2 Physiologically Based Pharmacokinetic Modeling
11.3.3 Artificial Intelligence
11.3.4 Verification and Validation of Computational Models
11.4 Applications in R&D of Inhalers and Drug Formulations
11.4.1 Nebulizer Development
11.4.2 pMDI Development
11.4.3 SMI Development
11.4.4 DPI Development
11.4.5 Inhaled Drug Formulations
11.5 Applications in the Evaluation of Inhaled Drug Efficacy
11.5.1 Prediction of Drug Deposition
11.5.2 PBPK Modeling of Inhaled Drug Dissolution and Absorption
11.6 Applications in the Management of Chronic Respiratory Diseases
11.6.1 Inhaler-based Electronic Monitoring Devices
11.6.2 Improvement in Adherence
11.6.3 Measurement of Inhalation Parameters
11.6.4 Predictive Models for Acute Exacerbations
11.7 Challenges and Future
11.8 Conclusions
References
Chapter 12 Digital Formulation Development Using 3D Printing Technology: AI and Modeling
12.1 Introduction
12.2 3D Printing Methods in the Formulation of Pharmaceuticals
12.2.1 Extrusion-based Methods
12.2.2 Powder-Based Methods
12.2.3 Liquid-Based Methods
12.2.4 Sheet Lamination-Based Methods
12.3 Novel Tablet Structures Possible with 3D Printing
12.3.1 Unique Tablet External Geometries
12.3.2 Unique Tablet Internal Geometries
12.4 Artificial Intelligence in Formulation Development Using 3D Printing
12.4.1 Excipient Selection
12.4.2 Formulation Development using 3D Printing
12.4.3 Mathematical Modeling in Formulation Development Using 3D Printing
12.4.4 Predicting Printability
12.4.5 Predicting Dissolution Profiles
12.5 3D Printing Formulation by Design
12.5.1 3D Printing Formulation by the Design (3DFbD®) Approach
12.5.2 Contribution of 3DFbD® to Quality by Design
12.6 Summary
References
Chapter 13 A Review on Research and Application of Expert Systems on Drug Formulation and Process Design
13.1 Introduction
13.2 The Structure of ES
13.2.1 Database
13.2.2 Rule Base
13.2.3 Inference Engine
13.2.4 User Interface
13.3 Applications
13.3.1 SeDeM
13.3.2 Typical ESs in TCM Research
13.4 Discussions
References
Chapter 14 Application of PBPK Modeling in Formulation Development
14.1 Introduction
14.2 Pharmacokinetic (PK) Software for Modeling
14.2.1 Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) Modeling
14.2.2 De Novo Drug Design and Synthesis Planning
14.2.3 Drug Formulation Design Using Molecular Dynamic (MD) Simulation
14.2.4 Drug Formulation Design with Physiologically Based Pharmacokinetic Modeling
14.3 Modeling Mechanisms for Different Types of Dosage Forms
14.3.1 Models for Oral Solid Dosage Forms
14.3.2 Inhalation Model
14.3.3 Dermal Model
14.3.4 Long-acting Injection Model
14.3.5 Limitations and Improvements
14.4 Summary and Conclusion
References
Chapter 15 Multiscale Models for Tablet Manufacturing Process Development
15.1 Introduction
15.2 Tablet Manufacturing
15.3 Computational Modeling
15.4 Case Studies
15.4.1 Diamond Pilot Plant at the University of Sheffield
15.4.2 Simulation of a Continuous Direct Compression Process
15.5 Summary and Outlook
References
Chapter 16 Machine Learning as a Part of Pharmaceutical Product Development
16.1 Introduction
16.2 Pharmaceutical Materials Science
16.2.1 Examples of Pharmaceutically Relevant Databases
16.2.2 Simulation
16.3 Product Design
16.4 Processing of Pharmaceuticals
16.4.1 Process Data and Predictive Models
16.4.2 Process Analytics as a Source of Data
16.4.3 Aspects Related to Production Systems for Personalized Pharmaceuticals
16.5 Analytical Chemistry in a Pharmaceutical Setting
16.6 Concluding Remarks
References
Chapter 17 Big Data Analysis of Patents and Their Applications in Biomedical Research and Development
17.1 Introduction
17.1.1 Status of Patent Data Utilization
17.1.2 Utility of Patent Data for Biomedical Research
17.1.3 Conceptual Framework
17.2 Patent Landscape Analysis
17.2.1 Data Collection and Standardization
17.2.2 Brief Bibliometric Analysis
17.2.3 Standards of Patent Landscaping
17.2.4 Patent Retrieval Database
17.3 Mining Chemical Information from Patents
17.3.1 Chemical Information in Patents
17.3.2 Methods in Chemical Information Data Mining
17.3.3 Patent Chemical Information Database
17.3.4 Comparison of Popular OCSR Tools
17.4 Mining Biological Information from Patents
17.4.1 Biological Information in Patents
17.4.2 Methods in Biological Sequence Data Mining
17.4.3 Patent Biological Information Databases
17.4.4 Patent Antibody Sequence Data Mining - A Case Study
17.5 Mining Pharmaceutics Information from Patents
17.5.1 Studies of Pharmaceutics Patent Analysis
17.5.2 Methods in Pharmaceutics Information Data Mining
17.6 Practical Operations and Related Issues
17.6.1 Patent Retrieval Database Operation
17.6.2 Chemical Information Database Operation
17.6.3 Biological Information Database Operation
17.6.4 Database Issues
17.6.5 Workflow of Biomedical Patent Analysis
17.7 Outlook
References
Chapter 18 Model-informed Drug Development (MIDD) Regarding Regulatory Requirements and Thinking
18.1 Driving Forces Toward MIDD
18.2 Regulatory Guidance on Modeling Methods
18.2.1 E-R Models or PK/PD Models
18.2.2 Pop-PK Model
18.2.3 PBPK Model
18.2.4 CFD and Other Modeling Techniques
18.3 Evolving Thinking on MIDD
18.3.1 PBPK Modeling for In Vivo Study Waiver
18.3.2 PK-Related Modeling for Reducing BE Study Burden
18.3.3 Applications of Machine Learning and Other Statistical Models
18.3.4 Efforts from Regulatory Agencies Toward Promoting MIDD Applications
18.4 The Advancement of Pharmacometrics in China
18.5 Summary
References
Index
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