book

Computational Drug Delivery

Name: Computational Drug Delivery
ISBN: 9783111209715

by Pooja A. Chawla, Dilpreet Singh, Kamal Dua, Muralikrishnan Dhanasekaran, Viney Chawla

October 2024

Intermediate to advanced

440 pages

16h 56m

English

De Gruyter

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Dilpreet Singh, Pooja A. Chawla, Viney Chawla, Kamal Dua 1 Introduction to computer simulations in drug delivery: current strategies and future prospects
1.1 Introduction1.1.1 Conventional techniques in drug delivery1.1.2 Computational drug delivery: a new era of research1.1.3 Computational simulations (CSs): cutting-edge techniques in drug delivery1.1.4 Simulation plan for computer simulations1.1.5 Simulation software/packages utilized in drug delivery1.2 Applications of computer simulations in drug delivery1.2.1 Role of CS in drug delivery1.2.2 Role of PCA in drug delivery1.3 Conclusion
Ram Babu Sharma, Sakshi Tomar, Swati Kaushal, Amardeep Kaur 2 The role of multiscale approaches for the rational design of nanoparticulate drug delivery system: recent advances
2.1 Introduction to nanoparticulate drug delivery systems2.2 Rational design principles for nanoparticulate drug delivery systems2.2.1 Drug selection2.2.2 Particle size and shape2.2.3 Surface modification2.2.4 Targeting strategies2.2.5 Stability and release kinetics2.3 Multiscale approaches in nanoparticulate design2.3.1 Molecular modeling and simulation2.3.2 Nanoengineering techniques2.3.3 In vitro and in vivo studies2.3.4 Optimization of targeting strategies2.3.5 Predictive modeling and optimization2.4 Molecular modeling and simulation in nanoparticulate design2.4.1 Predicting drug–particle interactions2.4.2 Optimizing drug loading and release2.4.3 Assessing stability and aggregation tendencies2.4.4 Surface modification and targeting2.4.5 Accelerating design and optimization2.5 Nanoengineering techniques2.5.1 Emulsion-based methods2.5.2 Nanoprecipitation2.5.3 Controlled nucleation2.5.4 Bottom-up synthesis2.5.5 Top-down fabrication2.5.6 Molecular self-assembly2.5.7 Templated growth2.5.8 Atomic layer deposition (ALD)2.5.9 Multiscale characterization2.6 Target and delivery strategies in nanoparticulate drug delivery system with references2.6.1 Passive targeting2.6.2 Active targeting2.6.3 pH-responsive delivery2.6.4 Stimuli-responsive delivery2.6.5 Cell-specific targeting2.7 In vitro and in vivo study2.7.1 In vitro studies2.8 Challenges and future perspectives2.8.1 Challenges2.8.2 Future perspectives2.9 Conclusion
Mohit Motiwale, Himanshu Verma, Om Silakari, Bharti Sapra 3 The utilization of descriptors in convoluted Lipinski’s rule of five
3.1 Introduction3.2 Various rules of drug-likeness3.3 Physicochemical properties employed as descriptors by Lipinski3.3.1 Molecular weight3.3.2 Lipophilicity3.3.3 Hydrogen bond donors3.3.4 Hydrogen bond acceptors3.4 Applicability of Lipinski’s rule3.5 Beyond the rule of five3.6 Various useful databases in drug-likeness3.7 Online tools for screening new chemical entities for “rule of five”3.7.1 SCFBio, Lipinski filter3.7.2 DruLiTo3.7.3 SwissADME3.7.4 ADMETLab3.7.5 admetSAR3.7.6 QikProp3.7.7 ADMET descriptors, discovery studio (DS)3.7.8 DrugMint3.7.9 FAF-Drugs43.7.10 Drug-likeness filter (DLF)3.8 Online resources for drug-likeness of natural products and special drugs3.9 Conclusions
Sunil Kumar Kadiri, Dhritija Sathavalli, Prashant Tiwari 4 Computer-aided pharmacokinetic functions for extravascular route for oral drug delivery system
4.1 Introduction4.1.1 Principle of CADD in pharmacokinetics4.1.2 Modeling methods4.1.3 CADD simulations in pharmacokinetic functions4.1.4 Pharmacophore modeling in oral drug delivery4.1.5 Quantitative structure–activity relationship4.1.6 QSAR modeling4.1.7 Software used in pharmacokinetic functions for extravascular route for oral drug delivery4.1.8 GastroPlus software4.1.9 Application of Simcyp to human ADME prediction4.2 Computer-aided pharmacokinetic functions in oral drug delivery system4.3 Drug absorption4.3.1 Solubility4.3.2 Intestinal permeation4.3.3 Drug distribution4.3.4 PPB (Plasma Protein Binding) or protein binding4.3.5 BBB (blood–brain barrier)4.3.6 Excretion4.3.7 Active transporters4.3.8 BBB–choline transporter4.4 Conclusion
Amita Sarwal, Sunil Bharti, T.V.S. Padmajyoti 5 Computational approaches to the prediction of the blood–brain distribution and design of targeted drugs
5.1 Introduction5.2 Barriers to brain-targeted drug delivery5.2.1 Blood–brain barrier5.2.2 Blood–cerebrospinal fluid barrier5.2.3 Blood–tumor barrier5.3 Methods to assess drug permeability across the BBB5.3.1 In vitro methods5.3.2 In vivo models5.4 Various approaches for brain-targeted drug delivery5.4.1 Direct systemic delivery5.4.2 Direct CNS delivery5.4.3 Chemistry-based approaches5.4.4 Novel approaches5.4.5 Biotechnology-based approaches5.5 Computational tools5.5.1 Machine learning model5.5.2 QSAR models5.6 Software tools5.7 Future prospects
Malti Arya, Sarita K. Yadav, Madhuri Verma, Pranay Wal, Pooja A. Chawla, Viney Chawla 6 Computational methods in the pragmatic development of nanoemulsions, polymeric micelles, and dendrimers for drug delivery
6.1 Introduction6.2 Nanoemulsions6.2.1 Importance of rational design for enhanced therapeutic outcomes6.2.2 Role of computational approaches6.2.3 Significance in achieving precision and efficiency6.2.4 Decoding nanoemulsion dynamics through molecular dynamics simulations6.2.5 Optimizing nanoemulsion formulations for controlled release6.3 Polymeric micelles in drug delivery6.3.1 Computational modeling approaches6.3.2 Molecular dynamics6.3.3 QSPR (quantitative structure–property relationship)6.4 Dendrimers6.4.1 Role of computational approaches6.5 Challenges and future perspectives
Gowtham Menon, Rutuja Vilas Nikam, Sachin S. Gaikwad, Hemant U. Chikhale 7 Virtual screening of mucoadhesive polymers for the development of efficient drug delivery system: current approaches
7.1 Introduction7.2 Mechanism of mucoadhesion7.2.1 Stage 1: contact stage7.2.2 Stage 2: consolidation stage7.3 Mucoadhesive polymers7.3.1 Carbomers7.3.2 Hydroxypropyl methylcellulose (HPMC)7.3.3 Chitosan7.3.4 Polyvinylpyrrolidone (PVP)7.3.5 Polyethylene oxide (PEO)7.3.6 Sodium alginate7.3.7 Polycarbophil7.3.8 Sodium hyaluronate7.3.9 Pectin7.3.10 Gellan gum7.4 Computational techniques in pharma7.5 Virtual screening of mucoadhesive polymers7.5.1 Database selection7.5.2 Target selection7.5.3 Molecular modeling and docking7.5.4 Scoring and ranking7.5.5 Filtering and optimization7.5.6 Experimental validation7.6 Future directions and potential developments of virtual screening of mucoadhesive polymers7.6.1 Counting scheme7.6.2 Functional group filters7.6.3 Topological classification7.6.4 Pharmacophore points filter7.6.5 Scoring functions: physics-, empirical-, and knowledge-based7.6.6 Molecular weights, energy levels, and chemical structural fingerprints7.6.7 Neural networks7.6.8 High-throughput virtual screening7.7 Conclusion
Neha Jain, Triveni, Aarushi Kaith, Aditi Sinha, Tanya Mathur, Shreya Kaul, Manisha Pandey, Upendra Nagaich 8 QbD and artificial intelligence in nanoparticulate drug delivery systems: recent advances
8.1 Introduction8.2 QbD elements and its role in drug delivery8.3 General aspects of AI in nanomedicine8.4 Recent advancements employing QbD and AI in nanoparticulate-based drug delivery8.5 Challenges and future aspects8.6 Conclusion
Apporva Chawla, Prince Ahad Mir, Md Sadique Hussain, Sameena Ramzan, Tooba Dedmari, Roohi Mohi-ud-din, Pooja A. Chawla, Reyaz Hassan Mir 9 Nanotoxicity prediction in nanotechnology-driven drugs using QSPR modeling
9.1 Introduction9.2 Principles of QSPR modeling9.2.1 Definition and scope9.2.2 Importance of molecular descriptors and physicochemical properties9.2.3 Application of QSPR in drug development and safety assessment9.3 Data collection and compilation9.3.1 Sources of nanotoxicity data9.3.2 Curation and preparation of datasets9.3.3 Challenges in data quality and availability9.4 Descriptor selection9.4.1 Essential molecular and physicochemical descriptors9.4.2 The relevance of structural characteristics9.4.3 Advanced computational techniques for feature selection9.5 Model development9.5.1 Building predictive models for nanotoxicity9.5.2 Machine learning algorithms in QSPR modeling9.5.3 Model validation and performance assessment9.6 Predictive nanotoxicological endpoints9.6.1 Cellular responses and cytotoxicity9.6.2 Biodistribution and pharmacokinetics9.6.3 Organ-specific and systemic toxicity9.7 Applications in drug development9.7.1 Enhancing nanotherapeutic safety profiles9.7.2 Reducing animal testing and ethical considerations9.7.3 Regulatory implications and accelerated approvals9.8 Challenges and limitations9.8.1 Data gaps and biases in nanotoxicity datasets9.8.2 Extrapolation to real-world scenarios9.8.3 Incorporating nanoscale properties and interactions9.8.4 Future directions and emerging technologies
Shivang Dhoundiyal, Md Aftab Alam, Sakshi Sagar, Shikha Yadav, Sumbul Shadab, Niyaz Ahmad 10 Molecular simulations strategies for designing 2D nanomaterials for drug delivery applications
10.1 Introduction10.1.1 2D nanomaterials and their unique properties10.1.2 Importance of drug delivery in nanomedicine10.1.3 Role of molecular simulations in designing 2D nanomaterials for drug delivery10.2 Fundamentals of molecular simulations10.2.1 Principles and algorithms of molecular simulations10.2.2 Parameters and force fields used in molecular simulations for nanomaterials10.3 Molecular simulation strategies for designing 2D nanomaterials10.3.1 Computational methods for predicting the properties of 2D nanomaterials10.3.2 Multiscale simulations approach for studying 2D nanomaterials at different length scales and timescales10.3.3 Strategies for optimizing drug loading and release from 2D nanomaterials using molecular simulations10.4 Molecular simulations for drug delivery10.4.1 Case studies on specific types of 2D nanomaterials10.4.2 Molecular simulation-based investigation of drug–nanomaterial interactions and drug release mechanisms10.4.3 Integration of molecular simulations with experimental techniques for designing 2D nanomaterials10.5 Limitations and challenges10.6 Future perspectives10.7 Conclusion

Aditya Sharma, Sakshi Sagar, Md Aftab Alam, Manjeet Kaur, Tarique Anwer, Pramod Kumar Sharma 11 Applications and molecular simulation strategies for excipient–excipient compatibility
11.1 Introduction11.2 Molecular simulations strategies11.3 Molecular simulation software11.4 Excipient applications in formulation design and drug delivery11.5 Excipient–excipient compatibility11.6 Excipient–excipient interaction11.7 Excipient–excipient incompatibility11.8 Cause of incompatibilities11.9 Chemistry of drug–excipient interactions11.10 Impact of excipients on drug release and bioavailability11.11 Importance of excipient functionality in drug delivery applications11.12 Keeping faithfulness alive 11.13 Future prospects11.14 Conclusions
Abhishek Singh, Seema Yadav, Narahari Narayan Palei, Biswa Mohan Sahoo 12 Application of simulation system for selection of nanocarrier for biopharmaceutically challenging pharmaceuticals
12.1 Introduction12.1.1 Significance of biopharmaceutically challenging pharmaceuticals12.1.2 The conundrum of traditional drug delivery methods12.1.3 Nanocarriers: a promise of precision12.1.4 The role of simulation systems: navigating the molecular landscape12.2 Molecular dynamic simulations12.2.1 Monte Carlo simulations12.2.2 Quantitative structure–activity relationship (QSAR) modeling12.2.3 Integration of experimental data12.2.4 High-performance computing (HPC)12.2.5 Illustrating the need for advanced solutions: case studies in nanocarrier selection for biopharmaceutically challenging pharmaceuticals12.3 Nanocarriers: types and properties12.3.1 Overview of nanocarriers12.3.2 Highlighting unique properties beneficial for drug delivery12.4 Theoretical foundations of simulation systems12.4.1 Molecular dynamic simulation: decoding molecular dance12.5 Computational techniques12.5.1 HPC’s role in the acceleration of simulation processes12.5.2 Significance for large-scale studies12.6 Case studies and applications12.6.1 Challenges12.6.2 Difficulties in anticipating biological response12.6.3 Variability in the real world12.6.4 Iterative characteristics of simulation-based approaches12.7 Future perspectives and emerging trends12.7.1 Advancements in simulation systems: shaping the future of nanocarrier selection12.7.2 Emerging technologies12.7.3 Quantum computing12.7.4 Machine learning and artificial intelligence12.7.5 In silico clinical trials12.7.6 Parameterization and improved force fields12.7.7 Multiscale simulations12.7.8 High-performance computing and parallel processing12.7.9 Virtual reality (VR) and interactive simulations12.7.10 Interdisciplinary collaboration: catalyzing progress in drug discovery12.7.11 Taking down silos: the importance of collaboration12.8 Guidelines for effective simulation studies: navigating ethical considerations and ensuring data validity12.8.1 Clearly define objectives and scope12.8.2 Thorough literature review12.8.3 Select appropriate simulation tools and models12.8.4 Ethical considerations12.8.5 Rigorous data validation12.8.6 Sensitivity analysis12.8.7 Reproducibility12.8.8 Iterative refinement12.8.9 Collaboration and peer review12.9 Conclusion
Sankha Bhattacharya 13 Applications and challenges in molecular dynamic simulations in polymeric nanoparticle drug delivery systems
13.1 Introduction13.2 A molecular dynamics perspective of polymeric nanoparticle-based drug delivery systems13.3 Principles of molecular dynamic simulations13.4 Mechanical statistics: arithmetic mean13.5 Mechanics approach13.6 Applications of molecular dynamic simulations in polymeric nanoparticles drug delivery systems13.7 Mechanical statistics: arithmetic mean13.8 Regression model13.9 Structural optimization of periodic systems in molecular dynamics13.10 Challenges in molecular dynamic simulations of polymeric nanoparticles drug delivery systems13.11 Modeling the mPEG-PLGA-SS-DOX/DOX micelle generation process13.12 Atomistic simulations13.13 Coarse grain13.14 Free energy calculations for drug release13.15 Molecular dynamic simulations in polymeric nanoparticles13.16 Modeling molecular dynamics for nanomedical purpose13.17 How molecular dynamic simulation can provide mechanistic insight into drug solubility and dissolution13.18 Future directions and conclusionAcknowledgements
Rania M. Hathout 14 Role of principal component analysis in drug formulation and delivery
14.1 Overview of machine learning methods14.2 Unsupervised versus supervised machine learning methods14.3 Description of the principal component analysis method14.4 Generated plots of the principal component analysis method14.5 Applications of PCA in drug formulation and delivery14.5.1 Exploiting PCA in determining the stability of soft nanoscale drug carriers14.5.2 Capturing the highly loaded drugs on polymeric nanocarriers utilizing PCA14.5.3 PCA in drug–excipient compatibility and interactions
Vimal Arora, Payal Mittal, Sanjay Kumar Elisetti 15 Computational approaches for predicting drug solubility and permeability in pharmaceutical formulation
15.1 Introduction15.2 Impact of chemical and physical properties on drug solubility and permeability in pharmaceutical products15.3 Computational approaches for predicting drug solubility and permeability using AI15.4 Transfer learning offers several advantages in drug discoveryHowever, generative models also pose challenges15.5 Virtual screening offers several advantages in drug discovery15.6 Challenges of virtual screening15.7 Advantages and disadvantages of using computational approaches for predicting drug solubility and permeability using AI15.7.1 Advantages15.7.2 Disadvantages15.8 Conclusion
Neha Jain, Manisha Pandey, Unnati Garg, Triveni, Sakshi Malhotra, Jatin Rathee, Deepika, Shreya Kaul, Upendra Nagaich 16 Molecular simulations and process modeling of tableting technology: recent advances and future insights
16.1 Introduction16.2 Tableting and its simulation16.2.1 Tableting process16.2.2 Strategies for existing simulation and agent-based simulation16.3 Agent-based simulation model for the tableting process16.4 Recent advances16.5 Conclusion and future insights
Abhishek Singh, Seema Yadav, Narahari Narayan Palei, Biswa Mohan Sahoo 17 Molecular simulation-based technology for antibody–drug conjugates for tumor targeting: current scenario and future insights
17.1 Introduction17.2 Methods of molecular modeling and simulation17.3 Significance of ADCs in cancer treatment17.3.1 Precision targeting17.3.2 Enhanced therapeutic index17.3.3 Reduced side effects17.3.4 Overcoming drug resistance17.3.5 Diverse applicability17.3.6 Potential for personalized medicine17.3.7 Clinical success stories17.3.8 Innovative research and development17.4 Motivation for molecular simulations17.4.1 Structural complexity of ADCs17.4.2 Stability challenges17.4.3 Efficient payload release17.4.4 Understanding antibody–antigen interactions17.4.5 Pharmacokinetic optimization17.4.6 Accelerating drug development timelines17.5 Overview of antibody–drug conjugates (ADCs)17.5.1 Components of ADCs17.6 Key elements of ADC design17.7 Antibody17.8 Antibody selection17.9 Linker chemistry17.9.1 Linker selection17.9.2 Cleavable linker17.9.3 Noncleavable linkers17.10 Cytotoxic payloads for ADCs17.10.1 Auristatin17.10.2 Maytansionids17.10.3 Calicheamicin17.10.4 Duocarmycin17.10.5 Amatoxin17.11 Other ADC payloads17.11.1 Doxorubicin17.12 Mechanism of action17.12.1 Target-specific binding17.12.2 Monoclonal antibody recognition17.12.3 Antigen selection17.12.4 Selective binding17.12.5 Intracellular payload release17.12.6 Endocytosis17.12.7 Intracellular trafficking17.12.8 Lysosomal degradation17.13 Payload release17.13.1 Payload-mediated cytotoxicity17.13.2 Cytotoxic effects17.13.3 Apoptosis induction17.13.4 Selective action17.13.5 Importance of linker design17.13.6 Cleavability17.13.7 Therapeutic impact17.13.8 Reduced side effects17.14 Clinical success stories17.15 Significance in cancer treatment17.16 Current state of ADC development17.16.1 Gemtuzumab ozogamicin17.17 Role of molecular simulations in ADC development17.17.1 Molecular dynamics simulations17.17.2 Ensemble sampling in molecular dynamics17.17.3 Applications of molecular dynamic simulations17.17.4 Challenges and advances17.17.5 Future directions17.18 Monte Carlo simulations: navigating probabilistic landscapes in molecular modeling17.18.1 Fundamentals of Monte Carlo simulations17.18.2 Monte Carlo algorithms17.18.3 Applications of Monte Carlo simulations17.18.4 Challenges and advances17.18.5 Future directions17.19 Applications of molecular simulations in ADC research17.19.1 Predicting antibody–antigen interactions17.19.2 Studying payload-release mechanisms17.19.3 Understanding ADC stability and pharmacokinetics17.20 ADC design is informed by case studies and success stories in molecular simulations17.20.1 Examples of molecular simulations guiding ADC design17.20.2 Improving stability and efficacy through simulations17.20.3 Case study: pharmacokinetic optimization of an ADC17.21 Future directions and emerging technologies in molecular simulations for ADCs17.21.1 Integration of artificial intelligence17.21.2 High-performance computing advancements17.21.3 Advanced sampling techniques17.21.4 Challenges and opportunities in ADC development17.21.5 Opportunities for interdisciplinary collaborations17.22 Regulatory landscape for ADC development17.22.1 FDA and EMA guidelines for ADC development17.22.2 Ethical considerations in molecular simulations17.22.3 Transparency and reproducibility17.23 Advances in ADC technology17.23.1 First-generation ADCs17.23.2 Second-generation ADCs17.23.3 Third-generation ADCs17.24 Clinical trials of ADCs17.25 Conclusion
Index

Overview

The book bridges the gap between pharmaceutics and molecular modelling at the micro, meso and macro scale. It covers Lipinski's rule of five, nanoparticulate drug delivery, computational prediction of drug solubility and ability to cross blood brain barrier, computer-based simulation of pharmacokinetic parameters, virtual screening of mucoadhesive polymers, QSPR modelling, designing of 2D nanomaterials and role of principal component analysis.

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