Biosystems, Biomedical and Drug Delivery Systems : Characterization, Restoration and Optimization

• Autore: Kulkarni Shrikaant
• Pubblicazione: Singapore : , : Springer, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (376 pages)
• Altri autori: HaghiA. K ManwatkarSonali
• Nota di contenuto: Intro -- Preface -- Contents -- Contributors -- Abbreviations -- List of Figures -- 1 Editorial: Future of Novel Technologies in Biosystems, Biomedical, and Drug Delivery -- 1.1 Nature and Characterization of Biosystems -- 1.2 Restoration of Biological Functions -- 1.3 Optimization of Drug Delivery -- 1.4 Conclusion -- Part I Novel Technologies in Biosystems, Biomedical, and Drug Delivery: Characterization -- 2 Characterization Tools for Current Drug Delivery Systems -- 2.1 Introduction -- 2.2 Techniques Employed in Characterizing Drug Delivery Systems -- 2.3 Determination of Particle Size -- 2.3.1 Utilizing Dynamic Light Scattering (DLS)/Photon Correlation Spectroscopy (PCS) Technique -- 2.3.2 Single-Particle Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) -- 2.4 Microscopic Characterization Techniques -- 2.4.1 Scanning Electron Microscopy (SEM) -- 2.4.2 Environmental Scanning Electron Microscopy (ESEM) -- 2.4.3 Field Emission Scanning Electron Microscopy (FESEM) -- 2.4.4 Transmission Electron Microscopy (TEM) -- 2.4.5 Confocal Laser Scanning Microscopy (CLSM) -- 2.4.6 Atomic Force Microscopy (AFM) -- 2.5 Compatibility Studies (Physical-Chemical Characterization) -- 2.5.1 Thermogravimetry (TG) and Differential Scanning Calorimetry (DSC) -- 2.5.2 X-Ray Powder Diffraction (XRPD) -- 2.6 Fourier Transform Infrared Spectroscopy (FTIR) -- 2.7 Limitations of Existing Characterization Techniques -- 2.7.1 Dynamic Light Scattering (DLS)/Photon Correlation Spectroscopy (PCS) -- 2.7.2 Single-Particle Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) -- 2.7.3 Scanning Electron Microscopy (SEM) -- 2.7.4 Environmental Scanning Electron Microscopy (ESEM) -- 2.7.5 Field Emission Scanning Electron Microscopy (FESEM) -- 2.7.6 Transmission Electron Microscopy (TEM) -- 2.7.7 Confocal Laser Scanning Microscopy (CLSM) -- 2.7.8 Atomic Force Microscopy (AFM). ; 2.7.9 Differential Scanning Calorimetry (DSC)/Thermogravimetry (TG) -- 2.7.10 X-Ray Powder Diffraction (XRPD) -- 2.7.11 Fourier Transform Infrared Spectroscopy (FTIR) -- 2.8 Discussion -- References -- 3 Characterization of Transdermal Drug Delivery Systems: Retrospect and Future Prospects -- 3.1 Introduction -- 3.2 Historical Perspective -- 3.2.1 Early Transdermal Patches -- 3.2.2 Milestones in TDDS Evolution -- 3.3 Challenges in Transdermal Drug Delivery -- 3.3.1 Skin Barrier -- 3.3.2 Dose Limitations -- 3.3.3 Adhesion and Irritation -- 3.3.4 Variable Absorption -- 3.4 Future Prospects -- 3.4.1 Nanotechnology -- 3.4.2 Personalized TDDS -- 3.4.3 Biologics and Vaccines -- 3.4.4 Sustainable Materials -- 3.4.5 Continuous Monitoring -- 3.5 Synergistic Potential and the Challenges of Transdermal Drug Delivery System with Artificial Intelligence (AI) -- 3.5.1 Enhanced Drug Formulation and Design -- 3.5.2 Personalized Medicine -- 3.5.3 Predictive Modeling -- 3.5.4 Feedback Mechanisms -- 3.5.5 Data-Driven Optimization -- 3.5.6 Challenges and Considerations -- 3.6 Conclusion -- References -- 4 Analytical Tools for the Characterization of Nasal Spray Drug Products -- 4.1 Introduction -- 4.2 Physical Tests -- 4.2.1 Pump Delivery (Shot Weight) -- 4.2.2 Number of Actuations/Containers -- 4.2.3 Viscosity -- 4.2.4 Net Fill Content and Minimum Fill Justification -- 4.2.5 Osmolality -- 4.2.6 Priming and Repriming Study -- 4.2.7 Surface Tension -- 4.3 Nasal Spray Characterization Tests -- 4.3.1 Droplet Size Distribution (DSD) -- 4.3.2 Spray Pattern (SP) -- 4.4 Plume Geometry (PG) -- 4.4.1 Method Precision -- 4.4.2 Intermediate Precision -- 4.4.3 Robustness -- 4.5 Chemical Tests -- 4.5.1 Assay -- 4.5.2 Related Substances -- 4.5.3 Preservative Content -- 4.6 Conclusion -- References -- Part II Novel Technologies in Biosystems, Biomedical, and Drug Delivery: Restoration. ; 5 AI-Enabled Models in the Restoration of Drug Efficacy and Drug Design -- 5.1 Introduction -- 5.2 Traditional Drug Discovery Process: Challenges and Limitations -- 5.3 The Emergence of AI in Drug Discovery and Design -- 5.4 Data Collection and Management -- 5.5 Target Identification and Validation -- 5.5.1 Systematic Analysis of Biological Datasets -- 5.5.2 Precision in Target Selection -- 5.5.3 Reducing Late-Stage Failures -- 5.5.4 Streamlining Drug Development -- 5.6 Molecular Modeling and in Silico Drug Design -- 5.6.1 Enhanced Accuracy and Speed -- 5.6.2 Efficient Drug Candidate Assessment -- 5.6.3 Reducing Time and Resource Investment -- 5.6.4 Tailored Therapeutics -- 5.6.5 Iterative Improvement -- 5.7 High-Throughput Screening and Compound Selection -- 5.7.1 AI-Powered Robotic Systems -- 5.7.2 Machine Learning Algorithms -- 5.7.3 The Advantages of AI in Compound Selection -- 5.7.4 Comprehensive Screening of Compound Libraries -- 5.8 Predictive Toxicology and ADMET Evaluation -- 5.8.1 Traditional Challenges in Predictive Toxicology and ADMET Evaluation -- 5.8.2 The Role of AI in Predictive Toxicology -- 5.8.3 Optimizing ADMET Evaluation with AI -- 5.8.4 The Advantages of AI in Predictive Toxicology and ADMET Evaluation -- 5.9 Clinical Trial Optimization -- 5.9.1 Efficient Patient Recruitment -- 5.9.2 Personalized Trial Matching -- 5.9.3 Adaptive Clinical Trials -- 5.9.4 Enhanced Data Analysis -- 5.9.5 Benefits and Implications -- 5.10 Ethical and Regulatory Considerations -- 5.10.1 Ethical Considerations in AI-Driven Drug Discovery -- 5.10.2 Regulatory Challenges in an AI-Driven Landscape -- 5.10.3 Building Trust and Accountability -- 5.11 Challenges and Future Directions -- 5.12 Conclusions -- References -- 6 Restoration and Sustenance of Nano Drug Delivery Systems: Potential, Challenges, and Limitations -- 6.1 Introduction. ; 6.2 Conventional Drug Delivery Systems: Challenges and Limitations -- 6.2.1 Microspheres -- 6.2.2 Gels -- 6.2.3 Prodrugs -- 6.3 New Drug Carriers Systems -- 6.3.1 Polymeric Nanoparticles -- 6.3.2 Metal Nanoparticles and Quantum Dots -- 6.3.3 Micro and Nanosponges -- 6.3.4 Microsponges -- 6.3.5 Nanosponges -- 6.3.6 Vesicular System -- 6.3.7 Solid Lipid Nanoparticles (SLNs) -- 6.3.8 Nano-structured Lipid Carriers -- 6.3.9 Microemulsions -- 6.3.10 Nanoemulsions -- 6.3.11 Immunoconjugates -- 6.3.12 "In Situ Gel Drug Delivery System" -- 6.4 Challenges and Future Directions -- 6.5 Conclusion -- References -- 7 Artificial Intelligence and Machine Learning in Restoring and Strengthening HealthCare -- 7.1 Introduction to ML and AI -- 7.2 Tasks Machine Learning Can Handle Within Health Care -- 7.3 Opportunities and Prospects of Machine Learning Provides for Healthcare -- 7.4 Health Care Advantages of Machine Learning -- 7.5 Potential Applications of ML in Healthcare -- 7.5.1 Clinical Decision Support Systems (CDSS) -- 7.5.2 Smart Recordkeeping -- 7.5.3 Machine Learning in Medical Imaging -- 7.5.4 Personalized Medicine -- 7.5.5 Behavior Adjustments -- 7.5.6 Predictive Approach to Treatment -- 7.5.7 Data Collection -- 7.5.8 Elderly and Low-Mobility Groups Care -- 7.5.9 Robotic Surgery -- 7.6 Ethics of Employing ML in Healthcare -- 7.6.1 Data Security and Privacy -- 7.6.2 Issues with Autonomy -- 7.6.3 Patient Safety -- 7.6.4 Clear Communication and Informed Consent -- 7.6.5 Inclusion and Representation -- 7.7 ML Problems Within the Healthcare Sector -- 7.7.1 Inadequacy of Reliable Data to Develop Accurate Algorithms -- 7.7.2 Creating ML Tools that Are Compliant with Medical Workflow -- 7.7.3 Building Teams with Diverse Skill Sets in One Location -- 7.8 Future Scope -- References. ; Part III Novel Technologies in Biosystems, Biomedical, and Drug Delivery: Optimization -- 8 Optimizing Oncology Tools: Organ-On-A-Clip Alternative to Animal Model -- 8.1 Introduction -- 8.2 Oncology: Challenges and Constraints in Drug Development -- 8.3 Primer for the Organ-On-A-Chip (OoC) -- 8.3.1 Drug Screening and Development -- 8.3.2 Personalized Medicine -- 8.3.3 Microenvironment Replication -- 8.3.4 Metastasis Studies -- 8.3.5 Evaluation of Therapeutic Resistance -- 8.3.6 Reducing Dependency on Animal Models -- 8.3.7 Integration with Microfluidics -- 8.4 Design of OoC Devices -- 8.5 OoCs Based Platforms for Novel Drug Development -- 8.6 Microfluidic Systems in Cancer Investigation -- 8.7 Utilizing Microfluidics for Isolation of Circulating Tumour Cell (CTC) -- 8.8 Application of Microfluidic Platforms for Analysis of Cancer Cell Phenotype -- 8.9 Device for Exploring Metastasis Using Microfluidics -- 8.10 The Implementation Process of Constructing the Tumor Microenvironment -- 8.11 Conclusion -- References -- 9 Optimizing Drug Synthesis: AI-Powered Kinetics Study in Pharmaceutical Research -- 9.1 Introduction -- 9.1.1 The Evolution of AI and ML in Drug Discovery -- 9.1.2 The Role of Kinetics in Drug Synthesis -- 9.2 Foundations of AI and ML in Drug Synthesis -- 9.2.1 Machine Learning Algorithms for Kinetics Prediction -- 9.2.2 Data Sources and Data Preprocessing -- 9.2.3 Model Validation and Performance Metrics -- 9.3 Predicting Reaction Rates -- 9.3.1 AI/ML Models for Reaction Rate Prediction -- 9.3.2 Reaction Mechanism and Rate-Determining Steps -- 9.3.3 Case Studies Related to Reaction Rate Prediction -- 9.4 Optimizing Reaction Conditions -- 9.4.1 Optimizing Techniques and Algorithms -- 9.4.2 Bayesian Optimization in Drug Synthesis -- 9.5 Understanding Reaction Mechanisms -- 9.5.1 Deep Learning Approaches to Reaction Mechanism Elucidation. ; 9.5.2 Reaction Pathways and Transition State Modeling.
• Titolo autorizzato: Biosystems, Biomedical and Drug Delivery Systems
• ISBN: 9789819725960; 9789819725953
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910865242803321
• Lo trovi qui: Univ. Federico II