The world of pharmaceutical research is moving at lightning speed, and the age-old approach to drug discovery faces many challenges. It's a fascinating time to be on the cutting edge of medical innovation, but it's certainly not without its obstacles. The process of developing new drugs is often time-consuming, expensive, and fraught with uncertainty. Researchers are constantly seeking ways to streamline this process, reduce costs, and increase the success rate of bringing new drugs to market. One promising solution lies in the convergence of pharmacy science and engineering, particularly in computational drug discovery. Converging Pharmacy Science and Engineering in Computational Drug Discovery presents a comprehensive solution to these challenges by exploring the transformative synergy between pharmacy science and engineering. This book demonstrates how researchers can expedite the identification and development of novel therapeutic compounds by harnessing the power of computational approaches, such as sophisticated algorithms and modeling techniques. Through interdisciplinary collaboration, pharmacy scientists and engineers can revolutionize drug discovery, paving the way for more efficient and effective treatments. This book is an invaluable resource for pharmaceutical scientists, researchers, and engineers seeking to enhance their understanding of computational drug discovery. This book inspires future innovations by showcasing cutting-edge methodologies and innovative research at the intersection of pharmacy science and engineering. It contributes to the ongoing evolution of pharmaceutical research. It offers practical insights and solutions that will shape the future of drug discovery, making it essential reading for anyone involved in the pharmaceutical industry.

Healthcare and pharmaceuticals are rapidly advancing with technological innovations, and the lack of understanding of AI algorithms poses a significant challenge in these fields. The need for more transparency in AI decision-making processes raises concerns about accountability, ethical implications, and regulatory compliance. As stakeholders in these critical sectors seek clarity and understanding, Analyzing Explainable AI in Healthcare and the Pharmaceutical Industry provides a reliable resource to discover new solutions. This book serves as a comprehensive guide, unraveling the complexities of explainable artificial intelligence (XAI) and its pivotal role in transforming healthcare and pharmaceutical practices. Demystifying AI algorithms and revealing their decision-making mechanisms equips readers with the foundational knowledge needed to confidently navigate AI integration in these domains. From healthcare professionals to policymakers, its insights cater to a diverse audience, fostering cross-disciplinary collaboration and facilitating informed decision-making.

In today’s digital age, the healthcare industry is undergoing a paradigm shift towards embracing innovative technologies to enhance patient care, improve efficiency, and ensure data security. With the increasing adoption of electronic health records, telemedicine, and AI-driven diagnostics, robust cybersecurity measures and advanced data management strategies have become paramount. Protecting sensitive patient information from cyber threats is critical and maintaining effective data management practices is essential for ensuring the integrity, accuracy, and availability of vast amounts of healthcare data. Cybersecurity and Data Management Innovations for Revolutionizing Healthcare delves into the intersection of healthcare, data management, cybersecurity, and emerging technologies. It brings together a collection of insightful chapters that explore the transformative potential of these innovations in revolutionizing healthcare practices around the globe. Covering topics such as advanced analytics, data breach detection, and privacy preservation, this book is an essential resource for healthcare professionals, researchers, academicians, healthcare professionals, data scientists, cybersecurity experts, and more.

Artificial Intelligence (AI) technology has led to the creation of many opportunities in the field of healthcare. Like other industries, stakeholders in the healthcare sector stand to benefit tremendously from its adoption. The multifaceted benefits associated with AI are something that makes the adoption of technology constructive for the sector. That said, it is equally important to take care of the ethical, security, and safety challenges related to AI applications. AI Healthcare Applications and Security, Ethical, and Legal Considerations discusses in detail the various facets of AI integration in the healthcare sector. This book offers comprehensive information on how to integrate AI into the healthcare sector safely and ethically. Covering topics such as cybersecurity, machine learning models, and public policy, this book is an excellent resource for healthcare professionals and administrators, researchers, ethicists, legal scholars, healthcare policy makers and regulators, medical informatics and IT professionals, educators, bioethics professionals, academicians, and more.

In the realm of healthcare, the persistent challenges of data breaches, centralized systems, and fraudulent claims have posed significant hurdles in ensuring the integrity and security of patient information. The traditional approaches to managing Electronic Health Records (EHR) often fall short, leaving room for exploitation and compromising the confidentiality of sensitive medical data. Enter the transformative solution presented in Blockchain and IoT Approaches for Secure Electronic Health Records (EHR). This groundbreaking book navigates the intricate landscape of healthcare technology, addressing the vulnerabilities in the current systems. By leveraging the power of Blockchain technology, it pioneers a secure peer-to-peer communication system that not only ensures the tamper-proof nature of health records but also revolutionizes the entire healthcare industry. The book is a comprehensive exploration of Blockchain's relevance in healthcare, covering the architecture, scope, and applications that promise to redefine how patient data is managed and protected.

In the field of healthcare technology, the Internet of Medical Things (IoMT) stands at the forefront of progress, revolutionizing patient care through advanced monitoring and treatment modalities. However, this digital transformation brings forth a new challenge— the vulnerability of sensitive medical data to cyber threats. Lightweight Digital Trust Architectures in the Internet of Medical Things (IoMT) examines ways to fortify IoMT against potential breaches through the exploration of these trust architectures. Delving deep into data privacy technologies, the book examines the implications of regulatory frameworks such as GDPR, HIPAA, and cybersecurity law. It assesses traditional security methods and considers innovative approaches, offering insights into certificate generation, digital identification, and the optimization of network protocols for secure data transmission.Lightweight Digital Trust Architectures in the Internet of Medical Things (IoMT) illuminates the path forward for IoMT security. Its objectives are multi-faceted: from unraveling the intricacies of the health chain to dissecting the role of lightweight cryptographic key agreement mechanisms in safeguarding medical data. The book grapples with the challenges and advantages of implementing compact cryptographic techniques in healthcare, particularly within the decentralized framework of IoMT. By exploring the potential of Federated Learning (FL) in bolstering privacy and improving healthcare outcomes, the book aims to equip researchers, healthcare professionals, and IT experts with valuable knowledge. Through real-world case studies, it endeavors to pave the way for a future where security and efficiency seamlessly integrate in IoMT.

Managing the Drug Discovery Process, Second Edition thoroughly examines the current state of pharmaceutical research and development by providing experienced perspectives on biomedical research, drug hunting and innovation, including the requisite educational paths that enable students to chart a career path in this field. The book also considers the interplay of stakeholders, consumers, and drug firms with respect to a myriad of factors. Since drug research can be a high-risk, high-payoff industry, it is important to students and researchers to understand how to effectively and strategically manage both their careers and the drug discovery process. This new edition takes a closer look at the challenges and opportunities for new medicines and examines not only the current research milieu that will deliver novel therapies, but also how the latest discoveries can be deployed to ensure a robust healthcare and pharmacoeconomic future. All chapters have been revised and expanded with new discussions on remarkable advances including CRISPR and the latest gene therapies, RNA-based technologies being deployed as vaccines as well as therapeutics, checkpoint inhibitors and CAR-T approaches that cure cancer, diagnostics and medical devices, entrepreneurship, and AI. Written in an engaging manner and including memorable insights, this book is aimed at anyone interested in helping to save countless more lives through science. A valuable and compelling resource, this is a must-read for all students, educators, practitioners, and researchers at large—indeed, anyone who touches this critical sphere of global impact—in and around academia and the biotechnology/pharmaceutical industry. - Considers drug discovery in multiple R&D venues - big pharma, large biotech, start-up ventures, academia, and nonprofit research institutes - with a clear description of the degrees and training that will prepare students well for a career in this arena - Analyzes the organization of pharmaceutical R&D, taking into account human resources considerations like recruitment and configuration, management of discovery and development processes, and the coordination of internal research within, and beyond, the organization, including outsourced work - Presents a consistent, well-connected, and logical dialogue that readers will find both comprehensive and approachable - Addresses new areas such as CRISPR gene editing technologies and RNA-based drugs and vaccines, personalized medicine and ethical and moral issues, AI/machine learning and other in silico approaches, as well as completely updating all chapters

Artificial Neural Network for Drug Design, Delivery and Disposition provides an in-depth look at the use of artificial neural networks (ANN) in pharmaceutical research. With its ability to learn and self-correct in a highly complex environment, this predictive tool has tremendous potential to help researchers more effectively design, develop, and deliver successful drugs. This book illustrates how to use ANN methodologies and models with the intent to treat diseases like breast cancer, cardiac disease, and more. It contains the latest cutting-edge research, an analysis of the benefits of ANN, and relevant industry examples. As such, this book is an essential resource for academic and industry researchers across the pharmaceutical and biomedical sciences. - Written by leading academic and industry scientists who have contributed significantly to the field and are at the forefront of artificial neural network (ANN) research - Focuses on ANN in drug design, discovery and delivery, as well as adopted methodologies and their applications to the treatment of various diseases and disorders - Chapters cover important topics across the pharmaceutical process, such as ANN in structure-based drug design and the application of ANN in modern drug discovery - Presents the future potential of ANN-based strategies in biomedical image analysis and much more