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Managing the Drug Discovery Process, Second Edition thoroughly examines the current state of pharmaceutical research and development by providing chemistry-based perspectives on biomedical research, drug hunting and innovation. The book also considers the interplay of stakeholders, consumers, and the drug firm with attendant factors, including those that are technical, legal, economic, demographic, political, social, ecological, and infrastructural. Since drug research can be a high-risk, high-payoff industry, it is important to researchers to effectively and strategically manage the drug discovery process. This new edition takes a closer look at increasing pre-approval costs for new drugs and examines not only why these increases occur, but also how they can be overcome to ensure a robust pharmacoeconomic future. All chapters have been revised and expanded with new discussions on genetic and epigenetic advances including CRISPR, exosomes, immuno-oncology approaches, including checkpoint inhibitors and CAR-T, antibiotics, entrepreneurship, and AI. Written in an engaging manner and including memorable insights, this book is aimed at redirecting the drug discovery process to make it more efficient and cost-effective in order to achieve the goal of saving countless more lives through science. A valuable and compelling resource, this is a must-read for all students and researchers in academia and the pharmaceutical industry. Considers drug discovery in multiple R&D venues, including big pharma, large biotech, start-up ventures, academia, and nonprofit research institutes 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 gene editing technologies such as CRISPR, personalized medicine, ethical and moral issues, data visualization and statistical representations, AI and machine learning as well as completely updating all chapters
