Drug discovery increasingly requires a common understanding by researchers of the many and diverse factors that go into the making of new medicines. The scientist entering the field will immediately face important issues for which his education may not have prepared him: project teams, patent law, consultants, target product profiles, industry trends, Gantt charts, target validation, pharmacokinetics, proteomics, phenotype assays, biomarkers, and many other unfamiliar topics for which a basic understanding must somehow be obtained. Even the more experienced scientist can find it frustratingly difficult to get an overview of the many factors involved in modern drug discovery and often only after years of exploring does a whole and integrated picture emerge in the mind of the researcher.Real World Drug Discovery: A Chemist's Guide to Biotech and Pharmaceutical Research presents this kind of map of the landscape of drug discovery. In a single, readable volume it outlines processes and explains essential concepts and terms for the recent science graduate wondering what to expect in pharma or biotech, the medicinal chemist seeking a broader and more timely understanding of the industry, or the contractor or collaborator whose understanding of the commercial drug discovery process could increase the value of his contribution to it. - Interviews with well-known experts in many of the fields involved, giving insightful comments from authorities on many of the sub-disciplines important to cutting edge drug discovery. - Helpful suggestions gleaned from years of experience in biotech and pharma, which represents a repository drug discovery "lore" not previously available in any book. - "Periodic Table of Drugs" listing current top-selling drugs arranged by target and laid out so that structural similarities and differences are plain and clear. - Extensive use of diagrams to illustrate concepts like biotech startup models, preteomic profiling for target identification, Gantt charts for project planning, etc.

Real-world evidence (RWE) has been at the forefront of pharmaceutical innovations. It plays an important role in transforming drug development from a process aimed at meeting regulatory expectations to an operating model that leverages data from disparate sources to aid business, regulatory, and healthcare decision making. Despite its many benefits, there is no single book systematically covering the latest development in the field. Written specifically for pharmaceutical practitioners, Real-World Evidence in Drug Development and Evaluation, presents a wide range of RWE applications throughout the lifecycle of drug product development. With contributions from experienced researchers in the pharmaceutical industry, the book discusses at length RWE opportunities, challenges, and solutions. Features Provides the first book and a single source of information on RWE in drug development Covers a broad array of topics on outcomes- and value-based RWE assessments Demonstrates proper Bayesian application and causal inference for real-world data (RWD) Presents real-world use cases to illustrate the use of advanced analytics and statistical methods to generate insights Offers a balanced discussion of practical RWE issues at hand and technical solutions suitable for practitioners with limited data science expertise

This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.

This handbook provides the first-ever inside view of today's integrated approach to rational drug design. Chemoinformatics experts from large pharmaceutical companies, as well as from chemoinformatics service providers and from academia demonstrate what can be achieved today by harnessing the power of computational methods for the drug discovery process. With the user rather than the developer of chemoinformatics software in mind, this book describes the successful application of computational tools to real-life problems and presents solution strategies to commonly encountered problems. It shows how almost every step of the drug discovery pipeline can be optimized and accelerated by using chemoinformatics tools -- from the management of compound databases to targeted combinatorial synthesis, virtual screening and efficient hit-to-lead transition. An invaluable resource for drug developers and medicinal chemists in academia and industry.

The process of drug discovery and development is a complex multistage logistics project spanned over 10-15 years with an average budget exceeding 1 billion USD. Starting with target identification and synthesizing anywhere between 10k to 15k synthetic compounds to potentially obtain the final drug that reaches the market involves a complicated maze with multiple inter- and intra-operative fields. Topics described in this book emphasize the progresses in computational applications, pharmacokinetics advances, and molecular modeling developments. In addition the book also contains special topics describing target deorphaning in Mycobacterium tuberculosis, therapy treatment of some rare diseases, and developments in the pediatric drug discovery process.

This encyclopedia covers the definitions, concepts, methods, theories, and application of evidence-based pharmaceutical public health and health services research. It highlights why and how this field has a significant impact on healthcare. The work aims to synthesize baseline knowledge as well as the latest and cutting-edge research-based information. The encyclopedia collates information on public health, health services research, evidence-based pharmacy practice and its impacts on patients, decision-makers and consumers. This reference work discusses all aspects of policy and practice decisions on medicines use, access and pharmacy services by covering broad aspects related to pharmacy practice, public health and health services research. The aim is to develop high-quality content, which will be a must-read and be used as a reference source at all pharmacy and medical schools in the world. The health services research investigates the impact of social factors, organizational policies, financing systems, medical technologies and personal influence on access, quality and cost of healthcare concerning the quality of life of the patients. This reference work fundamentally promotes the evidence-based evaluation of healthcare services and thus will improve the better access and delivery of healthcare services. Also, pharmacy, medical and health services students and researchers need a broad understanding of pharmaceutical public health, evidence-based approaches to delivering care, changing professional and patient behavior and undertaking research in these areas. In general, there is a need to build research capacity and capability in the pharmacy profession. EDITOR-IN-CHIEF: Professor Zaheer-Ud-Din Babar, University of Huddersfield SECTION EDITORS: Filipa Alves da Costa, University of LisbonZubin Austin, University of TorontoDalia Dawood, National Institute for Health and Care Excellence Andy Gray, University of Kwa Zulu-NatalRachele Hendricks-Sturrup, Duke Margolis Center for Health PolicyJason Hsu, Taiwan Medical UniversityRabia Hussain, Universiti Sains MalaysiaChristine Y. Lu, Harvard Medical School and Harvard Pilgrim Health Care InstituteMohamed Izham Mohamed Ibrahim, Qatar UniversityPrasad Nishtala, University of BathDerek Charles Stewart, College of Pharmacy, Qatar University Fatima Suleman, University of Kwa Zulu-NatalZaheer-Ud-Din Babar, University of Huddersfield

The confluence of big data, artificial intelligence (AI), and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R & D, emerging applications of big data, AI and ML in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations. Features Provides a comprehensive review of challenges and opportunities as related to the applications of big data, AI, and ML in the entire spectrum of drug R & D Discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval Offers a balanced approach to data science organization build Presents real-world examples of AI-powered solutions to a host of issues in the lifecycle of drug development Affords sufficient context for each problem and provides a detailed description of solutions suitable for practitioners with limited data science expertise

Burger’s Medicinal Chemistry, Drug Discovery and Development Explore the freshly updated flagship reference for medicinal chemists and pharmaceutical professionals The newly revised eighth edition of the eight-volume Burger’s Medicinal Chemistry, Drug Discovery and Development is the latest installment in this celebrated series covering the entirety of the drug development and discovery process. With the addition of expert editors in each subject area, this eight-volume set adds 35 chapters to the extensive existing chapters. New additions include analyses of opioid addiction treatments, antibody and gene therapy for cancer, blood-brain barrier, HIV treatments, and industrial-academic collaboration structures. Along with the incorporation of practical material on drug hunting, the set features sections on drug discovery, drug development, cardiovascular diseases, metabolic diseases, immunology, cancer, anti-Infectives, and CNS disorders. The text continues the legacy of previous volumes in the series by providing recognized, renowned, authoritative, and comprehensive information in the area of drug discovery and development while adding cutting-edge new material on issues like the use of artificial intelligence in medicinal chemistry. Included: Volume 1: Methods in Drug Discovery, edited by Kent D. Stewart Volume 2: Discovering Lead Molecules, edited by Kent D. Stewart Volume 3: Drug Development, edited by Ramnarayan S. Randad and Michael Myers Volume 4: Cardiovascular, Endocrine, and Metabolic Diseases, edited by Scott D. Edmondson Volume 5: Pulmonary, Bone, Immunology, Vitamins, and Autocoid Therapeutic Agents, edited by Bryan H. Norman Volume 6: Cancer, edited by Barry Gold and Donna M. Huryn Volume 7: Anti-Infectives, edited by Roland E. Dolle Volume 8: CNS Disorders, edited by Richard A. Glennon Perfect for research departments in the pharmaceutical and biotechnology industries, Burger’s Medicinal Chemistry, Drug Discovery and Development can be used by graduate students seeking a one-stop reference for drug development and discovery and deserves its place in the libraries of biomedical research institutes, medical, pharmaceutical, and veterinary schools.

In Real-World Evidence in the Pharmaceutical Landscape, life science industry experts Sunil Dravida and his co-authors have developed the first comprehensive overview of its kind on Real-World Data (RWD) in the pharmaceutical industry. The authors examine the challenges and opportunities in applying real-world data along the pharmaceutical continuum, from clinical development to medical affairs, health economics and outcomes, and marketing. They address the difficulties identifying the suitable data sources, ensuring compliance with privacy, security and regulatory requirements, and the big job of translating data into Real-World Evidence (RWE) to generate meaningful insights that can improve decision making by stakeholders and measurable outcomes that can enhance people’s health and well-being. This book is a must-read for those in the pharmaceutical industry involved with RWD, which includes just about every role, as healthcare is now dominated by the need for high-quality data that can enable better decision-making. This book is especially critical for those designing and leading RWD Centers of Excellence in pharmaceutical companies and the service providers supporting the RWD ecosystem.