Pharmacometrics is the science of interpreting and describing pharmacology in a quantitative fashion. The pharmaceutical industry is integrating pharmacometrics into its drug development program, but there is a lack of and need for experienced pharmacometricians since fewer and fewer academic programs exist to train them. Pharmacometrics: The Science of Quantitative Pharmacology lays out the science of pharmacometrics and its application to drug development, evaluation, and patient pharmacotherapy, providing a comprehensive set of tools for the training and development of pharmacometricians. Edited and written by key leaders in the field, this flagship text on pharmacometrics: Integrates theory and practice to let the reader apply principles and concepts. Provides a comprehensive set of tools for training and developing expertise in the pharmacometric field. Is unique in including computer code information with the examples. This volume is an invaluable resource for all pharmacometricians, statisticians, teachers, graduate and undergraduate students in academia, industry, and regulatory agencies.

This comprehensive volume provides an update on the current state of pharmacometrics in drug development. It consists of nineteen chapters all written by leading scientists from the pharmaceutical industry, regulatory agencies and academia. After an introduction of the basic pharmacokinetic and pharmacodynamic concepts of pharmacometrics in drug development, the book presents numerous examples of specific applications that utilize pharmacometrics with modeling and simulations over a variety of therapeutic areas, including pediatrics, diabetes, obesity, infections, psychiatrics, Alzheimer’s disease, and dermatology, among others. The examples illustrate how results from all phases of drug development can be integrated in a more timely and cost-effective process. Applying pharmacometric decision tools during drug development can allow objective, data-based decision making. At the same time, the process can identify redundant or unnecessary experiments as well as some costly clinical trials that can be avoided. In addition to cost saving by expedited development of successful drug candidates, pharmacometrics has an important economic impact in drug product selection. Unsuccessful drug candidates can be identified early and discontinued without expending efforts required for additional studies and allocating limited resources. Hence, pharmacometric modeling and simulation has become a powerful tool to bring new and better medications to the patient at a faster pace and with greater probability of success.

Pharmacometrics represents a strategy to optimize and rationalize decision-making process integrating information on drug behavior, pharmacological response, and disease progression both in the drug development phases and in their clinical use. Pharmacometrics focuses on characterizing the pharmacokinetic and pharmacodynamic behavior of one or several active ingredients through the development of mathematical and statistical models that allow characterizing both the average behavior in the population and the different sources of variability. Currently, pharmacometrics has transformed drug development and therapeutic use paradigm, which yield to the recognition by the main regulatory agencies (FDA, EMA, and PMDA).

This contributed volume presents an overview of concepts, methods, and applications used in several quantitative areas of drug research, development, and marketing. Chapters bring together the theories and applications of various disciplines, allowing readers to learn more about quantitative fields, and to better recognize the differences between them. Because it provides a thorough overview, this will serve as a self-contained resource for readers interested in the pharmaceutical industry, and the quantitative methods that serve as its foundation. Specific disciplines covered include: Biostatistics Pharmacometrics Genomics Bioinformatics Pharmacoepidemiology Commercial analytics Operational analytics Quantitative Methods in Pharmaceutical Research and Development is ideal for undergraduate students interested in learning about real-world applications of quantitative methods, and the potential career options open to them. It will also be of interest to experts working in these areas.

This book focuses on the new discipline of translational medicine as it pertains to drug development within the pharmaceutical and biotechnology industry. It is essential for anyone interested in translational medicine from a variety of backgrounds: university institutes, medical schools, pharmaceutical companies and drug development researchers and decision-makers.

Written by dedicated and active professionals from different areas of the pharmaceutical, biomedical, and medtech sectors, this book provides information on job and career opportunities in various life sciences industries. It also contains useful tips to launch your own startup. The pharmaceutical, biomedical and medical technology sectors offer a wide range of employment opportunities to talented and motivated young graduates. However, many of these employment prospects are not well known to early career scientists, who concentrate primarily on the scientific and academic content of their fields of interest. The book is divided into five parts: Part 1 provides an academic perspective that focuses on the specific preparation required in the final years of study to embark on a successful career in the pharmaceutical and biomedical industries. In Part 2, industry experts discuss employment possibilities all along the drug or product life cycle, from discovery research and development to commercialisation. Part 3 follows, highlighting opportunities in support functions such as regulatory affairs or quality assurance. Part 4 focuses on additional opportunities in the wider biomedical sector, while Part 5 contains practical tips and training opportunities for entering the pharmaceutical and biomedical industries. In the epilogue, the authors reflect on this fascinating field and its career prospects. The book offers a multidisciplinary perspective on career opportunities in the pharmaceutical and biomedical industry to a wide range of students and young life scientists.

There are many steps on the road from discovery of an anticancer drug to securing its final approval by the Food and Drug Administration. In this thoroughly updated and expanded second edition of the Handbook of Anticancer Pharmacokinetics and Pharmacodynamics, leading investigators synthesize an invaluable overview of the experimental and clinical processes of anticancer drug development, creating a single indispensable reference that covers all the steps from the identification of cancer-specific molecular targets to screening techniques and the development and validation of bioanalytical methods to clinical trial design and all phases of clinical trials. The authors have included new material on phase 0 trials in oncology, organ dysfunction trials, drug formulations and their impact on anticancer drug PK/PD including strategies to improve drug delivery, pharmacogenomics and cancer therapy, high throughput platforms in drug metabolism and transport pharmacogenetics, imaging in drug development and nanotechnology in cancer. Authoritative and up-to-date, Handbook of Anticancer Pharmacokinetics and Pharmacodynamics, 2nd Edition provides in one comprehensive and highly practical volume a detailed step-by-step guide to the successful design and approval of anticancer drugs. Road map to anticancer drug development from discovery to NDA submission Discussion of molecular targets and preclinical screening Development and validation of bioanalytical methods Chapters on clinical trial design and phase 0, I, II, III clinical trials Pharmacokinetics, pharmacodynamics, pharmacogenomics, and pharmacogenetics of anticancer agents Review of the drug development process from both laboratory and clinical perspectives New technological advances in imaging, high throughput platforms, and nanotechnology in anticancer drug development

This edition includes both updates and new uses and issues concerning CTS, along with case studies of how clinical trial simulations are being applied in various therapeutic and application areas. Importantly, the book expands on the utility of CTS for informing decisions during drug development and regulatory review. Each chapter author was selected on the basis of demonstrated expertise in state-of-the-art application of CTS. The target audience for this volume includes researchers and scientists who wish to consider use of simulations in the design, analysis, or regulatory review and guidance of clinical trials. This book does not embrace all aspects of trial design, nor is it intended as a complete recipe for using computers to design trials. Rather, it is an information source that enables the reader to gain understanding of essential background and knowledge for practical applications of simulation for clinical trial design and analysis. It is assumed that the reader has a working understanding of pharmacokinetics and pharmacodynamics, modeling, pharmacometric analyses, and/or the drug development and regulatory processes.

Artificial Intelligence for Drug Product Lifecycle Applications explains the use of artificial intelligence (AI) in drug discovery and development paths, including the clinical and postapproval phases. This book gives methods for each of the drug development steps, from the fundamentals to postapproval drug product. AI is a synergistic assembly of enhanced optimization strategies with particular applications in pharmaceutical development and advanced tools for promoting cost-effectiveness throughout the drug lifecycle. Specifically, AI brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patients comply with their treatments.Accelerated pharmaceutical development and drug product approval rates will enable larger profits from patent-protected market exclusivity. This book offers the tools and knowledge to create the right AI strategy to extend the landscape of AI applications across the drug lifecycle. It is especially useful for pharmaceutical scientists, health care professionals, and regulatory scientists, as well as advanced students and postgraduates actively involved in pharmaceutical product and process development involving the use of artificial intelligence in drug delivery applications. - Classifies AI methodologies and application examples into different categories representing the various steps of the drug development cycle - Combines timely literature review with clear artworks to improve understanding - Examines deep learning and machine learning in drug discovery