Download or read book Innovative Pharmacometric Approaches to Inform Drug Development and Clinical Use written by Zinnia P. Parra-Guillen and published by Frontiers Media SA. This book was released on 2024-02-21 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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).
Download or read book Gaussian Processes for Machine Learning written by Carl Edward Rasmussen and published by MIT Press. This book was released on 2005-11-23 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Download or read book Pharmacokinetic Pharmacodynamic Modeling and Simulation written by Peter L. Bonate and published by Springer Science & Business Media. This book was released on 2011-07-01 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a second edition to the original published by Springer in 2006. The comprehensive volume takes a textbook approach systematically developing the field by starting from linear models and then moving up to generalized linear and non-linear mixed effects models. Since the first edition was published the field has grown considerably in terms of maturity and technicality. The second edition of the book therefore considerably expands with the addition of three new chapters relating to Bayesian models, Generalized linear and nonlinear mixed effects models, and Principles of simulation. In addition, many of the other chapters have been expanded and updated.
Download or read book The Publishers Trade List Annual written by and published by . This book was released on 1965 with total page 2408 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistics of Extremes written by Jan Beirlant and published by John Wiley & Sons. This book was released on 2006-03-17 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in the statistical analysis of extreme values has flourished over the past decade: new probability models, inference and data analysis techniques have been introduced; and new application areas have been explored. Statistics of Extremes comprehensively covers a wide range of models and application areas, including risk and insurance: a major area of interest and relevance to extreme value theory. Case studies are introduced providing a good balance of theory and application of each model discussed, incorporating many illustrated examples and plots of data. The last part of the book covers some interesting advanced topics, including time series, regression, multivariate and Bayesian modelling of extremes, the use of which has huge potential.
Download or read book Pharmacometrics written by Ene I. Ette and published by John Wiley & Sons. This book was released on 2013-03-14 with total page 1236 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Applied Pharmacometrics written by Stephan Schmidt and published by Springer. This book was released on 2014-12-01 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Model Selection and Multimodel Inference written by Kenneth P. Burnham and published by Springer Science & Business Media. This book was released on 2007-05-28 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.
Download or read book Simulation for Designing Clinical Trials written by Hui Kimko and published by CRC Press. This book was released on 2002-12-12 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing more than just a comprehensive history, critical vocabulary, insightful compilation of motivations, and clear explanation of the state-of-the-art of modern clinical trial simulation, this book supplies a rigorous framework for employing simulation as an experiment, according to a predefined simulation plan, that reflects good simulation p
Download or read book Index Medicus written by and published by . This book was released on 2001-04 with total page 1716 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vols. for 1963- include as pt. 2 of the Jan. issue: Medical subject headings.
Download or read book Machine Learning Algorithms written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2017-07-24 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.
Download or read book Structural Biology in Drug Discovery written by Jean-Paul Renaud and published by John Wiley & Sons. This book was released on 2020-01-09 with total page 1437 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the most comprehensive and up-to-date overview of structure-based drug discovery covering both experimental and computational approaches, Structural Biology in Drug Discovery: Methods, Techniques, and Practices describes principles, methods, applications, and emerging paradigms of structural biology as a tool for more efficient drug development. Coverage includes successful examples, academic and industry insights, novel concepts, and advances in a rapidly evolving field. The combined chapters, by authors writing from the frontlines of structural biology and drug discovery, give readers a valuable reference and resource that: Presents the benefits, limitations, and potentiality of major techniques in the field such as X-ray crystallography, NMR, neutron crystallography, cryo-EM, mass spectrometry and other biophysical techniques, and computational structural biology Includes detailed chapters on druggability, allostery, complementary use of thermodynamic and kinetic information, and powerful approaches such as structural chemogenomics and fragment-based drug design Emphasizes the need for the in-depth biophysical characterization of protein targets as well as of therapeutic proteins, and for a thorough quality assessment of experimental structures Illustrates advances in the field of established therapeutic targets like kinases, serine proteinases, GPCRs, and epigenetic proteins, and of more challenging ones like protein-protein interactions and intrinsically disordered proteins
Download or read book Introduction to Population Pharmacokinetic Pharmacodynamic Analysis with Nonlinear Mixed Effects Models written by Joel S. Owen and published by John Wiley & Sons. This book was released on 2014-07-08 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a user-friendly, hands-on introduction to the Nonlinear Mixed Effects Modeling (NONMEM) system, the most powerful tool for pharmacokinetic / pharmacodynamic analysis. • Introduces requisite background to using Nonlinear Mixed Effects Modeling (NONMEM), covering data requirements, model building and evaluation, and quality control aspects • Provides examples of nonlinear modeling concepts and estimation basics with discussion on the model building process and applications of empirical Bayesian estimates in the drug development environment • Includes detailed chapters on data set structure, developing control streams for modeling and simulation, model applications, interpretation of NONMEM output and results, and quality control • Has datasets, programming code, and practice exercises with solutions, available on a supplementary website
Download or read book Frontiers in Aging Neuroscience Editor s Pick 2021 written by Thomas Wisniewski and published by Frontiers Media SA. This book was released on 2021-08-03 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonlinear Models for Repeated Measurement Data written by Marie Davidian and published by Routledge. This book was released on 2017-11-01 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonlinear measurement data arise in a wide variety of biological and biomedical applications, such as longitudinal clinical trials, studies of drug kinetics and growth, and the analysis of assay and laboratory data. Nonlinear Models for Repeated Measurement Data provides the first unified development of methods and models for data of this type, with a detailed treatment of inference for the nonlinear mixed effects and its extensions. A particular strength of the book is the inclusion of several detailed case studies from the areas of population pharmacokinetics and pharmacodynamics, immunoassay and bioassay development and the analysis of growth curves.
Download or read book Clinical Trial Data Analysis Using R and SAS written by Ding-Geng (Din) Chen and published by CRC Press. This book was released on 2017-06-01 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Review of the First Edition "The goal of this book, as stated by the authors, is to fill the knowledge gap that exists between developed statistical methods and the applications of these methods. Overall, this book achieves the goal successfully and does a nice job. I would highly recommend it ...The example-based approach is easy to follow and makes the book a very helpful desktop reference for many biostatistics methods."—Journal of Statistical Software Clinical Trial Data Analysis Using R and SAS, Second Edition provides a thorough presentation of biostatistical analyses of clinical trial data with step-by-step implementations using R and SAS. The book’s practical, detailed approach draws on the authors’ 30 years’ experience in biostatistical research and clinical development. The authors develop step-by-step analysis code using appropriate R packages and functions and SAS PROCS, which enables readers to gain an understanding of the analysis methods and R and SAS implementation so that they can use these two popular software packages to analyze their own clinical trial data. What’s New in the Second Edition Adds SAS programs along with the R programs for clinical trial data analysis. Updates all the statistical analysis with updated R packages. Includes correlated data analysis with multivariate analysis of variance. Applies R and SAS to clinical trial data from hypertension, duodenal ulcer, beta blockers, familial andenomatous polyposis, and breast cancer trials. Covers the biostatistical aspects of various clinical trials, including treatment comparisons, time-to-event endpoints, longitudinal clinical trials, and bioequivalence trials.
Download or read book Comprehensive Medicinal Chemistry III written by and published by Elsevier. This book was released on 2017-06-03 with total page 4609 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive Medicinal Chemistry III, Eight Volume Set provides a contemporary and forward-looking critical analysis and summary of recent developments, emerging trends, and recently identified new areas where medicinal chemistry is having an impact. The discipline of medicinal chemistry continues to evolve as it adapts to new opportunities and strives to solve new challenges. These include drug targeting, biomolecular therapeutics, development of chemical biology tools, data collection and analysis, in silico models as predictors for biological properties, identification and validation of new targets, approaches to quantify target engagement, new methods for synthesis of drug candidates such as green chemistry, development of novel scaffolds for drug discovery, and the role of regulatory agencies in drug discovery. Reviews the strategies, technologies, principles, and applications of modern medicinal chemistry Provides a global and current perspective of today's drug discovery process and discusses the major therapeutic classes and targets Includes a unique collection of case studies and personal assays reviewing the discovery and development of key drugs
