Multivariate Analysis In The Pharmaceutical Industry
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📒Multivariate Analysis In The Pharmaceutical Industry ✍ Ana Patricia Ferreira
✏Multivariate Analysis in the Pharmaceutical Industry Book Summary : Multivariate Analysis in the Pharmaceutical Industry provides industry practitioners with guidance on multivariate data methods and their applications over the lifecycle of a pharmaceutical product, from process development, to routine manufacturing, focusing on the challenges specific to each step. It includes an overview of regulatory guidance specific to the use of these methods, along with perspectives on the applications of these methods that allow for testing, monitoring and controlling products and processes. The book seeks to put multivariate analysis into a pharmaceutical context for the benefit of pharmaceutical practitioners, potential practitioners, managers and regulators. Users will find a resources that addresses an unmet need on how pharmaceutical industry professionals can extract value from data that is routinely collected on products and processes, especially as these techniques become more widely used, and ultimately, expected by regulators. Targets pharmaceutical industry practitioners and regulatory staff by addressing industry specific challenges Includes case studies from different pharmaceutical companies and across product lifecycle of to introduce readers to the breadth of applications Contains information on the current regulatory framework which will shape how multivariate analysis (MVA) is used in years to come
📒Chemical Engineering In The Pharmaceutical Industry ✍ Mary T. am Ende
✏Chemical Engineering in the Pharmaceutical Industry Book Summary : A guide to the important chemical engineering concepts for the development of new drugs, revised second edition The revised and updated second edition of Chemical Engineering in the Pharmaceutical Industry offers a guide to the experimental and computational methods related to drug product design and development. The second edition has been greatly expanded and covers a range of topics related to formulation design and process development of drug products. The authors review basic analytics for quantitation of drug product quality attributes, such as potency, purity, content uniformity, and dissolution, that are addressed with consideration of the applied statistics, process analytical technology, and process control. The 2nd Edition is divided into two separate books: 1) Active Pharmaceutical Ingredients (API’s) and 2) Drug Product Design, Development and Modeling. The contributors explore technology transfer and scale-up of batch processes that are exemplified experimentally and computationally. Written for engineers working in the field, the book examines in-silico process modeling tools that streamline experimental screening approaches. In addition, the authors discuss the emerging field of continuous drug product manufacturing. This revised second edition: Contains 21 new or revised chapters, including chapters on quality by design, computational approaches for drug product modeling, process design with PAT and process control, engineering challenges and solutions Covers chemistry and engineering activities related to dosage form design, and process development, and scale-up Offers analytical methods and applied statistics that highlight drug product quality attributes as design features Presents updated and new example calculations and associated solutions Includes contributions from leading experts in the field Written for pharmaceutical engineers, chemical engineers, undergraduate and graduation students, and professionals in the field of pharmaceutical sciences and manufacturing, Chemical Engineering in the Pharmaceutical Industry, Second Edition contains information designed to be of use from the engineer's perspective and spans information from solid to semi-solid to lyophilized drug products.
📒Chemical Engineering In The Pharmaceutical Industry ✍ David J. am Ende
✏Chemical Engineering in the Pharmaceutical Industry Book Summary : This book deals with various unique elements in the drugdevelopment process within chemical engineering science andpharmaceutical R&D. The book is intended to be used as aprofessional reference and potentially as a text book reference inpharmaceutical engineering and pharmaceutical sciences. Many of theexperimental methods related to pharmaceutical process developmentare learned on the job. This book is intended to provide many ofthose important concepts that R&D Engineers and manufacturingEngineers should know and be familiar if they are going to besuccessful in the Pharmaceutical Industry. These include basicanalytics for quantitation of reaction components– oftenskipped in ChE Reaction Engineering and kinetics books. In additionChemical Engineering in the Pharmaceutical Industryintroduces contemporary methods of data analysis for kineticmodeling and extends these concepts into Quality by Designstrategies for regulatory filings. For the current professionals,in-silico process modeling tools that streamlineexperimental screening approaches is also new and presented here.Continuous flow processing, although mainstream for ChE, is uniquein this context given the range of scales and the complex economicsassociated with transforming existing batch-plant capacity. The book will be split into four distinct yet related parts.These parts will address the fundamentals of analytical techniquesfor engineers, thermodynamic modeling, and finally provides anappendix with common engineering tools and examples of theirapplications.
📒Pharmaceutical Quality By Design ✍ Walkiria S. Schlindwein
✏Pharmaceutical Quality by Design Book Summary : A practical guide to Quality by Design for pharmaceutical product development Pharmaceutical Quality by Design: A Practical Approach outlines a new and proven approach to pharmaceutical product development which is now being rolled out across the pharmaceutical industry internationally. Written by experts in the field, the text explores the QbD approach to product development. This innovative approach is based on the application of product and process understanding underpinned by a systematic methodology which can enable pharmaceutical companies to ensure that quality is built into the product. Familiarity with Quality by Design is essential for scientists working in the pharmaceutical industry. The authors take a practical approach and put the focus on the industrial aspects of the new QbD approach to pharmaceutical product development and manufacturing. The text covers quality risk management tools and analysis, applications of QbD to analytical methods, regulatory aspects, quality systems and knowledge management. In addition, the book explores the development and manufacture of drug substance and product, design of experiments, the role of excipients, multivariate analysis, and include several examples of applications of QbD in actual practice. This important resource: Covers the essential information about Quality by Design (QbD) that is at the heart of modern pharmaceutical development Puts the focus on the industrial aspects of the new QbD approach Includes several illustrative examples of applications of QbD in practice Offers advanced specialist topics that can be systematically applied to industry Pharmaceutical Quality by Design offers a guide to the principles and application of Quality by Design (QbD), the holistic approach to manufacturing that offers a complete understanding of the manufacturing processes involved, in order to yield consistent and high quality products.
📒Statistics In The Pharmaceutical Industry 3rd Edition ✍ C. Ralph Buncher
✏Statistics In the Pharmaceutical Industry 3rd Edition Book Summary : The growth of the pharmaceutical industry over the past decade is astounding, but the impact of this growth on statistics is somewhat confusing. While software has made analysis easier and more efficient, regulatory bodies now demand deeper and more complex analyses, and pharmacogenetic/genomic studies serve up an entirely new set of challenges. For more than two decades, Statistics in the Pharmaceutical Industry has been the definitive guide to sorting through the challenges in the industry, and this Third Edition continues that tradition. Updated and expanded to reflect the most recent trends and developments in the field, Statistics in the Pharmaceutical Industry, Third Edition presents chapters written by experts from both regulatory agencies and pharmaceutical companies who discuss everything from experimental design to post-marketing studies. This approach sheds light on what regulators consider acceptable methodologies and what methods have proven successful for industrial statisticians. Both new and revised chapters reflect the increasingly global nature of the industry as represented by authors from Japan and Europe, the increasing trend toward non-inferiority/equivalence testing, adaptive design in clinical trials, global harmonization of regulatory standards, and multiple comparison studies. The book also examines the latest considerations in anti-cancer studies. Statistics in the Pharmaceutical Industry, Third Edition demystifies the approval process by combining regulatory and industrial points of view, making it a must-read for anyone performing statistical analysis at any point in the drug approval process.
✏11th International Symposium on Process Systems Engineering PSE2012 Book Summary :
📒Multivariate Analysis For The Biobehavioral And Social Sciences ✍ Bruce L. Brown
✏Multivariate Analysis for the Biobehavioral and Social Sciences Book Summary : An insightful guide to understanding and visualizing multivariatestatistics using SAS®, STATA®, and SPSS® Multivariate Analysis for the Biobehavioral and Social Sciences:A Graphical Approach outlines the essential multivariate methodsfor understanding data in the social and biobehavioral sciences.Using real-world data and the latest software applications, thebook addresses the topic in a comprehensible and hands-on manner,making complex mathematical concepts accessible to readers. The authors promote the importance of clear, well-designedgraphics in the scientific process, with visual representationsaccompanying the presented classical multivariate statisticalmethods . The book begins with a preparatory review of univariatestatistical methods recast in matrix notation, followed by anaccessible introduction to matrix algebra. Subsequent chaptersexplore fundamental multivariate methods and related key concepts,including: Factor analysis and related methods Multivariate graphics Canonical correlation Hotelling's T-squared Multivariate analysis of variance (MANOVA) Multiple regression and the general linear model (GLM) Each topic is introduced with a research-publication case studythat demonstrates its real-world value. Next, the question "how doyou do that?" is addressed with a complete, yet simplified,demonstration of the mathematics and concepts of the method.Finally, the authors show how the analysis of the data is performedusing Stata®, SAS®, and SPSS®. The discussedapproaches are also applicable to a wide variety of modernextensions of multivariate methods as well as modern univariateregression methods. Chapters conclude with conceptual questionsabout the meaning of each method; computational questions that testthe reader's ability to carry out the procedures on simpledatasets; and data analysis questions for the use of the discussedsoftware packages. Multivariate Analysis for the Biobehavioral and Social Sciencesis an excellent book for behavioral, health, and social sciencecourses on multivariate statistics at the graduate level. The bookalso serves as a valuable reference for professionals andresearchers in the social, behavioral, and health sciences whowould like to learn more about multivariate analysis and itsrelevant applications.
📒Introduction To Multivariate Analysis ✍ Sadanori Konishi
✏Introduction to Multivariate Analysis Book Summary : Select the Optimal Model for Interpreting Multivariate Data Introduction to Multivariate Analysis: Linear and Nonlinear Modeling shows how multivariate analysis is widely used for extracting useful information and patterns from multivariate data and for understanding the structure of random phenomena. Along with the basic concepts of various procedures in traditional multivariate analysis, the book covers nonlinear techniques for clarifying phenomena behind observed multivariate data. It primarily focuses on regression modeling, classification and discrimination, dimension reduction, and clustering. The text thoroughly explains the concepts and derivations of the AIC, BIC, and related criteria and includes a wide range of practical examples of model selection and evaluation criteria. To estimate and evaluate models with a large number of predictor variables, the author presents regularization methods, including the L1 norm regularization that gives simultaneous model estimation and variable selection. For advanced undergraduate and graduate students in statistical science, this text provides a systematic description of both traditional and newer techniques in multivariate analysis and machine learning. It also introduces linear and nonlinear statistical modeling for researchers and practitioners in industrial and systems engineering, information science, life science, and other areas.
✏An Analysis of Competitive Positioning Strategies in the U S Ethical Pharmaceutical Industry Book Summary :
📒Operational Tools In The Management Of Financial Risks ✍ Constantin Zopounidis
✏Operational Tools in the Management of Financial Risks Book Summary : This book presents a set of new, innovative mathematical modeling tools for analyzing financial risk. Operational Tools in the Management of Financial Risks presents an array of new tools drawn from a variety of research areas, including chaos theory, expert systems, fuzzy sets, neural nets, risk analysis, stochastic programming, and multicriteria decision making. Applications cover, but are not limited to, bankruptcy, credit granting, capital budgeting, corporate performance and viability, portfolio selection/management, and country risk. The book is organized into five sections. The first section applies multivariate data and multicriteria analyses to the problem of portfolio selection. Articles in this section combine classical approaches with newer methods. The second section expands the analysis in the first section to a variety of financial problems: business failure, corporate performance and viability, bankruptcy, etc. The third section examines the mathematical programming techniques including linear, dynamic, and stochastic programming to portfolio managements. The fourth section introduces fuzzy set and artificial intelligence techniques to selected types of financial decisions. The final section explores the contribution of several multicriteria methodologies in the assessment of country financial risk. In total, this book is a systematic examination of an emerging methodology for managing financial risk in business.