This chapter provides a basic theoretical background on experimental design application and interpretation. Techniques described include screening designs, full and fractional factorial designs, Plackett–Burman design, D-optimal designs, response surface methodology, central composite designs, Box–Behnken design, and mixture designs, etc. The reader will be introduced to the experimental domains covered by specific design, making it easier to select the one appropriate for the problem. After theoretical introduction, a number of illustrative examples of design of experiments application in the field of pharmaceutical technology are presented.

Computer-Aided Applications in Pharmaceutical Technology: Delivery Systems, Dosage Forms, and Pharmaceutical Unit Operations, Second Edition covers the fundamentals of experimental design application and interpretation in pharmaceutical technology, chemometric methods with an emphasis on their applications in process control, neural computing, data science, computer-aided biopharmaceutical characterization, as well as the application of computational fluid dynamics in pharmaceutical technology. Completely updated, the book introduces the theory and practice of computational tools through new case studies. Chapters cover Quality by Design in pharmaceutical development, overview data mining methodologies, present computer-aided formulation development, cover experimental design applications, and much more. Presents a comprehensive review of the current state of the art on various computer-aided applications in pharmaceutical technology Includes case studies to facilitate understanding of various concepts in computer-aided applications Covers applications such as the development of dosage forms and/or delivery systems, pharmaceutical unit operations, and relevant physiologically based pharmacokinetic simulations

This chapter introduces the concept of formulation development assisted by computer applications. Development and optimization of various types of pharmaceutical emulsions microemulsions, self-microemulsifying systems, and double emulsions are presented. Illustrative examples are presented to demonstrate the ability of computer-aided tools to facilitate formulation development. Various techniques, such as design of experiments and artificial neural networks, are implemented for optimization of the formulation and/or processing parameters. Furthermore, some of the critical quality attributes and processing parameters are optimized simultaneously. The examples presented should serve as the foundation for the future quality-by-design development of pharmaceutical emulsion and (self) microemulsion formulations.

This first chapter introduces the concept of quality-by-design (QbD) and its role in pharmaceutical product development. QbD assures the quality of a pharmaceutical product through scientific development and risk management tools, and will eventually enable real-time release, regardless of the formulation type. Several guidelines on pharmaceutical development, quality risk management, and pharmaceutical quality systems are presented that are applicable throughout the product lifecycle. Design space appointment and control strategies for risk management are introduced. The meaning of the QbD concept is presented from both regulatory and manufacturers’ points of view. Several illustrative examples are provided to facilitate the understanding of the QbD concept and ease of its application.

This chapter presents a review of the possible applications of methods based on neural computing in pharmaceutical products and process development. Some of the methods described are used for classification purposes, whereas others can be applied to modeling and optimization, or even induction of rules. Basic concepts of each method are theoretically described, followed by examples of their application in pharmaceutical technology. A theoretical background aims to provide a better understanding of the methods and is based upon their most important features. Examples should encourage the reader to embrace the above-mentioned methods and use them to complement conventional statistical methods for classification and regression.

This chapter introduces the concept of gastrointestinal absorption simulation using in silico methodology. Parameters used for model construction and the sensitivity predicted pharmacokinetic responses to various input parameters are described. Virtual trials for in silico modeling of drug absorption are presented. The influence of food on drug absorption, as well as correlation between the in vitro and in vivo results, are also addressed, followed by biowaiver considerations. Numerous examples are provided throughout the chapter.

This chapter provides a basic theoretical background on chemometrics and chemometric methods for the analysis of multivariate data. Multivariate data analysis is essential for both product and process development and optimization. Depending on the problem studied, classification and/or regression multivariate methods are applied for data analysis. Different supervised and unsupervised methods for classification and regression are presented, followed by examples of their application in pharmaceutical technology. Some of the methods described include principal component analysis, various supervised classification methods, multiple linear regression, principal component regression, partial least squares regression, support vector machines, etc.