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.
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 introduces the concept of computational fluid dynamics (CFD) and its applications in pharmaceutical technology. Basic theoretical explanations on the mathematics of fluid flow and numerical grids are provided. CFD is a versatile tool that is mainly used in complex dynamical process characterization. Examples of CFD applications in development of inhalers, analysis of dissolution apparatus hydrodynamics, and fluidized bed process simulations are presented.
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