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No / The purpose of this study was to determine whether artificial neural network (ANN) programs implementing different backpropagation algorithms and default settings are capable of generating equivalent highly predictive models. Three ANN packages were used: INForm, CAD/Chem and MATLAB. Twenty variants of gradient descent, conjugate gradient, quasi-Newton and Bayesian regularisation algorithms were used to train networks containing a single hidden layer of 3¿12 nodes. All INForm and CAD/Chem models trained satisfactorily for tensile strength, disintegration time and percentage dissolution at 15, 30, 45 and 60 min. Similarly, acceptable training was obtained for MATLAB models using Bayesian regularisation. Training of MATLAB models with other algorithms was erratic. This effect was attributed to a tendency for the MATLAB implementation of the algorithms to attenuate training in local minima of the error surface. Predictive models for tablet capping and friability could not be generated. The most predictive models from each ANN package varied with respect to the optimum network architecture and training algorithm. No significant differences were found in the predictive ability of these models. It is concluded that comparable models are obtainable from different ANN programs provided that both the network architecture and training algorithm are optimised. A broad strategy for optimisation of the predictive ability of an ANN model is proposed.

No / This study has investigated an artificial intelligence technology ¿ model trees ¿ as a modelling tool applied to an immediate release tablet formulation database. The modelling performance was compared with artificial neural networks that have been well established and widely applied in the pharmaceutical product formulation fields. The predictability of generated models was validated on unseen data and judged by correlation coefficient R2. Output from the model tree analyses produced multivariate linear equations which predicted tablet tensile strength, disintegration time, and drug dissolution profiles of similar quality to neural network models. However, additional and valuable knowledge hidden in the formulation database was extracted from these equations. It is concluded that, as a transparent technology, model trees are useful tools to formulators

<p>This thesis describes the application of multivariate methods in a novel approach to the formulation of tablets for direct compression. It begins with a brief historical review, followed by a basic introduction to key aspects of tablet formulation and multivariate data analysis. The bulk of the thesis is concerned with the novel approach, in which excipients were characterised in terms of multiple physical or (in most cases) spectral variables. By applying Principal Component Analysis (PCA) the descriptive variables are summarized into a few latent variables, usually termed scores or principal properties (PP’s). In this way the number of descriptive variables is dramatically reduced and the excipients are described by orthogonal continuous variables. This means that the PP’s can be used as ordinary variables in a statistical experimental design. The combination of latent variables and experimental design is termed multivariate design or experimental design in PP’s. Using multivariate design many excipients can be included in screening experiments with relatively few experiments.</p><p>The outcome of experiments designed to evaluate the effects of differences in excipient composition of formulations for direct compression is, of course, tablets with various properties. Once these properties, e.g. disintegration time and tensile strength, have been determined with standardised tests, quantitative relationships between descriptive variables and tablet properties can be established using Partial Least Squares Projections to Latent Structures (PLS) analysis. The obtained models can then be used for different purposes, depending on the objective of the research, such as evaluating the influence of the constituents of the formulation or optimisation of a certain tablet property.</p><p>Several examples of applications of the described methods are presented. Except in the first study, in which the feasibility of this approach was first tested, the disintegration time of the tablets has been studied more carefully than other responses. Additional experiments have been performed in order to obtain a specific disintegration time. Studies of mixtures of excipients with the same primary function have also been performed to obtain certain PP’s. Such mixture experiments also provide a straightforward approach to additional experiments where an interesting area of the PP space can be studied in more detail. The robustness of a formulation with respect to normal batch-to-batch variability has also been studied.</p><p>The presented approach to tablet formulation offers several interesting alternatives, for both planning and evaluating experiments.</p>

This thesis describes the application of multivariate methods in a novel approach to the formulation of tablets for direct compression. It begins with a brief historical review, followed by a basic introduction to key aspects of tablet formulation and multivariate data analysis. The bulk of the thesis is concerned with the novel approach, in which excipients were characterised in terms of multiple physical or (in most cases) spectral variables. By applying Principal Component Analysis (PCA) the descriptive variables are summarized into a few latent variables, usually termed scores or principal properties (PP’s). In this way the number of descriptive variables is dramatically reduced and the excipients are described by orthogonal continuous variables. This means that the PP’s can be used as ordinary variables in a statistical experimental design. The combination of latent variables and experimental design is termed multivariate design or experimental design in PP’s. Using multivariate design many excipients can be included in screening experiments with relatively few experiments. The outcome of experiments designed to evaluate the effects of differences in excipient composition of formulations for direct compression is, of course, tablets with various properties. Once these properties, e.g. disintegration time and tensile strength, have been determined with standardised tests, quantitative relationships between descriptive variables and tablet properties can be established using Partial Least Squares Projections to Latent Structures (PLS) analysis. The obtained models can then be used for different purposes, depending on the objective of the research, such as evaluating the influence of the constituents of the formulation or optimisation of a certain tablet property. Several examples of applications of the described methods are presented. Except in the first study, in which the feasibility of this approach was first tested, the disintegration time of the tablets has been studied more carefully than other responses. Additional experiments have been performed in order to obtain a specific disintegration time. Studies of mixtures of excipients with the same primary function have also been performed to obtain certain PP’s. Such mixture experiments also provide a straightforward approach to additional experiments where an interesting area of the PP space can be studied in more detail. The robustness of a formulation with respect to normal batch-to-batch variability has also been studied. The presented approach to tablet formulation offers several interesting alternatives, for both planning and evaluating experiments.