In the first chapter of this thesis an investigation of the influence of cellulose type II in the drug release from a coated tablet is presented. The cellulose II product can be obtained by mercerization from cellulose I and is suitable to be used in tableting as a multipurpose excipient. As this polymorphic form of cellulose also acts as a super disintegrant, carrying out an aqueous coating process could be a challenge. Compaction studies were not the focus of this work. Nevertheless, they had been executed in other prior studies cited in the references. Eudragit® RS and RL and Aquacoat® ECD, aqueous coating systems were used. Tablet cores composed of high amounts of cellulose II were sensitive to the aqueous coating. However, the process could be optimized and the source of defects eliminated adjusting parameters that reduced the contact time between tablet core and water. Drug release studies showed that methacrylic copolymers were flexible and kept the integrity of the reservoir, promoting a sustained drug release through the pores. In contrast, the ethylcellulose polymer was brittle and did rupture, promoting a burst drug release. Further studies with Aquacoat® ECD were done, in order to investigate the application of the cellulose type II as a hygroscopic and expansive agent for pulsatile drug-release systems. Many coating levels were applied in cores composed of 3 different ratios of drug:excipient. Effect of pore former in the coating layer was also analyzed. The lag time before coating rupture was directly proportional to the coating thickness, inversely proportional to the amount of pore former, and directly dependent to the level of cellulose type II present in the cores. Percolation theory provided useful explanations to describe the composition and formation of the tablet and the distribution of the pores and particles within it. It could be concluded that use of cellulose II polymorph can be a good opportunity to simply develop a modified-drug-delivery-system based on approved and well known excipients and also applying conventional and non-expensive processes. On the second chapter an investigation of the influence of drug load, liquid addition rate and batch size on the power consumption profile recorded during a high shear mixer granulation is presented. The materials used were microcrystalline cellulose and paracetamol. HPMC was added as a dry binder into the mixtures, and distilled water as the granulation liquid. Three ratios of drug:excipients were used. Percolation theory could be applied to describe the growth behaviour and granules properties. The liquid saturation was calculated and used to compare different moments of the granulation and also results among the formulations. The powder physicochemical properties strongly influenced the amount of liquid penetration and the free surface liquid which is necessary for the coalescence of primary particles and/or agglomerates, and consequently affected the power consumption profile. Thus the drug load was the most influential factor. The power profile was practically independent of the batch size. The liquid requirement was linearly dependent of the mass loaded. The liquid addition rate made a slight impact on the total amount of water used and in the granulate growth kinetics. The “in process” measurement of power consumption showed to be a reliable analytical tool for monitoring the moisture content and particles agglomeration growth. In the third chapter, in a collaboration project with University of Belgrade, the use of an Artificial Neural Network to predict characteristics of a wet granulation was studied. The initial aim was directly predict the profile of power consumed during the granulation process; using as inputs properties of the starting material as well as process variables. Data preparation is a fundamental and very important step once that the software will learn by observation of the data presented to it. As inputs formulation and process parameters were chosen, and the output was the power value (watts). As the experimental values were a time series/sequence, the order of the data is important to avoid that the software when taking a value doesn’t break the sequence, leading to a misunderstanding of the data. Different snippets (fragments of networks) were tested and the final topology that showed reduced error and better predictions was a so called Gamma-Recurrent Hybrid network. The results showed that the network was able to satisfactory predict the power consumption curve of formulations containing higher amounts of excipients. For the formulation with higher drug load (90%-wt. of paracetamol) the predictions were unsatisfactory. The experimental granulation process executed for that formulation generated more irregular power consumption curves and that wide variability among the inputted samples could result in a difficult learning from the system, and can be the reason for the lack of network precision in predicting the behaviour of this formulation. The absolute error for the prediction of the power consumption value was 76,35; relatively small compared with other systems tested. Submitting different inputs it was also tested the ability of an adaptive system to predict the relevant S2, S3, S4 and S5 points of a typical Leuenberger PC profile, and for that basic static feedforward backpropagation networks resulted in good predictions. Predicting the future output of very complex systems is a difficult task. Adaptive systems however have shown themselves, trained on the right data, quite capable of producing good predictions.