Emprego de redes neurais artificiais supervisionadas e não supervisionadas no estudo de parâmetros reológicos de excipientes farmacêuticos sólidos

In this paper artificial neural network (ANN) based on supervised and unsupervised algorithms were investigated for use in the study of rheological parameters of solid pharmaceutical excipients, in order to develop computational tools for manufacturing solid dosage forms. Among four supervised neural networks investigated, the best learning performance was achieved by a feedfoward multilayer perceptron whose architectures was composed by eight neurons in the input layer, sixteen neurons in the hidden layer and one neuron in the output layer. Learning and predictive performance relative to repose angle was poor while to Carr index and Hausner ratio (CI and HR, respectively) showed very good fitting capacity and learning, therefore HR and CI were considered suitable descriptors for the next stage of development of supervised ANNs. Clustering capacity was evaluated for five unsupervised strategies. Network based on purely unsupervised competitive strategies, classic "Winner-Take-All", "Frequency-Sensitive Competitive Learning" and "Rival-Penalize Competitive Learning" (WTA, FSCL and RPCL, respectively) were able to perform clustering from database, however this classification was very poor, showing severe classification errors by grouping data with conflicting properties into the same cluster or even the same neuron. On the other hand it could not be established what was the criteria adopted by the neural network for those clustering. Self-Organizing Maps (SOM) and Neural Gas (NG) networks showed better clustering capacity. Both have recognized the two major groupings of data corresponding to lactose (LAC) and cellulose (CEL). However, SOM showed some errors in classify data from minority excipients, magnesium stearate (EMG) , talc (TLC) and attapulgite (ATP). NG network in turn performed a very consistent classification of data and solve the misclassification of SOM, being the most appropriate network for classifying data of the study. The use of NG network in pharmaceutical technology was still unpublished. NG therefore has great potential for use in the development of software for use in automated classification systems of pharmaceutical powders and as a new tool for mining and clustering data in drug development

Caracterização térmica (TG/DTG, DTA, DSC, DSC-fotovisual) de Hormônios bioidênticos (estriol estradiol)

Bioidentical hormones are defined as compounds that have exactly the same chemical and molecular structure as hormones that are produced in the human body. It is believed that the use of hormones may be safer and more effective than the non-bioidentical hormones, because binding to receptors in the organism would be similar to the endogenous hormone. Bioidentical estrogens have been used in menopausal women, as an alternative to traditional hormone replacement therapy. Thermal data of these hormones are scarce in literature. Thermal analysis comprises a group of techniques that allows evaluating the physical-chemistry properties of a drug, while the drug is subjected to a controlled temperature programming. The thermal techniques are used in pharmaceutical studies for characterization of drugs, purity determination, polymorphism identification, compatibility and evaluation of stability. This study aims to characterize the bioidentical hormones estradiol and estriol through thermal techniques TG/DTG, DTA, DSC, DSC-photovisual. By the TG curves analysis was possible to calculated kinetic parameters for the samples. The kinetic data showed that there is good correlation in the different models used. For both estradiol and estriol, was found zero order reaction, which enabled the construction of the vapor pressure curves. Data from DTA and DSC curves of melting point and purity are the same of literature, showed relation with DSC-photovisual results. The analysis DTA curves showed the fusion event had the best linearity for both hormones. In the evaluation of possible degradation products, the analysis of the infrared shows no degradation products in the solid state