Estudo de pré-formulação para dose fixa combinada dos tuberculostáticos rifampicina, isoniazida, pirazinamida e etambutol (4 em 1)

According to the global framework regarding new cases of tuberculosis, Brazil appears at the 18th place. Thus, the Ministry of Health has defined this disease as a priority in the governmental policies. As a consequence, studies concerning treatment and prevention have increased. Fixed-dose combination formulations (FDC) are recognized as beneficial and are recommended by WHO, but they present instability and loss on rifampicin bioavailability. The main purpose of this work was to carry out a pre-formulation study with the schedule 1 tuberculosis treatment drugs: rifampicin, isoniazid, pyrazinamide and ethambutol and pharmaceutical excipients (lactose, cellulose, magnesium stearate and talc), in order to develop an FDC product (150 mg of rifampicin + 75 mg of isoniazid + 400 mg of pyrazinamide + 250 mg of ethambutol). The studies consisted of the determination of particle size and distribution (Ferret s diameter) and shape through optical microscopy, as well as rheological and technological properties (bulk and tapped densities, Hausner Factor, Carr s Index, repose angle and flux rate) and interactions among drugs and drug excipient through thermal analysis (DSC, DTA, TG and your derivate). The results showed that, except isoniazid, the other drugs presented poor rheological properties, determined by the physical characteristics of the particles: small size and rod like particles shape for rifampicin; rectangular shape for pyrazinamide and ethambutol, beyond its low density. The 4 drug mixture also not presented flowability, particularly that one containing drug quantity indicated for the formulation of FDC products. In this mixture, isoniazid, that has the best flowability, was added in a lower concentration. The addition of microcrystalline cellulose, magnesium stearate and talc to the drug mixtures improved flowability properties. In DSC analysis probable interactions among drugs were found, supporting the hypothesis of ethambutol and pyrazinamide catalysis of the rifampicin-isoniazid reaction resulting in 3- formylrifamycin isonicotinyl hydrazone (HYD) as a degradation product. In the mixtures containing lactose Supertab® DSC curves evidenced incompatibility among drugs and excipient. In the DSC curves of mixtures containing cellulose MC101®, magnesium stearate and talc, no alterations were observed comparing to the drug profiles. The TG/DTG of the binary and ternary mixtures curves showed different thermogravimetrics profiles relating that observed to the drug isolated, with the thermal decomposition early supporting the evidences of incompatibilities showed in the DSC and DTA curves

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