ArticleTurkish journal of pharmaceutical sciences2017
Quetiapine Fumarate Extended-release Tablet Formulation Design Using Artificial Neural Networks.
Article in Turkish journal of pharmaceutical sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 4 citations in OpenAlex.
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Authors and funding
4 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesThis design study was implemented within the scope of the quality by design approach, which included the "International Conference on Harmonization" guidelines. We evaluated the quality of a modified-release tablet formulation of quetiapine fumarate, which was designed using artificial neural networks (ANN), and determined a new formulation that was similar to the reference product. MATERIALS AND
methodsTwelve different formulations were produced and tested. The reference product's results and our experimental results were used as outputs for the training of the ANN programs of Intelligensys Ltd.
resultsDissolution tests were performed with the new formulation (F13) suggested by the INForm V.4 ANN program in three different pHs of the gastrointestinal system. The compliance of this formulation was confirmed by comparing the results with an f2 similarity test.
conclusionUse of these programs supports research and development processes with multiple evaluation methods and alternative formulations may be determined faster and at lower cost.
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