Articles Accepted

by Mrs. Pratiksha Akki, 20 Jul 2023
Co-Author(s): Apoorva V,Pratiksha Akki,Kusum S Akki

In the pharmaceutical industry, artificial intelligence (AI) refers to the use of automated algorithms to tasks that typically involve human intellect. A model of the human brain is an artificial neural network (ANN). The goal of an artificial neuron is to imitate the form and function of a real neuron. Oral disintegrating tablets (ODTs), which can dissolve on the tongue in three minutes or less, are an unusual dose form, especially for elderly and young patients. Current ODT formulation studies typically rely on laboratory trial-and-error and the hands-on experience of pharmaceutical professionals, which is ineffective and time-consuming. The current research objective was to develop an artificial neural network (ANN) prediction model for ODT formulations that involve wet granulation technique. A literature review was carried out by collecting 307 formulation data set to train the data. For the ODT formulation, the ANN predicted and practically obtained values were compared. Formulations were subjected to precompression and post Compression parameters since it is oral disintegration tablets, our focus was on Disintegration time and in- vitro dissolution studies. The F7 formulation showed disintegrating time has been accurately predicted to be 48.476 seconds and obtained to be 45.1 seconds and in-vitro dissolution rate has been accurately predicted to be 92.344% and obtained to be 93.74% which is found to be highest among all the formulations. Experimental data revealed the almost identical estimate for ODT formulations compared to the ANN prediction. The application of this prediction model could efficiently reduce the time and cost required to produce a pharmaceutical and consequently promote the development of an effective drug product.

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