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Current Drug Delivery


ISSN (Print): 1567-2018
ISSN (Online): 1875-5704

Optimization of Melatonin Dissolution from Extended Release Matrices Using Artificial Neural Networking

Author(s): Martarelli D., Casettari L., Shalaby K.S., Soliman M.E., Cespi M., Bonacucina G., Fagioli L., Perinelli D.R., Lam J.K.W. and Palmieri G.F.

Volume 13, Issue 4, 2016

Page: [565 - 573] Pages: 9

DOI: 10.2174/1567201812666150608101528

Price: $65


Background: Efficacy of melatonin in treating sleep disorders has been demonstrated in numerous studies. Being with short half-life, melatonin needs to be formulated in extended-release tablets to prevent the fast drop of its plasma concentration. However, an attempt to mimic melatonin natural plasma levels during night time is challenging.

Methods: In this work, Artificial Neural Networks (ANNs) were used to optimize melatonin release from hydrophilic polymer matrices. Twenty-seven different tablet formulations with different amounts of hydroxypropyl methylcellulose, xanthan gum and Carbopol®974P NF were prepared and subjected to drug release studies. Using dissolution test data as inputs for ANN designed by Visual Basic programming language, the ideal number of neurons in the hidden layer was determined trial and error methodology to guarantee the best performance of constructed ANN.

Results: Results showed that the ANN with nine neurons in the hidden layer had the best results. ANN was examined to check its predictability and then used to determine the best formula that can mimic the release of melatonin from a marketed brand using similarity fit factor.

Conclusion: This work shows the possibility of using ANN to optimize the composition of prolonged-release melatonin tablets having dissolution profile desired.

Keywords: ANN, drug delivery, hydrophilic polymers, melatonin, similarity fit factors, tablet formulation.

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