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

D Martarelli, L Casettari1, K S Shalaby

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Artificial Neural Networks (ANNs) optimized extended-release melatonin tablets to mimic natural plasma levels. This approach successfully determined ideal tablet formulations for sustained drug delivery, improving treatment for sleep disorders.

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Area of Science:

  • Pharmaceutical Sciences
  • Computational Chemistry

Background:

  • Melatonin efficacy for sleep disorders is established, but its short half-life necessitates extended-release formulations.
  • Achieving natural nocturnal melatonin plasma profiles with extended-release tablets presents a significant formulation challenge.

Purpose of the Study:

  • To optimize the composition of prolonged-release melatonin tablets using Artificial Neural Networks (ANNs).
  • To develop a method for mimicking the release profile of a marketed melatonin brand.

Main Methods:

  • Utilized Artificial Neural Networks (ANNs) to optimize drug release from hydrophilic polymer matrices.
  • Prepared 27 tablet formulations varying hydroxypropyl methylcellulose, xanthan gum, and Carbopol®974P NF content.
  • Employed dissolution test data as input for ANN model development in Visual Basic, determining optimal hidden layer neuron count via trial and error.

Main Results:

  • An ANN model with nine hidden layer neurons demonstrated optimal performance.
  • The predictive capability of the ANN was validated.
  • The optimized ANN model identified a formulation that successfully mimicked the release profile of a reference melatonin product using a similarity fit factor.

Conclusions:

  • Artificial Neural Networks (ANNs) offer a viable computational approach for optimizing prolonged-release melatonin tablet formulations.
  • This study demonstrates the potential of ANNs to achieve desired drug dissolution profiles for enhanced therapeutic outcomes.