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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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Updated: Jun 21, 2025

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Optimization and evaluation of modified release solid dosage forms using artificial neural network.

Tulsi Sagar Sheth1,2, Falguni Acharya3

  • 1Department of Applied Sciences and Humanities, Parul Institute of Engineering and Technology, Parul University, Vadodara, Gujarat, 391760, India.

Scientific Reports
|July 16, 2024
PubMed
Summary

Artificial Neural Networks (ANNs) optimized Quetiapine Fumarate modified-release tablets. This approach accurately predicted drug release profiles, demonstrating ANNs

Keywords:
Artificial neural networksDrug release profileMATLABSimilarity factor (f2)SimulationSolid dosage forms

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Area of Science:

  • Pharmaceutical Sciences
  • Computational Chemistry
  • Drug Delivery

Background:

  • Modified-release dosage forms are crucial for optimizing drug efficacy and patient compliance.
  • Quetiapine Fumarate is an antipsychotic medication requiring precise dose control.
  • Predictive modeling can accelerate the development of complex drug formulations.

Purpose of the Study:

  • To optimize and evaluate the drug release kinetics of Quetiapine Fumarate modified-release tablets.
  • To utilize Artificial Neural Networks (ANNs) for predicting and optimizing drug release profiles.
  • To establish a robust in silico method for pharmaceutical formulation development.

Main Methods:

  • Artificial Neural Networks (ANNs) were employed to model drug release kinetics.
  • Excipient compositions (Sodium Citrate, Eudragit® L100 55, Eudragit® L30 D55, Lactose Monohydrate, Dicalcium Phosphate, Glyceryl Behenate) were used as variable inputs.
  • In-vitro dissolution data at ten time points served as the target output for network training.

Main Results:

  • The trained ANNs successfully simulated and predicted the in-vitro dissolution profiles of Quetiapine Fumarate MR tablets.
  • The similarity factor (f2) confirmed a strong agreement between predicted and manufactured formulation release profiles.
  • ANNs demonstrated significant potential in optimizing pharmaceutical formulations.

Conclusions:

  • Artificial Neural Networks provide a powerful tool for optimizing drug release kinetics in modified-release formulations.
  • This study validates the use of ANNs for efficient and accurate prediction of pharmaceutical formulation performance.
  • The developed ANN model can accelerate the development cycle for Quetiapine Fumarate MR tablets and similar formulations.