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Preparation and optimisation of solid lipid nanoparticles of rivaroxaban using artificial neural networks and response surface method. | LitMetric
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Preparation and optimisation of solid lipid nanoparticles of rivaroxaban using artificial neural networks and response surface method.

Fatemeh Ghorbannejad Nashli Sareh Aghajanpour Ali Farmoudeh Seyed Sajad Hosseini Balef Meshkat Torkamanian Alireza Razavi Hamid Irannejad Pedram Ebrahimnejad

J Microencapsul

Pharmaceutical Sciences Research Center, Hemoglobinopathy Institute, Mazandaran University of Medical Sciences, Sari, Iran.

Published: January 2025

AI Article Synopsis

  • The study focused on enhancing the delivery of rivaroxaban through optimized solid lipid nanoparticles (SLN) to improve their size, entrapment efficiency, and ability to dissolve and cross the blood-brain barrier.
  • A total of 32 SLN formulations were created using a central composite design, and methods like response surface methodology (RSM) and artificial neural networks (ANN) were employed to predict their properties based on various factors.
  • The optimized SLN had an average particle size of 159.8 nm and an entrapment efficiency of 74.3%. The ANN model was found to be more accurate than RSM, indicating its effectiveness in optimizing pharmaceutical formulations.

Article Abstract

Aims: This study aimed to improve rivaroxaban delivery by optimising solid lipid nanoparticles (SLN) for minimal mean diameter and maximal entrapment efficiency (EE), enhancing solubility, bioavailability, and the ability to cross the blood-brain barrier.

Methods: A central composite design was employed to synthesise 32 SLN formulations. Response surface methodology (RSM) and artificial neural networks (ANN) models predicted mean diameter and EE based on five independent variables.

Results: The optimised SLN formulation achieved a mean particle diameter of 159.8 ± 15.2 nm, with a Polydispersity index of 0.46, a zeta potential of -28.8 mV, and an EE of 74.3% ± 5.6%. The ANN model showed superior accuracy for both mean diameter and EE, outperforming the RSM model. Structural integrity and stability were confirmed by scanning electron microscopy (SEM), differential scanning calorimetry (DSC), and Fourier-transform infrared spectroscopy (FTIR).

Conclusion: The high accuracy of the ANN model highlights its potential in optimising pharmaceutical formulations and improving SLN-based drug delivery systems.

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 Source
http://dx.doi.org/10.1080/02652048.2024.2437362DOI Listing

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