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

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.