Preparation and Optimisation of Solid Lipid Nanoparticles of Rivaroxaban Using Artificial Neural Networks and Response Surface Method

Preparation and Optimisation of Solid Lipid Nanoparticles of Rivaroxaban Using Artificial Neural Networks and Response Surface Method Publisher Pubmed

Ghorbannejad Nashli F^{1, 2} ; Aghajanpour S^{1, 2} ; Farmoudeh A^{1, 2} ; Balef SSH³ ; Torkamanian M⁴ ; Razavi A⁵ ; Irannejad H⁶ ; Ebrahimnejad P^{1, 2}

Show Affiliations

1. Pharmaceutical Sciences Research Center, Hemoglobinopathy Institute, Mazandaran University of Medical Sciences, Sari, Iran
2. Department of Pharmaceutics, Faculty of Pharmacy, Mazandaran University of Medical Sciences, Sari, Iran
3. Department of Bioinformatics, Institute of Biochemistry and Biophysics, Tehran, Iran
4. Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
5. Research Center for Clinical Virology, Tehran University of Medical Sciences, Tehran, Iran
6. Department of Medicinal Chemistry, Faculty of Pharmacy, Mazandaran University of Medical Sciences, Sari, Iran

Source: Journal of Microencapsulation Published:2025

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. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

Related Docs

1. A State-Of-The-Art Review on Solid Lipid Nanoparticles As a Nanovaccines Delivery System, Journal of Drug Delivery Science and Technology (2023)

2. The Synthesis, Application and Therapeutic Perspectives of Medicinal Plants-Based Solid Lipid Nanoparticles: A Comprehensive Review, Advances in Traditional Medicine (2025)

3. Utilizing Machine Learning for Predicting Drug Release From Polymeric Drug Delivery Systems, Computers in Biology and Medicine (2025)

4. The Significance of Artificial Intelligence in Drug Delivery System Design, Advanced Drug Delivery Reviews (2019)

5. Emerging Innovations in Vincristine-Encapsulated Nanoparticles: Pioneering a New Era in Oncological Therapeutics, Journal of Drug Delivery Science and Technology (2024)

6. Sensitivity Analysis and Modeling of 4-Chlorophenol Degradation in Aqueous Solutions by an Nzvi-Sodium Persulfate System, Desalination and Water Treatment (2018)

Experts (# of related papers)

View all Related Experts

Rassoul Dinarvand (2)

Mahmood Alimohammadi (1)

Style	Citing Format
MLA	Ghorbannejad Nashli F, et al.. "Preparation and Optimisation of Solid Lipid Nanoparticles of Rivaroxaban Using Artificial Neural Networks and Response Surface Method." Journal of Microencapsulation, vol. 42, no. 1, 2025, pp. 70-82.
APA	Ghorbannejad Nashli F, Aghajanpour S, Farmoudeh A, Balef SSH, Torkamanian M, Razavi A, Irannejad H, Ebrahimnejad P (2025). Preparation and Optimisation of Solid Lipid Nanoparticles of Rivaroxaban Using Artificial Neural Networks and Response Surface Method. Journal of Microencapsulation, 42(1), 70-82.
Chicago	Ghorbannejad Nashli F, Aghajanpour S, Farmoudeh A, Balef SSH, Torkamanian M, Razavi A, Irannejad H, Ebrahimnejad P. "Preparation and Optimisation of Solid Lipid Nanoparticles of Rivaroxaban Using Artificial Neural Networks and Response Surface Method." Journal of Microencapsulation 42, no. 1 (2025): 70-82.
Harvard	Ghorbannejad Nashli F et al. (2025) 'Preparation and Optimisation of Solid Lipid Nanoparticles of Rivaroxaban Using Artificial Neural Networks and Response Surface Method', Journal of Microencapsulation, 42(1), pp. 70-82.
Vancouver	Ghorbannejad Nashli F, Aghajanpour S, Farmoudeh A, Balef SSH, Torkamanian M, Razavi A, et al.. Preparation and Optimisation of Solid Lipid Nanoparticles of Rivaroxaban Using Artificial Neural Networks and Response Surface Method. Journal of Microencapsulation. 2025;42(1):70-82.
BibTex	@article{ author = {Ghorbannejad Nashli F and Aghajanpour S and Farmoudeh A and Balef SSH and Torkamanian M and Razavi A and Irannejad H and Ebrahimnejad P}, title = {Preparation and Optimisation of Solid Lipid Nanoparticles of Rivaroxaban Using Artificial Neural Networks and Response Surface Method}, journal = {Journal of Microencapsulation}, volume = {42}, number = {1}, pages = {70-82}, year = {2025} }
RIS	TY - JOUR AU - Ghorbannejad Nashli F AU - Aghajanpour S AU - Farmoudeh A AU - Balef SSH AU - Torkamanian M AU - Razavi A AU - Irannejad H AU - Ebrahimnejad P TI - Preparation and Optimisation of Solid Lipid Nanoparticles of Rivaroxaban Using Artificial Neural Networks and Response Surface Method JO - Journal of Microencapsulation VL - 42 IS - 1 SP - 70 EP - 82 PY - 2025 ER -

Science Communicator Platform

Authors

Authors Affiliations

Abstract