Bayesian fractional stochastic models for controlled release mechanisms in nanoparticle drug delivery
1 Department of Mathematics, University of Cincinnati, OH, USA.
2 Department of Mathematics, Agnes Scott College, Decatur, GA, USA.
3 Department of Electrical Engineering and Computer Science, Ohio University, OH, USA.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2023, 08(01), 459-468.
Article DOI: 10.30574/wjaets.2023.8.1.0058
Publication history:
Received on 10 January 2023; revised on 15 February 2023; accepted on 18 February 2023
Abstract:
Optimizing drug release kinetics from nanocarriers is crucial for enhancing therapeutic efficacy and minimizing systemic toxicity in pharmaceutical formulations. Traditional deterministic models often fail to adequately capture the complex, non-Fickian diffusion and stochastic variability inherent in nanoparticle-based drug delivery systems. This study develops and applies a Bayesian fractional stochastic model to describe drug release kinetics from liposomal, polymeric, and metallic nanoparticles. The model integrates fractional calculus to account for anomalous diffusion and memory effects, while Bayesian inference enables dynamic parameter estimation under uncertainty. Experimental drug release data were obtained across varying temperature and pH conditions, with additional evaluations of nanoparticle size and initial drug concentration effects. The proposed model achieved the lowest Root Mean Squared Error (RMSE = 2.31) and optimal Bayesian Information Criterion (BIC = 35.6) compared to traditional models, including Higuchi, Korsmeyer-Peppas, Zero-Order, and Weibull models. Sensitivity analyses revealed that drug release efficiency increased with temperature, lower pH, smaller nanoparticle size, and higher initial drug concentration. The Bayesian approach enabled real-time updating of fractional order (a) and release rate constant (k), ensuring model adaptability under varying experimental conditions. These findings confirm that the Bayesian fractional stochastic model provides a robust, adaptive, and accurate predictive tool for optimizing drug release from nanoparticle systems. This approach holds substantial potential for guiding the rational design of next-generation, patient-specific drug delivery systems in nanomedicine.
Keywords:
Bayesian inference; Fractional calculus; Stochastic modeling; Drug release kinetics; Nanocarriers
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0