Efficient prediction of in vitro piroxicam release and diffusion from topical films based on biopolymers using deep learning models and generative adversarial networks

Salma, Hentabli and Melha, Yahoum Madiha and Sonia, Lefnaoui and Hamza, Hentabli and Salim, Naomie (2021) Efficient prediction of in vitro piroxicam release and diffusion from topical films based on biopolymers using deep learning models and generative adversarial networks. Journal of Pharmaceutical Sciences, 110 (6). pp. 2531-2543. ISSN 0022-3549

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Official URL: http://dx.doi.org/10.1016/j.xphs.2021.01.032

Abstract

The purpose of this study was to simultaneously predict the drug release and skin permeation of Piroxicam (PX) topical films based on Chitosan (CTS), Xanthan gum (XG) and its Carboxymethyl derivatives (CMXs) as matrix systems. These films were prepared by the solvent casting method, using Tween 80 (T80) as a permeation enhancer. All of the prepared films were assessed for their physicochemical parameters, their in vitro drug release and ex vivo skin permeation studies. Moreover, deep learning models and machine learning models were applied to predict the drug release and permeation rates. The results indicated that all of the films exhibited good consistency and physicochemical properties. Furthermore, it was noticed that when T80 was used in the optimal formulation (F8) based on CTS-CMX3, a satisfactory drug release pattern was found where 99.97% of PX was released and an amount of 1.18 mg/cm2 was permeated after 48 h. Moreover, Generative Adversarial Network (GAN) efficiently enhanced the performance of deep learning models and DNN was chosen as the best predictive approach with MSE values equal to 0.00098 and 0.00182 for the drug release and permeation kinetics, respectively. DNN precisely predicted PX dissolution profiles with f2 values equal to 99.99 for all the formulations.

Item Type:Article
Uncontrolled Keywords:Biopolymers, Deep learning
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computing
ID Code:95884
Deposited By: Widya Wahid
Deposited On:22 Jun 2022 14:38
Last Modified:22 Jun 2022 14:38

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