Efficient Prediction of In Vitro Piroxicam Release and Diffusion From Topical Films Based on Biopolymers Using Deep Learning Models and Generative Adversarial Networks.
Biopolymers
Deep learning
Drug release
Generative adversarial networks
Machine learning
Piroxicam
Skin permeation
Topical films
Journal
Journal of pharmaceutical sciences
ISSN: 1520-6017
Titre abrégé: J Pharm Sci
Pays: United States
ID NLM: 2985195R
Informations de publication
Date de publication:
06 2021
06 2021
Historique:
received:
27
12
2020
revised:
28
01
2021
accepted:
29
01
2021
pubmed:
7
2
2021
medline:
22
6
2021
entrez:
6
2
2021
Statut:
ppublish
Résumé
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/cm
Identifiants
pubmed: 33548245
pii: S0022-3549(21)00074-5
doi: 10.1016/j.xphs.2021.01.032
pii:
doi:
Substances chimiques
Piroxicam
13T4O6VMAM
Chitosan
9012-76-4
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2531-2543Informations de copyright
Copyright © 2021 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.