Delineating the effects of hot-melt extrusion on the performance of a polymeric film using artificial neural networks and an evolutionary algorithm.
Artificial neural network
Formulation
Hot melt extrusion
Modeling
Vaginal film
Journal
International journal of pharmaceutics
ISSN: 1873-3476
Titre abrégé: Int J Pharm
Pays: Netherlands
ID NLM: 7804127
Informations de publication
Date de publication:
25 Nov 2019
25 Nov 2019
Historique:
received:
26
03
2019
revised:
05
08
2019
accepted:
12
08
2019
pubmed:
29
9
2019
medline:
28
3
2020
entrez:
28
9
2019
Statut:
ppublish
Résumé
The aim of this study was to utilize an artificial neural network (ANN) in conjunction with an evolutionary algorithm to investigate the relationship between hot melt extrusion (HME) process parameters and vaginal film performance. Investigated HME process parameters were: barrel temperature, screw speed, and feed rate. Investigated film performance attributes were: percent dissolution at 30 min, puncture strength, and drug content. An ANN model was successfully developed and validated with a root mean squared error of 0.043 and 0.098 for training and validation, respectively. Of all three assessed process parameters, the model revealed that barrel temperature has a significant impact on film performance. An increase in barrel temperature resulted in increased dissolution and punctures strength and decreased drug content. Additionally, a successful implementation of an evolutionary algorithm was carried out in order to demonstrate the potential applicability of the developed ANN model in film formulation optimization. In this analysis, the values predicted of film performance attributes were within 1% error of the experimental data. The findings of this study provide a quantitative framework to understand the relationship between HME parameters and film performance. This quantitative framework has the potential to be used for film formulation development and optimization.
Identifiants
pubmed: 31560958
pii: S0378-5173(19)30760-4
doi: 10.1016/j.ijpharm.2019.118715
pmc: PMC6891106
mid: NIHMS1543302
pii:
doi:
Substances chimiques
Drug Carriers
0
Polymers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
118715Subventions
Organisme : NIAID NIH HHS
ID : U19 AI082639
Pays : United States
Informations de copyright
Copyright © 2019 Elsevier B.V. All rights reserved.
Références
J Pharm Sci. 2017 Jan;106(1):313-321
pubmed: 27837967
Eur J Pharm Sci. 2016 Mar 10;84:92-102
pubmed: 26780593
Pharm Dev Technol. 2013 Feb;18(1):236-45
pubmed: 22881350
ISRN Pharm. 2012;2012:436763
pubmed: 23326686
Pharm Res. 1996 May;13(5):804-8
pubmed: 8860442
AAPS PharmSciTech. 2015 Oct;16(5):1059-68
pubmed: 25652731
Pharm Dev Technol. 2015 Nov;20(7):845-853
pubmed: 24980221
Talanta. 2008 Sep 15;76(5):965-77
pubmed: 18761143
J Pharm Sci. 2017 Aug;106(8):2015-2025
pubmed: 28456732
AAPS J. 2015 Jan;17(1):194-205
pubmed: 25344439
Adv Drug Deliv Rev. 2003 Sep 12;55(9):1217-31
pubmed: 12954200
J Pharm Pharmacol. 2014 May;66(5):624-38
pubmed: 24341981
Drug Dev Ind Pharm. 2007 Sep;33(9):909-26
pubmed: 17891577
Drug Dev Ind Pharm. 2008 Apr;34(4):363-72
pubmed: 18401778
AAPS PharmSciTech. 2019 Jun 26;20(6):239
pubmed: 31243640
Eur J Pharm Biopharm. 2011 Jan;77(1):122-31
pubmed: 20934511
J Clin Pharmacol. 2010 Sep;50(9 Suppl):20S-30S
pubmed: 20881214
Int J Pharm. 2012 Aug 20;433(1-2):112-8
pubmed: 22613207
Pharmaceutics. 2012 Oct 18;4(4):531-50
pubmed: 24300369
Pharm Dev Technol. 2013 Sep-Oct;18(5):1238-46
pubmed: 22582904
Eur J Pharm Biopharm. 2015 Aug;94:473-84
pubmed: 26159838
J Pharm Sci. 2010 Oct;99(10):4201-14
pubmed: 20310024
J AOAC Int. 2012 May-Jun;95(3):652-68
pubmed: 22816255
Eur J Pharm Biopharm. 2015 Aug;94:170-9
pubmed: 25986587
IEEE Trans Neural Netw. 2006 Jul;17(4):879-92
pubmed: 16856652