131
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Polycaprolactone and polycaprolactone triol blends to obtain a stable liquid nanotechnological formulation: synthesis, characterization and in vitroin vivo taste masking evaluation

ORCID Icon, , , , ORCID Icon, ORCID Icon, , ORCID Icon, , ORCID Icon, ORCID Icon & ORCID Icon show all
Pages 1556-1567 | Received 29 Mar 2021, Accepted 22 Nov 2021, Published online: 09 Dec 2021
 

Abstract

The use of polymeric blends is a potential strategy to obtain novel nanotechnological formulations aiming at drug delivery systems. Saquinavir, an antiretroviral drug, was chosen as a model drug for the development of new stable liquid formulations with unpleasant taste masking properties. Three formulations containing different polymeric ratios (1:3, 1:1 and 3:1) were prepared and properly characterized by particle size distribution, zeta potential, pH, drug content and encapsulation efficiency measurements. The stability was verified by monitoring the zeta potential, particle size distribution, polydispersity index and drug content by 90 days. The light backscattering analysis was used to early identify possible phenomena of instability in the formulations. The in vitro drug release and saquinavir cytotoxicity were evaluated. The in vitro and in vivo taste masking properties were studied using an electronic tongue and a human sensory panel. All formulations presented nanometric sizes around 200 nm and encapsulation efficiency above 99%. The parameters evaluated for stability remained constant throughout 90 days. The in vitro tests showed a controlled drug release and absence of toxic effects on human T lymphocytes. The electronic tongue experiment showed taste differences for all formulations in comparison to drug solutions, with a more pronounced difference for the formulation with higher polycaprolactone content (3:1). This formulation was chosen for in vivo sensory panel evaluation which results corroborated the electronic tongue experiments. In conclusion, the polymer blend nanoformulation developed herein showed the promising application to incorporate drugs aiming at pharmaceutical taste-masking properties.

Acknowledgments

Our acknowledgments to the support of the Brazilian government: Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul – FAPERGS , Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Cristália for providing saquinavir base. D. S. C. also acknowledges FAPESP (grant number 2017/12174-4), MCTI-SisNano (CNPq/402.287/2013-4) and Rede Agronano (EMBRAPA) from Brazil.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement

Part of the processed data required to reproduce these findings are available to download from [https://data.mendeley.com/datasets/7pc5dk7mfk/1]. Additional raw/processed data required to reproduce these findings cannot be shared at this time due to technical or time limitations.

Additional information

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance code 001, INCT-NANOFARMA [São Paulo Research Foundation (FAPESP, Brazil) Grant #2014/50928-2, Fundação de Apoio à Pesquisa do Estado do Rio Grande do Sul (FAPERGS) – grant number 12/1285-0 and CNPq Grant #477196/2013-6 and #465687/2014-8], PRONEX/FAPERGS/CNPq 12/2014 #16/2551-0000467-6.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 65.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 1,085.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.