Your browser doesn't support javascript.
loading
Reservoir-Style Polymeric Drug Delivery Systems: Empirical and Predictive Models for Implant Design.
Li, Linying; Lee, Chanhwa; Cruz, Daniela F; Krovi, Sai Archana; Hudgens, Michael G; Cottrell, Mackenzie L; Johnson, Leah M.
Afiliação
  • Li L; RTI International, 3040 E Cornwallis Road, Research Triangle Park, NC 27709, USA.
  • Lee C; Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA.
  • Cruz DF; RTI International, 3040 E Cornwallis Road, Research Triangle Park, NC 27709, USA.
  • Krovi SA; RTI International, 3040 E Cornwallis Road, Research Triangle Park, NC 27709, USA.
  • Hudgens MG; Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA.
  • Cottrell ML; Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA.
  • Johnson LM; RTI International, 3040 E Cornwallis Road, Research Triangle Park, NC 27709, USA.
Pharmaceuticals (Basel) ; 15(10)2022 Oct 03.
Article em En | MEDLINE | ID: mdl-36297338
Controlled drug delivery systems can provide sustained release profiles, favorable pharmacokinetics, and improved patient adherence. Here, a reservoir-style implant comprising a biodegradable polymer, poly(ε-caprolactone) (PCL), was developed to deliver drugs subcutaneously. This work addresses a key challenge when designing these implantable drug delivery systems, namely the accurate prediction of drug release profiles when using different formulations or form factors of the implant. The ability to model and predict the release behavior of drugs from an implant based on their physicochemical properties enables rational design and optimization without extensive and laborious in vitro testing. By leveraging experimental observations, we propose a mathematical model that predicts the empirical parameters describing the drug diffusion and partitioning processes based on the physicochemical properties of the drug. We demonstrate that the model enables an adequate fit predicting empirical parameters close to experimental values for various drugs. The model was further used to predict the release performance of new drug formulations from the implant, which aligned with experimental results for implants exhibiting zero-order release kinetics. Thus, the proposed empirical models provide useful tools to inform the implant design to achieve a target release profile.
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Pharmaceuticals (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Pharmaceuticals (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos
...