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Reservoir-Style Polymeric Drug Delivery Systems: Empirical and Predictive Models for Implant Design

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 ke...

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Autores principales: Li, Linying, Lee, Chanhwa, Cruz, Daniela F., Krovi, Sai Archana, Hudgens, Michael G., Cottrell, Mackenzie L., Johnson, Leah M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9610229/
https://www.ncbi.nlm.nih.gov/pubmed/36297338
http://dx.doi.org/10.3390/ph15101226
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author Li, Linying
Lee, Chanhwa
Cruz, Daniela F.
Krovi, Sai Archana
Hudgens, Michael G.
Cottrell, Mackenzie L.
Johnson, Leah M.
author_facet Li, Linying
Lee, Chanhwa
Cruz, Daniela F.
Krovi, Sai Archana
Hudgens, Michael G.
Cottrell, Mackenzie L.
Johnson, Leah M.
author_sort Li, Linying
collection PubMed
description 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.
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spelling pubmed-96102292022-10-28 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. Pharmaceuticals (Basel) Article 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. MDPI 2022-10-03 /pmc/articles/PMC9610229/ /pubmed/36297338 http://dx.doi.org/10.3390/ph15101226 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Linying
Lee, Chanhwa
Cruz, Daniela F.
Krovi, Sai Archana
Hudgens, Michael G.
Cottrell, Mackenzie L.
Johnson, Leah M.
Reservoir-Style Polymeric Drug Delivery Systems: Empirical and Predictive Models for Implant Design
title Reservoir-Style Polymeric Drug Delivery Systems: Empirical and Predictive Models for Implant Design
title_full Reservoir-Style Polymeric Drug Delivery Systems: Empirical and Predictive Models for Implant Design
title_fullStr Reservoir-Style Polymeric Drug Delivery Systems: Empirical and Predictive Models for Implant Design
title_full_unstemmed Reservoir-Style Polymeric Drug Delivery Systems: Empirical and Predictive Models for Implant Design
title_short Reservoir-Style Polymeric Drug Delivery Systems: Empirical and Predictive Models for Implant Design
title_sort reservoir-style polymeric drug delivery systems: empirical and predictive models for implant design
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9610229/
https://www.ncbi.nlm.nih.gov/pubmed/36297338
http://dx.doi.org/10.3390/ph15101226
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