High-Throughput Synthesis, Analysis, and Optimization of Injectable Hydrogels for Protein Delivery.
Biomacromolecules
; 21(1): 214-229, 2020 01 13.
Article
em En
| MEDLINE
| ID: mdl-31686502
ABSTRACT
The development of in situ-gelling hydrogels that can enable prolonged protein release is increasingly important due to the emergence of a growing number of protein-based therapeutics. Herein, we describe a high-throughput strategy to fabricate, characterize, and subsequently optimize hydrazone-cross-linked in situ-gelling hydrogels for protein delivery. Hydrogels are fabricated using an automated high-throughput robot to mix a variety of thermoresponsive, nonthermoresponsive, charged, neutral, naturally sourced, and synthetic polymers functionalized with hydrazide or aldehyde groups, generating in situ-gelling hydrogels with well-defined compositions within a 96-well plate. High-throughput characterization strategies are subsequently developed to enable on-plate analysis of hydrogel swelling, mechanics, degradation, transparency, and protein (ovalbumin) release kinetics that yield results consistent with those collected using traditional bulk hydrogel analysis techniques. Dynamic regression and latent variable modeling are then applied to fit performance statistics to the collected data set; subsequently, numerical optimization is used to identify mixtures of precursor polymers that exhibit targeted combinations of minimal burst release, maximum total protein release, minimum release rate, and maximum transparency (the latter of particular relevance for ophthalmic protein delivery applications). Given the rapid throughput of the protocols developed (i.e., 126 hydrogels can be synthesized and screened in quadruplicate within hours), this approach offers particular promise for accelerating the identification of injectable hydrogel compositions relevant for both protein delivery as well as other biomedical applications for which clearly predefined materials properties are required.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Proteínas
/
Hidrogéis
Tipo de estudo:
Guideline
/
Prognostic_studies
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article