POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles.
Mol Pharm
; 21(7): 3356-3374, 2024 Jul 01.
Article
em En
| MEDLINE
| ID: mdl-38805643
ABSTRACT
Block copolymers, composed of poly(2-oxazoline)s and poly(2-oxazine)s, can serve as drug delivery systems; they form micelles that carry poorly water-soluble drugs. Many recent studies have investigated the effects of structural changes of the polymer and the hydrophobic cargo on drug loading. In this work, we combine these data to establish an extended formulation database. Different molecular properties and fingerprints are tested for their applicability to serve as formulation-specific mixture descriptors. A variety of classification and regression models are built for different descriptor subsets and thresholds of loading efficiency and loading capacity, with the best models achieving overall good statistics for both cross- and external validation (balanced accuracies of 0.8). Subsequently, important features are dissected for interpretation, and the DrugBank is screened for potential therapeutic use cases where these polymers could be used to develop novel formulations of hydrophobic drugs. The most promising models are provided as an open-source software tool for other researchers to test the applicability of these delivery systems for potential new drug candidates.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Polímeros
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Sistemas de Liberação de Medicamentos
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Interações Hidrofóbicas e Hidrofílicas
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Aprendizado de Máquina
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Micelas
Idioma:
En
Revista:
Mol Pharm
Assunto da revista:
BIOLOGIA MOLECULAR
/
FARMACIA
/
FARMACOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Finlândia