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Data-Driven Design of Novel Polymer Excipients for Pharmaceutical Amorphous Solid Dispersions.
Di Mare, Elena J; Punia, Ashish; Lamm, Matthew S; Rhodes, Timothy A; Gormley, Adam J.
Afiliación
  • Di Mare EJ; Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States.
  • Punia A; Analytical Research and Development, MRL, Merck & Co., Inc., Rahway, New Jersey 07065, United States.
  • Lamm MS; Analytical Research and Development, MRL, Merck & Co., Inc., Rahway, New Jersey 07065, United States.
  • Rhodes TA; Analytical Research and Development, MRL, Merck & Co., Inc., Rahway, New Jersey 07065, United States.
  • Gormley AJ; Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States.
Bioconjug Chem ; 35(9): 1363-1372, 2024 09 18.
Article en En | MEDLINE | ID: mdl-39150455
ABSTRACT
About 90% of active pharmaceutical ingredients (APIs) in the oral drug delivery system pipeline have poor aqueous solubility and low bioavailability. To address this problem, amorphous solid dispersions (ASDs) embed hydrophobic APIs within polymer excipients to prevent drug crystallization, improve solubility, and increase bioavailability. There are a limited number of commercial polymer excipients, and the structure-function relationships which lead to successful ASD formulations are not well-documented. There are, however, certain solid-state ASD characteristics that inform ASD performance. One characteristic shared by successful ASDs is a high glass transition temperature (Tg), which correlates with higher shelf stability and decreased drug crystallization. We aim to identify how polymer features such as side chain geometry, backbone methylation, and hydrophilic-lipophilic balance impact Tg to design copolymers capable of forming high-Tg ASDs. We tested a library of 50 ASD formulations (18 previously studied and 32 newly synthesized) of the model drug probucol with copolymers synthesized through automated photoinduced electron/energy transfer-reversible addition-fragmentation chain-transfer (PET-RAFT) polymerization. A machine learning (ML) algorithm was trained on the Tg data to identify the major factors influencing Tg, including backbone methylation and nonlinear side chain geometry. In both polymer alone and probucol-loaded ASDs, a Random Forest Regressor captured structure-function trends in the data set and accurately predicted Tg with an average R2 > 0.83 across a 10-fold cross validation. This ML model will be used to predict novel copolymers to design ASDs with high Tg, a crucial factor in predicting ASD success.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Polímeros / Excipientes Idioma: En Revista: Bioconjug Chem Asunto de la revista: BIOQUIMICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Polímeros / Excipientes Idioma: En Revista: Bioconjug Chem Asunto de la revista: BIOQUIMICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos