Data-Driven Design of Novel Polymer Excipients for Pharmaceutical Amorphous Solid Dispersions

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 (<i>T</i>_g), 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 <i>T</i>_g to design copolymers capable of forming high-<i>T</i>_g 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 <i>T</i>_g data to identify the major factors influencing <i>T</i>_g, 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 <i>T</i>_g with an average <i>R</i>² > 0.83 across a 10-fold cross validation. This ML model will be used to predict novel copolymers to design ASDs with high <i>T</i>_g, a crucial factor in predicting ASD success.