Researchers at the University of Iowa are leading an effort to develop a new computational method to predict how pharmaceutical compounds crystallize into tablet form, a critical step in bringing new medicines to patients.

The research will address a longstanding challenge in drug development: predicting how a newly discovered pharmaceutical ingredient will organize into a solid form. Once a promising drug molecule is identified, that molecule must be formulated into a stable dosage form, such as a tablet. Compounds, however, can crystallize into multiple arrangements, known as polymorphs, and each can exhibit different properties that affect how well a drug is absorbed by the body.

“These different crystal forms can have a dramatic impact on the effectiveness of a medicine,” said Michael Schnieders, principal investigator and professor of biomedical engineering. “A tablet, for example, could spontaneously change into a less soluble crystal form and pass through the body. Our goal is to predict these outcomes before they happen.”

The team will develop a new framework that combines mathematical models, artificial intelligence, and experimental validation to predict molecular crystal structures directly from a compound’s chemical structure. 

Lewis Stevens, co-PI and associate professor in the College of Pharmacy, provides experimental validation of the predicted crystal structures and their relative stability. “The ability to reliably predict crystal structure will significantly accelerate the design of new materials for specific, on-demand applications in defense, energy, agriculture, and healthcare—and at a fraction of the current computational cost,” said Stevens.

The four-year, $2 million award from the National Science Foundation includes a team with partners from New York University and the University of California, Riverside. They will use a high-performance computing (HPC) cluster provided by the University of Iowa Information Technology Services Research Services, conducting large-scale calculations and simulations using the Argon HPC cluster.

Melissa Lawrence, associate director of research services, said: “As research increasingly depends on computational methods and AI-enabled discovery, resources like Argon help ensure Iowa researchers have the tools necessary to tackle ambitious scientific challenges and remain competitive on the national stage.”

The work also has important implications for pharmaceutical manufacturing and technology transfer, said Schnieders, faculty affiliate of the Iowa Technology Institute. Advances from the project could support UI Pharmaceuticals, the University of Iowa’s contract research organization that partners with pharmaceutical companies to develop and manufacture drug formulations. Improved prediction of crystal forms could help reduce the time and cost of developing new therapeutics.