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Optimization tools for drug formulation and discovery

Finding the ideal formulation for a new drug can involve sifting through many possible ingredients and conducting a large number of experiments. A mathematical optimization program developed by Herschel Rabitz and his team provides the ability to model a mixture to predict the performance of all possible combinations of components. The approach can be used not just for drug formulation, but also in drug discovery and fundamental research. The technology builds on nearly two decades of work in the Rabitz lab on how to understand and cope with systems that have large numbers of input variables. For example, when creating a new pharmaceutical compound, researchers must choose from many possible chemical groups to add to the structure. Similarly, when creating a peptide drug, researchers must decide which of the 20 common amino acids to include. In each case, the possible combinations are so numerous that it is impossible to test each combination. The approach developed by Rabitz and his team can help handle the number of input variables that need to be considered. “Our mathematical tools help reduce the number of required experiments that are necessary when searching through multiple variables,” Rabitz said. The team has already verified the usefulness of their optimization protocol in experimental settings for the formulation of monoclonal antibody drugs. They have also built a computational platform for peptide drug discovery, and they are applying their methods to enhance a technology known as optogenetics, which involves the use of light to control cellular processes. This technology was one of several presented at Celebrate Princeton Invention 2017. Learn more at invention.princeton.edu. Video producer: Catherine Zandonella, Office of the Dean for Research, Princeton University Videographer and editor: Evelyn Tu, Flying Camel Media Copyright 2017 The Trustees of Princeton University