When Ben Brown, assistant professor of chemistry, thinks about the opioid epidemic, he sees the problem at a molecular level. Painkillers legally used in medicine, such as oxycodone, are highly addictive, but a better understanding of how their molecules interact with proteins in the body could lead to the development of non-addictive alternatives, he said.
In May, the National Institute on Drug Abuse awarded Brown $1.5 million over five years to advance his work in this area. Brown, a fellow at the Vanderbilt Center for Addiction Research and the Center for Applied Artificial Intelligence in Protein Dynamics, is developing artificial intelligence that analyzes billions of potential opioid drugs to reveal detailed information about how they interact with key proteins. The remaining $875,000 of the grant will flow to Vanderbilt for indirect and administrative costs associated with Brown’s research.
Brown will focus his research on Mu-opioid receptors, which signal proteins in the central nervous system that bind to opioids. These receptors regulate pain, stress, mood and other functions. Drugs that target these receptors are among the most powerful pain relievers, but they are also the most addictive.
The grant, an Avenir Award in the Chemistry and Pharmacology of Substance Use Disorders, is awarded by NIDA to early-stage researchers who propose highly innovative studies and represent the future of addiction science.
The energy and enthusiasm that Ben brings to his science and his scientific collaborations is outstanding and it is only right that he be recognized as a new pioneer in his field. Ben is one of the intellectual contributors behind the founding of the Center for Applied Artificial Intelligence in Protein Dynamics. I expect Ben to make fundamental advances in multiple key aspects of computer-aided drug design.”
Hassane Mchaourab, director of the Center for Applied Artificial Intelligence in Protein Dynamics and Louise B. McGavock Chair and professor of molecular physiology and biophysics
Brown’s computational platform models drug-protein interactions in a way that accounts for their dynamic physical motions. These movements, called conformational changes, can happen in milliseconds and make a big difference in how a protein behaves and binds or interacts with a small molecule drug.
By computationally modeling this movement, algorithms can more effectively predict how closely proteins and drugs will interact and the effects of that interaction. This information is used to screen billions of potential drugs—an unprecedented scale for highly dynamic proteins—or to design new ones with properties that lead to fewer addictive side effects.
Computational platforms that model the structure of proteins and how they interact with drugs already exist, but they largely neglect conformational changes and do little to predict how a new drug will behave. This is partly due to the lack of data available for training algorithms.
With data-rich material from researchers Craig Lindsley, Heidi Hamm and Vsevolod V. Gurevich of Vanderbilt, Matthias Elgeti of the University of Leipzig and Wu Beili of the Shanghai Institute of Materia Medica, Brown will synthesize, functionally validate and characterize structural molecules and drug receptors designed by the researchers. After this component of the grant, Brown will feed the data back into the computational platform so it can be used as a starting point for more rounds of optimization—a computational-experimental iterative feedback loop.
“You see pediatric patients have surgery and get opioids postoperatively and then have a problem after that. It’s really sad,” Brown said. “So the goal is to provide analgesia without risking addiction. And for those who have addiction, to provide new drugs to help with recovery.”
In addition to the Center for Applied AI in Protein Dynamics and VCAR, Brown’s research collaborations include the Center for Structural Biology and the Vanderbilt Institute for Chemical Biology.
