When Ben Brown, a research assistant professor of chemistry, thinks about the opioid epidemic, he looks at the problem at a molecular level. Painkillers used legally in medicine, such as oxycodone, are highly addictive, but a better understanding of how their molecules interact with proteins in the body is leading to the development of non-addictive alternatives. That could lead to prescriptions, he said.
In May, the National Institute on Drug Abuse awarded Brown $1.5 million over five years to advance research in this area. Brown, an affiliated faculty member at the Vanderbilt Center for Addiction Research and the Center for Protein Dynamics and Applied Artificial Intelligence, has analyzed billions of potential opioid drugs to learn more about how they interact with key proteins. We are developing artificial intelligence that reveals new insights. The remainder of the grant, his $875,000, will go to Vanderbilt for indirect and administrative costs associated with Brown’s research.
Dr. Brown will focus his research on the Mu-opioid receptor, a central nervous system signaling protein that binds opioids. These receptors regulate pain, stress, mood, and other functions. Drugs that target these receptors are among the most powerful painkillers, but they are also the most addictive.
This grant, the Avenir Award in Chemistry and Pharmacology of Substance Use Disorders, is awarded by NIDA to early-stage researchers who propose highly innovative research and represent the future of addiction science.
The energy and enthusiasm that Ben brings to science and scientific collaboration is outstanding, and it is no surprise that he is recognized as a young pioneer in this field. Ben is one of the intellectual contributors who helped establish the Center for Applied AI in Protein Dynamics. I look forward to Ben making fundamental advances in multiple core aspects of computer-aided drug discovery. ”
Hassane Mchaourab, Director of the Center for Applied AI in Protein Dynamics; Louise B. McGavock, Professor of Molecular Physiology and Biophysics
Brown’s computational platform models drug-protein interactions in a way that accounts for their dynamic physical behavior. These movements, called conformational changes, can occur in milliseconds and can make big differences in how a protein behaves and how it binds and interacts with small molecule drugs.
By modeling this behavior in computers, algorithms can better predict how tightly proteins and drugs interact and the effects of this interaction. This information is used to screen billions of potential drugs. This is an unprecedented scale for highly dynamic proteins and will be used to design new drugs with properties that have fewer addictive side effects.
Computational platforms already exist that model protein structure and how proteins interact with drugs, but they largely ignore structural changes and do little to predict how new drugs will behave. you can’t. One reason for this is the lack of data available to train algorithms.
Drawing on a wealth of data from Vanderbilt researchers Craig Lindsley, Heidi Hamm, Vsevolod V. Gurevich, Matthias Elgety of the University of Leipzig, and Wu Beili of the Shanghai Institute of Materia Medica, Professor Brown: We plan to synthesize, functionally verify, and structurally characterize the drug molecules and receptors designed by Dr. researchers. Following this component of the grant, Brown will feed the data back into the computational platform so that it can be used as the starting point for further rounds of optimization, his loop of iterative feedback of computational experiments.
“We see pediatric patients having surgery, taking opioids post-surgery, and having problems afterwards. It’s really sad,” Dr. Brown said. “The goal, therefore, is to provide pain relief without the risk of addiction. And for addicts, to provide new drugs to aid recovery.”
In addition to the Center for Protein Dynamics and Applied AI at VCAR, Brown’s research affiliations include the Center for Structural Biology and the Vanderbilt Chemical Biology Institute.