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Mit KI den Kreislauf der Opioidsucht durchbrechen

von NFI Redaktion

When thinking about the opioid epidemic, Assistant Professor of Chemistry Ben Brown views the problem at the molecular level. Legally prescribed pain relievers like Oxycodone can be 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 a five-year period to advance his work in this area. Brown, a member of 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 gain detailed insights into their interaction with key proteins. The remaining $875,000 of the grant will go to Vanderbilt to cover indirect and administrative costs related to Brown’s research.

Brown will focus his research on Mu-opioid receptors, signaling proteins in the central nervous system that bind to opioids. These receptors modulate pain, stress, mood, and other functions. Drugs targeting these receptors are among the most potent analgesics but also the most addictive.

The grant, an Avenir Award in Chemistry and Pharmacology of Substance Use Disorders, is given by NIDA to young researchers proposing highly innovative studies and representing the future of addiction science.

„The energy and enthusiasm Ben brings to his science and scientific collaboration are outstanding, and it is fitting that he is recognized as a young pioneer in his field. Ben is one of the intellectual contributors to the founding of the Center for Applied AI in Protein Dynamics. I anticipate Ben will make fundamental advances in several core aspects of computer-aided drug design.“

Hassane Mchaourab, Director of the Center for Applied AI 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 takes into account their dynamic physical movements. These movements, called conformational changes, can occur within milliseconds and make a significant difference in how a protein behaves and how a low-molecular weight drug binds or interacts with it.

By computationally modeling these movements, algorithms can more effectively predict how closely proteins and drugs interact and the implications of this interaction. This information is used to test billions of potential drugs – an unprecedented scale for highly dynamic proteins – or develop new drugs with properties that lead to less addictive side effects.

There are already computer platforms that model protein structure and their interactions with drugs. However, they largely neglect conformational changes and are unable to accurately 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 structurally characterize drug molecules and receptors designed by him. Following this component of the grant, Brown will feed the data back into the computational platform to be used as a starting point for further optimization rounds – a computational-experimental iterative feedback loop.

„One sees pediatric patients being operated on, taking opioids postoperatively, and then having an issue afterwards. That’s really sad,“ Brown said. „So the goal is to provide pain medication without the risk of addiction. And to provide new medications for addicts that help in recovery.“

In addition to the Center for Applied AI in Protein Dynamics and VCAR, Brown’s research facilities include the Center for Structural Biology and the Vanderbilt Institute of Chemical Biology.

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